{"entity": "researcher", "timestamp": "2026-09-09T10:07:34.622Z", "family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "affiliations": ["Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland.", "Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, 33720, Finland."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72"}}, "publications": [{"entity": "publication", "iuid": "c4cda4b47cc54853a1b0f7048e77d45c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/c4cda4b47cc54853a1b0f7048e77d45c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/c4cda4b47cc54853a1b0f7048e77d45c"}}, "title": "Retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies on the ProMort cohort: study protocol.", "authors": [{"family": "Ji", "given": "Xiaoyi", "initials": "X", "orcid": "0009-0005-8431-7906", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e2e50c016e1040a5995a50a049280577.json"}}, {"family": "Zelic", "given": "Renata", "initials": "R"}, {"family": "Aspegren", "given": "Oskar", "initials": "O"}, {"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Fiorentino", "given": "Michelangelo", "initials": "M"}, {"family": "Giunchi", "given": "Francesca", "initials": "F"}, {"family": "Molinaro", "given": "Luca", "initials": "L"}, {"family": "Boman", "given": "Sol Erika", "initials": "SE"}, {"family": "Szolnoky", "given": "Kelvin", "initials": "K", "orcid": "0000-0002-0554-1872", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5059c9f881d4434f9b64f3bb381478fe.json"}}, {"family": "Liu", "given": "Luana Xuan", "initials": "LX"}, {"family": "Pettersson", "given": "Andreas", "initials": "A"}, {"family": "Vincent", "given": "Per Henrik", "initials": "PH"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Akre", "given": "Olof", "initials": "O"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}], "type": "journal article", "published": "2025-12-24", "journal": {"title": "BMJ Open", "issn": "2044-6055", "volume": "15", "issue": "12", "pages": "e111361", "issn-l": "2044-6055"}, "abstract": "Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass other critical diagnostic and prognostic markers, including cancer volume quantification and detection of cribriform morphology patterns and perineural invasion. Artificial intelligence (AI) applications in digital pathology have emerged as promising solutions for enhancing diagnostic consistency and accuracy, with recent research demonstrating that automated systems can match expert-level performance in prostate biopsy evaluation. Nevertheless, comprehensive validation studies have revealed concerning limitations in model generalisability when deployed across different clinical environments and patient populations. Recent systematic reviews revealed widespread risk-of-bias limitations and insufficient external validation in AI diagnostic studies, highlighting critical needs for accumulated evidence supporting generalisability before clinical implementation. Rigorous external validation with preregistered protocols using independent datasets from diverse clinical settings remains essential to establish the reliability and safety of AI-assisted prostate pathology systems.\n\nThis study protocol establishes a framework for the retrospective external validation of an AI system developed for prostate biopsy assessment, to be conducted on the case-control samples of the National Prostate Cancer Register of Sweden, ProMort study (1998-2015). The primary aim is to evaluate the AI model's diagnostic accuracy and Gleason grading performance using completely independent datasets separate from any model development or previously used validation cohorts. The diversity of the validation samples, spanning multiple geographic regions, temporal collection periods and reference standards, allows evaluation of model robustness across varied clinical contexts. Secondary aims encompass evaluating AI performance in cancer length estimation and detection of cribriform patterns and perineural invasion. This protocol delineates procedures for data collection, reference standard clarification and prespecified statistical analyses, ensuring comprehensive validation and reliable performance assessment. The study design conforms to established reporting guidelines Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Standards for Reporting Diagnostic Accuracy Studies using Artificial Intelligence (STARD-AI), and recognised best practices for AI validation in medical imaging.\n\nData collection and usage were approved by the Swedish Regional Ethics Review Board and the Swedish Ethical Review Authority (permits 2012/1586-31/1, 2016/613-31/2, 2019-01395, 2019-05220). The study adheres to the Declaration of Helsinki principles, and findings will be made available in open access peer-reviewed publications.", "doi": "10.1136/bmjopen-2025-111361", "pmid": "41448704", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12742080"}, {"db": "pii", "key": "bmjopen-2025-111361"}], "notes": [], "created": "2026-08-20T12:02:09.634Z", "modified": "2026-08-20T12:02:09.689Z"}, {"entity": "publication", "iuid": "323bbe2a3c6e444081e24c57fa16df77", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/323bbe2a3c6e444081e24c57fa16df77.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/323bbe2a3c6e444081e24c57fa16df77"}}, "title": "Artificial intelligence-assisted prostate cancer diagnosis for reduced use of immunohistochemistry.", "authors": [{"family": "Blilie", "given": "Anders", "initials": "A", "orcid": "0009-0002-9546-5443", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d778d06e206948e5ac99017ce1ae8434.json"}}, {"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Szolnoky", "given": "Kelvin", "initials": "K", "orcid": "0000-0002-0554-1872", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5059c9f881d4434f9b64f3bb381478fe.json"}}, {"family": "Boman", "given": "Sol Erika", "initials": "SE", "orcid": "0009-0001-5733-3178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8a605b0dadad4b3696396827ebac9348.json"}}, {"family": "Titus", "given": "Matteo", "initials": "M", "orcid": "0009-0008-7893-7385", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/049f236217f147f78635b9d3a361db98.json"}}, {"family": "Gonzalez", "given": "Geraldine Martinez", "initials": "GM"}, {"family": "Asenjo", "given": "Jos\u00e9", "initials": "J"}, {"family": "Gambacorta", "given": "Marcello", "initials": "M"}, {"family": "Libretti", "given": "Paolo", "initials": "P"}, {"family": "Gudlaugsson", "given": "Einar", "initials": "E"}, {"family": "Kjosavik", "given": "Svein R", "initials": "SR", "orcid": "0000-0002-7004-5533", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/97167c1ee44443108098eb823bd18fa7.json"}}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Janssen", "given": "Emiel A M", "initials": "EAM"}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}], "type": "journal article", "published": "2025-10-15", "journal": {"title": "Commun Med (Lond)", "issn": "2730-664X", "volume": "5", "issue": "1", "pages": "425", "issn-l": null}, "abstract": "Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it increases workload, costs, and leads to diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin and eosin (H&E) stained tissue sections.\n\nIn this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy. We retrospectively analyzed prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts consisted exclusively of diagnostically challenging cases where pathologists had required IHC to finalize the diagnosis.\n\nWe show that the AI model achieves high performance, with area under the curve values ranging from 0.951 to 0.993 for detecting cancer in routine H&E-stained slides. When applying sensitivity-prioritized diagnostic thresholds, the model reduces the need for IHC staining by 44.4%, 42.0%, and 20.7% across the three cohorts, without a single false negative prediction. Among slides with a benign ground truth label, IHC use is reduced by up to 80.6%.\n\nThis AI model shows promise for reducing unnecessary IHC use in difficult prostate biopsy cases while maintaining diagnostic safety. Its integration into clinical workflows could streamline decision-making in prostate pathology and alleviate resource burdens.", "doi": "10.1038/s43856-025-01185-y", "pmid": "41094148", "labels": {"DDLS Fellow": null, "Kimmo Kartasalo": null}, "xrefs": [{"db": "pmc", "key": "PMC12528698"}, {"db": "pii", "key": "10.1038/s43856-025-01185-y"}], "notes": [], "created": "2025-10-30T15:41:17.882Z", "modified": "2026-06-03T09:44:07.037Z"}, {"entity": "publication", "iuid": "af5b1bed406e47ad890769e1d8f42bbd", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/af5b1bed406e47ad890769e1d8f42bbd.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/af5b1bed406e47ad890769e1d8f42bbd"}}, "title": "Hierarchical Vision Transformers for prostate biopsy grading: Towards bridging the generalization gap.", "authors": [{"family": "Grisi", "given": "Cl\u00e9ment", "initials": "C"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "van der Laak", "given": "Jeroen", "initials": "J", "orcid": "0000-0001-7982-0754", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/188f689ad0304f28b42bc9d3dbf6a56e.json"}}, {"family": "Litjens", "given": "Geert", "initials": "G", "orcid": "0000-0003-1554-1291", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3792b285bcf04c11b268da1088160a9d.json"}}], "type": "journal article", "published": "2025-10-00", "journal": {"title": "Med Image Anal", "issn": "1361-8423", "volume": "105", "pages": "103663", "issn-l": null}, "abstract": "Practical deployment of Vision Transformers in computational pathology has largely been constrained by the sheer size of whole-slide images. Transformers faced a similar limitation when applied to long documents, and Hierarchical Transformers were introduced to circumvent it. This work explores the capabilities of Hierarchical Vision Transformers for prostate cancer grading in WSIs and presents a novel technique to combine attention scores smartly across hierarchical transformers. Our best-performing model matches state-of-the-art algorithms with a 0.916 quadratic kappa on the Prostate cANcer graDe Assessment (PANDA) test set. It exhibits superior generalization capacities when evaluated in more diverse clinical settings, achieving a quadratic kappa of 0.877, outperforming existing solutions. These results demonstrate our approach's robustness and practical applicability, paving the way for its broader adoption in computational pathology and possibly other medical imaging tasks. Our code is publicly available at https://github.com/computationalpathologygroup/hvit.", "doi": "10.1016/j.media.2025.103663", "pmid": "40644915", "labels": {"DDLS Fellow": null, "Kimmo Kartasalo": null}, "xrefs": [{"db": "pii", "key": "S1361-8415(25)00210-5"}], "notes": [], "created": "2025-10-30T15:42:14.631Z", "modified": "2025-11-04T09:38:52.902Z"}, {"entity": "publication", "iuid": "420a0bbc01614b8a8e996e0a27dbc995", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/420a0bbc01614b8a8e996e0a27dbc995.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/420a0bbc01614b8a8e996e0a27dbc995"}}, "title": "Retrospective Validation of an Artificial Intelligence System for Diagnostic Assessment of Prostate Biopsies on the ProMort Cohort: Study Protocol", "authors": [{"family": "Ji", "given": "Xiaoyi", "initials": "X", "orcid": "0009-0005-8431-7906", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e2e50c016e1040a5995a50a049280577.json"}}, {"family": "Zelic", "given": "Renata", "initials": "R", "orcid": "0000-0002-8842-5964", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/121ec02a5a924a899b913c600b3b3763.json"}}, {"family": "Aspegren", "given": "Oskar", "initials": "O", "orcid": "0000-0002-5309-7737", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d267fe4993d14025a303ba9a1f5863da.json"}}, {"family": "Mulliqi", "given": "Nita", "initials": "N", "orcid": "0009-0002-7448-4960", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/306c43e2841c4ad098db7a5ac481a1bf.json"}}, {"family": "Fiorentino", "given": "Michelangelo", "initials": "M", "orcid": "0000-0002-1749-150X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/adb2abeaee404c04b373ac96157340bf.json"}}, {"family": "Giunchi", "given": "Francesca", "initials": "F", "orcid": "0000-0001-5298-939X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3d3cc40c9d2e4fc9a796549fa6677e30.json"}}, {"family": "Molinaro", "given": "Luca", "initials": "L"}, {"family": "Boman", "given": "Sol Erika", "initials": "SE", "orcid": "0009-0001-5733-3178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8a605b0dadad4b3696396827ebac9348.json"}}, {"family": "Szolnoky", "given": "Kelvin", "initials": "K", "orcid": "0000-0002-0554-1872", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5059c9f881d4434f9b64f3bb381478fe.json"}}, {"family": "Liu", "given": "Luana Xuan", "initials": "LX"}, {"family": "Pettersson", "given": "Andreas", "initials": "A"}, {"family": "Vincent", "given": "Per Henrik", "initials": "PH", "orcid": "0000-0002-9895-5803", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d5490a9e6c334e48b301e64fdd747504.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Akre", "given": "Olof", "initials": "O", "orcid": "0000-0002-3750-3029", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3f25f3b240644cda828e4744b30020a9.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}], "type": "posted-content", "published": "2025-09-23", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2025.09.22.25336169", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T11:07:11.947Z", "modified": "2026-08-20T11:07:12.332Z"}, {"entity": "publication", "iuid": "f905ca88a80f42109bb997cd17743470", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f905ca88a80f42109bb997cd17743470.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f905ca88a80f42109bb997cd17743470"}}, "title": "Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol.", "authors": [{"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Blilie", "given": "Anders", "initials": "A", "orcid": "0009-0002-9546-5443", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d778d06e206948e5ac99017ce1ae8434.json"}}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Szolnoky", "given": "Kelvin", "initials": "K", "orcid": "0000-0002-0554-1872", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5059c9f881d4434f9b64f3bb381478fe.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Titus", "given": "Matteo", "initials": "M", "orcid": "0009-0008-7893-7385", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/049f236217f147f78635b9d3a361db98.json"}}, {"family": "Martinez Gonzalez", "given": "Geraldine", "initials": "G"}, {"family": "Boman", "given": "Sol Erika", "initials": "SE", "orcid": "0009-0001-5733-3178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8a605b0dadad4b3696396827ebac9348.json"}}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Gudlaugsson", "given": "Einar", "initials": "E"}, {"family": "Kjosavik", "given": "Svein Reidar", "initials": "SR", "orcid": "0000-0002-7004-5533", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/97167c1ee44443108098eb823bd18fa7.json"}}, {"family": "Asenjo", "given": "Jos\u00e9", "initials": "J"}, {"family": "Gambacorta", "given": "Marcello", "initials": "M"}, {"family": "Libretti", "given": "Paolo", "initials": "P"}, {"family": "Braun", "given": "Marcin", "initials": "M"}, {"family": "Kordek", "given": "Radzislaw", "initials": "R"}, {"family": "\u0141owicki", "given": "Roman", "initials": "R"}, {"family": "Hotakainen", "given": "Kristina", "initials": "K"}, {"family": "V\u00e4re", "given": "P\u00e4ivi", "initials": "P"}, {"family": "Pedersen", "given": "Bodil Ginnerup", "initials": "BG", "orcid": "0000-0003-2792-7343", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/926d2e6e88a049f8ba5e50dbdd4fe210.json"}}, {"family": "S\u00f8rensen", "given": "Karina Dalsgaard", "initials": "KD"}, {"family": "Ulh\u00f8i", "given": "Benedicte Parm", "initials": "BP"}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Tsuzuki", "given": "Toyonori", "initials": "T", "orcid": "0000-0002-4855-4366", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42c993c2c0ab4b50afa03134f70118d9.json"}}, {"family": "Janssen", "given": "Emilius Adrianus Maria", "initials": "EAM"}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}], "type": "journal article", "published": "2025-07-07", "journal": {"title": "BMJ Open", "issn": "2044-6055", "volume": "15", "issue": "7", "pages": "e097591", "issn-l": "2044-6055"}, "abstract": "Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI's capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy.\n\nThis study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices.\n\nData collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).", "doi": "10.1136/bmjopen-2024-097591", "pmid": "40623883", "labels": {"DDLS Fellow": null, "Kimmo Kartasalo": null}, "xrefs": [{"db": "pmc", "key": "PMC12258300"}, {"db": "pii", "key": "bmjopen-2024-097591"}], "notes": [], "created": "2025-10-30T15:40:42.132Z", "modified": "2025-11-04T09:40:10.515Z"}, {"entity": "publication", "iuid": "6defc26e37704bc18722bc1e8d65512b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6defc26e37704bc18722bc1e8d65512b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6defc26e37704bc18722bc1e8d65512b"}}, "title": "The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.", "authors": [{"family": "Weitz", "given": "Philippe", "initials": "P", "orcid": "0000-0002-1788-0716", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a70939bc05a04f87b9a61e7d98448e09.json"}}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5c33a5b8cef4cad8b9f57b4510fb0c2.json"}}, {"family": "Carr", "given": "Circe", "initials": "C"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Boissin", "given": "Constance", "initials": "C"}, {"family": "Koivukoski", "given": "Sonja", "initials": "S", "orcid": "0000-0002-4909-3522", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1889b0f9c9f943609f51e8d8d15041f1.json"}}, {"family": "Kuusela", "given": "Aino", "initials": "A"}, {"family": "Rasic", "given": "Dusan", "initials": "D", "orcid": "0000-0003-4610-5265", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42ddfdb3b820417197cedfd1b96ab172.json"}}, {"family": "Feng", "given": "Yanbo", "initials": "Y"}, {"family": "Pouplier", "given": "Sandra Sinius", "initials": "SS"}, {"family": "Sharma", "given": "Abhinav", "initials": "A"}, {"family": "Eriksson", "given": "Kajsa Ledesma", "initials": "KL"}, {"family": "Robertson", "given": "Stephanie", "initials": "S"}, {"family": "Marzahl", "given": "Christian", "initials": "C"}, {"family": "Gatenbee", "given": "Chandler D", "initials": "CD"}, {"family": "Anderson", "given": "Alexander R A", "initials": "ARA"}, {"family": "Wodzinski", "given": "Marek", "initials": "M"}, {"family": "Jurgas", "given": "Artur", "initials": "A"}, {"family": "Marini", "given": "Niccol\u00f2", "initials": "N"}, {"family": "Atzori", "given": "Manfredo", "initials": "M"}, {"family": "M\u00fcller", "given": "Henning", "initials": "H"}, {"family": "Budelmann", "given": "Daniel", "initials": "D"}, {"family": "Weiss", "given": "Nick", "initials": "N"}, {"family": "Heldmann", "given": "Stefan", "initials": "S"}, {"family": "Lotz", "given": "Johannes", "initials": "J"}, {"family": "Wolterink", "given": "Jelmer M", "initials": "JM"}, {"family": "De Santi", "given": "Bruno", "initials": "B"}, {"family": "Patil", "given": "Abhijeet", "initials": "A"}, {"family": "Sethi", "given": "Amit", "initials": "A"}, {"family": "Kondo", "given": "Satoshi", "initials": "S"}, {"family": "Kasai", "given": "Satoshi", "initials": "S"}, {"family": "Hirasawa", "given": "Kousuke", "initials": "K"}, {"family": "Farrokh", "given": "Mahtab", "initials": "M"}, {"family": "Kumar", "given": "Neeraj", "initials": "N"}, {"family": "Greiner", "given": "Russell", "initials": "R"}, {"family": "Latonen", "given": "Leena", "initials": "L", "orcid": "0000-0003-4502-2193", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f85efd5db6e74874acdb8d14237ae732.json"}}, {"family": "Laenkholm", "given": "Anne-Vibeke", "initials": "A"}, {"family": "Hartman", "given": "Johan", "initials": "J", "orcid": "0000-0002-6500-8527", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d62d622200d443b7b5be34ff3c0945be.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}], "type": "journal article", "published": "2024-10-00", "journal": {"title": "Med Image Anal", "issn": "1361-8423", "issn-l": null, "volume": "97", "issue": null, "pages": "103257"}, "abstract": "The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.", "doi": "10.1016/j.media.2024.103257", "pmid": "38981282", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pii", "key": "S1361-8415(24)00182-8"}], "notes": [], "created": "2024-11-05T16:12:17.237Z", "modified": "2025-04-07T06:58:23.491Z"}, {"entity": "publication", "iuid": "36212ac7310c4b549ce72c41f2cced1f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/36212ac7310c4b549ce72c41f2cced1f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/36212ac7310c4b549ce72c41f2cced1f"}}, "title": "A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.", "authors": [{"family": "Weitz", "given": "Philippe", "initials": "P", "orcid": "0000-0002-1788-0716", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a70939bc05a04f87b9a61e7d98448e09.json"}}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5c33a5b8cef4cad8b9f57b4510fb0c2.json"}}, {"family": "Carr", "given": "Circe", "initials": "C"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Boissin", "given": "Constance", "initials": "C"}, {"family": "Koivukoski", "given": "Sonja", "initials": "S", "orcid": "0000-0002-4909-3522", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1889b0f9c9f943609f51e8d8d15041f1.json"}}, {"family": "Kuusela", "given": "Aino", "initials": "A"}, {"family": "Rasic", "given": "Dusan", "initials": "D", "orcid": "0000-0003-4610-5265", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42ddfdb3b820417197cedfd1b96ab172.json"}}, {"family": "Feng", "given": "Yanbo", "initials": "Y"}, {"family": "Sinius Pouplier", "given": "Sandra", "initials": "S", "orcid": "0000-0002-2625-7440", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/190eef15655e4ea6b8eaa15ccddfb7a0.json"}}, {"family": "Sharma", "given": "Abhinav", "initials": "A"}, {"family": "Ledesma Eriksson", "given": "Kajsa", "initials": "K"}, {"family": "Latonen", "given": "Leena", "initials": "L", "orcid": "0000-0003-4502-2193", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f85efd5db6e74874acdb8d14237ae732.json"}}, {"family": "Laenkholm", "given": "Anne-Vibeke", "initials": "A"}, {"family": "Hartman", "given": "Johan", "initials": "J", "orcid": "0000-0002-6500-8527", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d62d622200d443b7b5be34ff3c0945be.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}], "type": "dataset", "published": "2023-08-24", "journal": {"title": "Sci Data", "issn": "2052-4463", "issn-l": "2052-4463", "volume": "10", "issue": "1", "pages": "562"}, "abstract": "The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.", "doi": "10.1038/s41597-023-02422-6", "pmid": "37620357", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC10449765"}, {"db": "pii", "key": "10.1038/s41597-023-02422-6"}], "notes": [], "created": "2024-11-05T16:10:24.038Z", "modified": "2024-11-29T09:28:40.734Z"}, {"entity": "publication", "iuid": "34dd4118753048eab5325f80ccac79e7", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/34dd4118753048eab5325f80ccac79e7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/34dd4118753048eab5325f80ccac79e7"}}, "title": "Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.", "authors": [{"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Capuccini", "given": "Marco", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Lindskog", "given": "Cecilia", "initials": "C", "orcid": "0000-0001-5611-1015", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/21610a810a87459c962d6da3f2ad38ad.json"}}, {"family": "Janssen", "given": "Emiel A M", "initials": "EAM"}, {"family": "Blilie", "given": "Anders", "initials": "A"}, {"family": "ISUP Prostate Imagebase Expert Panel", "given": "", "initials": ""}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}], "type": "journal article", "published": "2022-12-15", "journal": {"title": "Nat Commun", "issn": "2041-1723", "issn-l": "2041-1723", "volume": "13", "issue": "1", "pages": "7761"}, "abstract": "Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.", "doi": "10.1038/s41467-022-34945-8", "pmid": "36522311", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9755280"}, {"db": "pii", "key": "10.1038/s41467-022-34945-8"}], "notes": [], "created": "2024-11-05T16:10:20.350Z", "modified": "2024-11-29T09:28:59.239Z"}, {"entity": "publication", "iuid": "a9d473ca72164e18a3db61d78fef3d7a", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a9d473ca72164e18a3db61d78fef3d7a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a9d473ca72164e18a3db61d78fef3d7a"}}, "title": "Detection of perineural invasion in prostate needle biopsies with deep neural networks.", "authors": [{"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Tsuzuki", "given": "Toyonori", "initials": "T", "orcid": "0000-0002-4855-4366", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42c993c2c0ab4b50afa03134f70118d9.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}], "type": "journal article", "published": "2022-07-00", "journal": {"title": "Virchows Arch.", "issn": "1432-2307", "issn-l": "0945-6317", "volume": "481", "issue": "1", "pages": "73-82"}, "abstract": "The presence of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI are, however, labor intensive. To aid pathologists in this task, we developed an artificial intelligence (AI) algorithm based on deep neural networks. We collected, digitized, and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7406 men who underwent biopsy in a screening trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n = 8318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build the AI algorithm, while 20% were used to evaluate its performance. For detecting PNI in prostate biopsy cores, the AI had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1652 PNI negative cores in the independent test set. For a pre-specified operating point, this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. The concordance of the AI with pathologists, measured by mean pairwise Cohen's kappa (0.74), was comparable to inter-pathologist concordance (0.68 to 0.75). The proposed algorithm detects PNI in prostate biopsies with acceptable performance. This could aid pathologists by reducing the number of biopsies that need to be assessed for PNI and by highlighting regions of diagnostic interest.", "doi": "10.1007/s00428-022-03326-3", "pmid": "35449363", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9226086"}, {"db": "pii", "key": "10.1007/s00428-022-03326-3"}], "notes": [], "created": "2024-11-05T16:09:36.259Z", "modified": "2024-11-29T09:29:14.824Z"}, {"entity": "publication", "iuid": "09e38ac904ad4fb0858f5de82a16deb0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/09e38ac904ad4fb0858f5de82a16deb0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/09e38ac904ad4fb0858f5de82a16deb0"}}, "title": "Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.", "authors": [{"family": "Weitz", "given": "Philippe", "initials": "P", "orcid": "0000-0002-1788-0716", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a70939bc05a04f87b9a61e7d98448e09.json"}}, {"family": "Wang", "given": "Yinxi", "initials": "Y"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Egevad", "given": "Lars", "initials": "L"}, {"family": "Lindberg", "given": "Johan", "initials": "J"}, {"family": "Gr\u00f6nberg", "given": "Henrik", "initials": "H", "orcid": "0000-0002-1073-2753", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33927307bf964b2fa09e09c0c867b542.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}], "type": "journal article", "published": "2022-06-27", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "issn-l": "1367-4803", "volume": "38", "issue": "13", "pages": "3462-3469"}, "abstract": "Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression.\r\n\r\nWe conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value <1\u00d710-4) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.\r\n\r\nA self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097.\r\n\r\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/btac343", "pmid": "35595235", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9237721"}, {"db": "pii", "key": "6589889"}], "notes": [], "created": "2024-11-05T16:09:35.045Z", "modified": "2024-11-29T09:29:36.368Z"}, {"entity": "publication", "iuid": "90b36d42fc5b4fb98ab07730320e52f5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/90b36d42fc5b4fb98ab07730320e52f5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/90b36d42fc5b4fb98ab07730320e52f5"}}, "title": "Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.", "authors": [{"family": "Bulten", "given": "Wouter", "initials": "W", "orcid": "0000-0002-6129-5039", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f592c80c84c34a70971251c380a8578b.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Chen", "given": "Po-Hsuan Cameron", "initials": "PC", "orcid": "0000-0002-0083-4991", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/dd162c57871947aea109e009fc1d2daf.json"}}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}, {"family": "Pinckaers", "given": "Hans", "initials": "H"}, {"family": "Nagpal", "given": "Kunal", "initials": "K"}, {"family": "Cai", "given": "Yuannan", "initials": "Y"}, {"family": "Steiner", "given": "David F", "initials": "DF", "orcid": "0000-0003-1297-0023", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a209b0168a394a09bd0cfe9cd1e1934b.json"}}, {"family": "van Boven", "given": "Hester", "initials": "H"}, {"family": "Vink", "given": "Robert", "initials": "R"}, {"family": "Hulsbergen-van de Kaa", "given": "Christina", "initials": "C"}, {"family": "van der Laak", "given": "Jeroen", "initials": "J", "orcid": "0000-0001-7982-0754", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/188f689ad0304f28b42bc9d3dbf6a56e.json"}}, {"family": "Amin", "given": "Mahul B", "initials": "MB", "orcid": "0000-0001-5943-3634", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d7974058596e4429b0035f1eff340a4c.json"}}, {"family": "Evans", "given": "Andrew J", "initials": "AJ"}, {"family": "van der Kwast", "given": "Theodorus", "initials": "T", "orcid": "0000-0001-8640-5786", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/176f93a02e394a659619ae2ff57398e4.json"}}, {"family": "Allan", "given": "Robert", "initials": "R"}, {"family": "Humphrey", "given": "Peter A", "initials": "PA"}, {"family": "Gr\u00f6nberg", "given": "Henrik", "initials": "H", "orcid": "0000-0002-1073-2753", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33927307bf964b2fa09e09c0c867b542.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Delahunt", "given": "Brett", "initials": "B"}, {"family": "Tsuzuki", "given": "Toyonori", "initials": "T", "orcid": "0000-0002-4855-4366", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42c993c2c0ab4b50afa03134f70118d9.json"}}, {"family": "H\u00e4kkinen", "given": "Tomi", "initials": "T"}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Demkin", "given": "Maggie", "initials": "M"}, {"family": "Dane", "given": "Sohier", "initials": "S"}, {"family": "Tan", "given": "Fraser", "initials": "F"}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Corrado", "given": "Greg S", "initials": "GS"}, {"family": "Peng", "given": "Lily", "initials": "L"}, {"family": "Mermel", "given": "Craig H", "initials": "CH", "orcid": "0000-0002-0816-3395", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ff69bed2b4394cd8972c937ebb642c55.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Litjens", "given": "Geert", "initials": "G", "orcid": "0000-0003-1554-1291", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3792b285bcf04c11b268da1088160a9d.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "PANDA challenge consortium", "given": "", "initials": ""}], "type": "journal article", "published": "2022-01-00", "journal": {"title": "Nat. Med.", "issn": "1546-170X", "issn-l": "1078-8956", "volume": "28", "issue": "1", "pages": "154-163"}, "abstract": "Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted \u03ba, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.", "doi": "10.1038/s41591-021-01620-2", "pmid": "35027755", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8799467"}, {"db": "pii", "key": "10.1038/s41591-021-01620-2"}], "notes": [], "created": "2024-11-05T16:09:31.876Z", "modified": "2024-11-29T09:30:09.852Z"}, {"entity": "publication", "iuid": "9900bd886963425bb691338c471bedf9", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9900bd886963425bb691338c471bedf9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9900bd886963425bb691338c471bedf9"}}, "title": "OpenPhi: an interface to access Philips iSyntax whole slide images for computational pathology.", "authors": [{"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}], "type": "journal article", "published": "2021-11-05", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "issn-l": "1367-4803", "volume": "37", "issue": "21", "pages": "3995-3997"}, "abstract": "Digital pathology enables applying computational methods, such as deep learning, in pathology for improved diagnostics and prognostics, but lack of interoperability between whole slide image formats of different scanner vendors is a challenge for algorithm developers. We present OpenPhi-Open PatHology Interface, an Application Programming Interface for seamless access to the iSyntax format used by the Philips Ultra Fast Scanner, the first digital pathology scanner approved by the United States Food and Drug Administration. OpenPhi is extensible and easily interfaced with existing vendor-neutral applications.\r\n\r\nOpenPhi is implemented in Python and is available as open-source under the MIT license at: https://gitlab.com/BioimageInformaticsGroup/openphi. The Philips Software Development Kit is required and available at: https://www.openpathology.philips.com. OpenPhi version 1.1.1 is additionally provided as Supplementary Data.\r\n\r\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/btab578", "pmid": "34358287", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8570784"}, {"db": "pii", "key": "6343446"}], "notes": [], "created": "2024-11-05T16:08:25.508Z", "modified": "2024-11-29T09:30:27.734Z"}, {"entity": "publication", "iuid": "15b43437cf7f4c9088cb8c614a832f0a", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/15b43437cf7f4c9088cb8c614a832f0a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/15b43437cf7f4c9088cb8c614a832f0a"}}, "title": "Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer.", "authors": [{"family": "Wang", "given": "Yinxi", "initials": "Y", "orcid": "0000-0002-1651-7763", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cb79b8566b0e4eefba67e9c8d8cc3808.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Weitz", "given": "Philippe", "initials": "P", "orcid": "0000-0002-1788-0716", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a70939bc05a04f87b9a61e7d98448e09.json"}}, {"family": "\u00c1cs", "given": "Bal\u00e1zs", "initials": "B", "orcid": "0000-0002-0972-4633", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9d642b1db48643f497be9a35a71dadde.json"}}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Larsson", "given": "Christer", "initials": "C"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Hartman", "given": "Johan", "initials": "J", "orcid": "0000-0002-6500-8527", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d62d622200d443b7b5be34ff3c0945be.json"}}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}], "type": "journal article", "published": "2021-10-01", "journal": {"title": "Cancer Res.", "issn": "1538-7445", "issn-l": "0008-5472", "volume": "81", "issue": "19", "pages": "5115-5126"}, "abstract": "Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.", "doi": "10.1158/0008-5472.CAN-21-0482", "pmid": "34341074", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9397635"}, {"db": "pii", "key": "0008-5472.CAN-21-0482"}], "notes": [], "created": "2024-11-05T16:08:24.047Z", "modified": "2024-11-29T09:30:42.787Z"}, {"entity": "publication", "iuid": "b65e6e54c6054588b6912118bfd56f84", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b65e6e54c6054588b6912118bfd56f84.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b65e6e54c6054588b6912118bfd56f84"}}, "title": "Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer.", "authors": [{"family": "Chelebian", "given": "Eduard", "initials": "E", "orcid": "0000-0001-6852-6605", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a91f8ed142fa424c9a81328bf2595fbe.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b65fab9c103c4551b457a801988ce728.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Marklund", "given": "Maja", "initials": "M", "orcid": "0000-0003-2627-2437", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/037e17f3f330479fa3bd48fcd0c55684.json"}}, {"family": "Tanoglidi", "given": "Anna", "initials": "A", "orcid": "0000-0002-1217-2219", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/71297f7922214a47bad7d21f2dfb1d51.json"}}, {"family": "Mirtti", "given": "Tuomas", "initials": "T", "orcid": "0000-0003-0455-9891", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c1963bc5086f4b45b9a48cb60115d578.json"}}, {"family": "Colling", "given": "Richard", "initials": "R", "orcid": "0000-0001-6344-9081", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/368d85f93e2346e5be917031d742ec51.json"}}, {"family": "Erickson", "given": "Andrew", "initials": "A", "orcid": "0000-0002-4850-4086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e1725ce48b1945af8a7df8d084403a37.json"}}, {"family": "Lamb", "given": "Alastair D", "initials": "AD", "orcid": "0000-0002-2968-7155", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f447a100ea1f440f820859d85d66770d.json"}}, {"family": "Lundeberg", "given": "Joakim", "initials": "J", "orcid": "0000-0003-4313-1601", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d9fa47767cd14ef2b9528c8b998cf095.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}], "type": "journal article", "published": "2021-09-28", "journal": {"title": "Cancers (Basel)", "issn": "2072-6694", "issn-l": "2072-6694", "volume": "13", "issue": "19", "pages": null}, "abstract": "Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.", "doi": "10.3390/cancers13194837", "pmid": "34638322", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8507756"}, {"db": "pii", "key": "cancers13194837"}], "notes": [], "created": "2024-11-05T16:08:22.249Z", "modified": "2024-11-29T09:30:51.851Z"}, {"entity": "publication", "iuid": "f89ec060f7f34be7ab0850d93dd84380", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f89ec060f7f34be7ab0850d93dd84380.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f89ec060f7f34be7ab0850d93dd84380"}}, "title": "The importance of study design in the application of artificial intelligence methods in medicine.", "authors": [{"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}], "type": "journal article", "published": "2019-10-18", "journal": {"title": "NPJ Digit Med", "issn": "2398-6352", "issn-l": null, "volume": "2", "issue": null, "pages": "101"}, "abstract": null, "doi": "10.1038/s41746-019-0174-1", "pmid": "31646183", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6802122"}, {"db": "pii", "key": "174"}], "notes": [], "created": "2024-11-05T16:10:25.356Z", "modified": "2024-11-29T09:31:22.619Z"}, {"entity": "publication", "iuid": "3fab25f2fb434804aa7fd393e0d5e104", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/3fab25f2fb434804aa7fd393e0d5e104.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/3fab25f2fb434804aa7fd393e0d5e104"}}, "title": "Deep Learning in Image Cytometry: A Review.", "authors": [{"family": "Gupta", "given": "Anindya", "initials": "A", "orcid": "0000-0003-3557-4947", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/57cb44fb9e0a42ba8f91e813e2e45c76.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ", "orcid": "0000-0003-4046-9017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ef739cc45eff40b98c80ecc77f3afb73.json"}}, {"family": "Wieslander", "given": "H\u00e5kan", "initials": "H", "orcid": "0000-0002-6289-7285", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/72067b4a36de4d68a2200110df7cef4b.json"}}, {"family": "Pielawski", "given": "Nicolas", "initials": "N", "orcid": "0000-0001-8182-0091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a001a04d47014ba4af7cccb48b669783.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d2d38e59d5c840a0a53a4b90690fdc7a.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5c33a5b8cef4cad8b9f57b4510fb0c2.json"}}, {"family": "Suveer", "given": "Amit", "initials": "A", "orcid": "0000-0002-7779-094X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/caf81c0056e8483c9f072e06453ab67d.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bda9a501396248b5a7daa41db01518dc.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "I", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8dbd58070a9b4985b8bff09c1f965413.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}], "type": "journal article", "published": "2019-04-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "volume": "95", "issue": "4", "pages": "366-380"}, "abstract": "Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. \u00a9 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23701", "pmid": "30565841", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6590257"}], "notes": [], "created": "2024-11-05T16:04:36.061Z", "modified": "2024-11-29T09:31:39.875Z"}, {"entity": "publication", "iuid": "ec5af15c2fcd4fbca0292ec3b72dc5ee", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ec5af15c2fcd4fbca0292ec3b72dc5ee.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ec5af15c2fcd4fbca0292ec3b72dc5ee"}}, "title": "Focal Adhesion Kinase and ROCK Signaling Are Switch-Like Regulators of Human Adipose Stem Cell Differentiation towards Osteogenic and Adipogenic Lineages.", "authors": [{"family": "Hyv\u00e4ri", "given": "Laura", "initials": "L", "orcid": "0000-0002-9893-0323", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/acdbb8ff6f6b4bfdac09534392ae9631.json"}}, {"family": "Ojansivu", "given": "Miina", "initials": "M", "orcid": "0000-0002-5493-3530", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f752fc30ac694e9f9495aa2d8e6df60e.json"}}, {"family": "Juntunen", "given": "Miia", "initials": "M", "orcid": "0000-0001-5282-4376", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0567dd8adccc4f2a86119b3220719106.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Miettinen", "given": "Susanna", "initials": "S", "orcid": "0000-0002-0647-9556", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5ce415c4e4d7419ab3704b4096333c59.json"}}, {"family": "Vanhatupa", "given": "Sari", "initials": "S", "orcid": "0000-0002-2444-9713", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/adadbd28e84a4764be421fe20cf3648b.json"}}], "type": "journal article", "published": "2018-09-12", "journal": {"title": "Stem Cells Int", "issn": "1687-966X", "issn-l": null, "volume": "2018", "issue": null, "pages": "2190657"}, "abstract": "Adipose tissue is an attractive stem cell source for soft and bone tissue engineering applications and stem cell therapies. The adipose-derived stromal/stem cells (ASCs) have a multilineage differentiation capacity that is regulated through extracellular signals. The cellular events related to cell adhesion and cytoskeleton have been suggested as central regulators of differentiation fate decision. However, the detailed knowledge of these molecular mechanisms in human ASCs remains limited. This study examined the significance of focal adhesion kinase (FAK), Rho-Rho-associated protein kinase (Rho-ROCK), and their downstream target extracellular signal-regulated kinase 1/2 (ERK1/2) on hASCs differentiation towards osteoblasts and adipocytes. Analyses of osteogenic markers RUNX2A, alkaline phosphatase, and matrix mineralization revealed an essential role of active FAK, ROCK, and ERK1/2 signaling for the osteogenesis of hASCs. Inhibition of these kinases with specific small molecule inhibitors diminished osteogenesis, while inhibition of FAK and ROCK activity led to elevation of adipogenic marker genes AP2 and LEP and lipid accumulation implicating adipogenesis. This denotes to a switch-like function of FAK and ROCK signaling in the osteogenic and adipogenic fates of hASCs. On the contrary, inhibition of ERK1/2 kinase activity deceased adipogenic differentiation, indicating that activation of ERK signaling is required for both adipogenic and osteogenic potential. Our findings highlight the reciprocal role of cell adhesion mechanisms and actin dynamics in regulation of hASC lineage commitment. This study enhances the knowledge of molecular mechanisms dictating hASC differentiation and thus opens possibilities for more efficient control of hASC differentiation.", "doi": "10.1155/2018/2190657", "pmid": "30275837", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6157106"}], "notes": [], "created": "2024-11-05T16:04:38.060Z", "modified": "2024-11-29T09:31:50.695Z"}]}