{"entity": "researcher", "timestamp": "2026-08-10T19:51:27.609Z", "family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "affiliations": ["Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b"}}, "publications": [{"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": "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": "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": "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"}]}