{"entity": "researcher", "timestamp": "2026-09-12T07:52:00.396Z", "family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "affiliations": ["Institute of Biomedicine, University of Turku, Turku, Finland."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe"}}, "publications": [{"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": "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": "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"}]}