{"entity": "researcher", "timestamp": "2026-08-10T20:25:32.117Z", "family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "affiliations": ["Department of Pathology and Molecular Medicine, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c"}}, "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": "f85fb3bc915e4840a3e68620ac3d82e6", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f85fb3bc915e4840a3e68620ac3d82e6.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f85fb3bc915e4840a3e68620ac3d82e6"}}, "title": "Interobserver reproducibility of cribriform cancer in prostate needle biopsies and validation of International Society of Urological Pathology criteria.", "authors": [{"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Iczkowski", "given": "Kenneth A", "initials": "KA"}, {"family": "van der Kwast", "given": "Theo", "initials": "T"}, {"family": "van Leenders", "given": "Geert J L H", "initials": "GJLH", "orcid": "0000-0003-2176-9102", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06fb563d1d3243ffb42d4dbfb495eccf.json"}}, {"family": "Leite", "given": "Katia R M", "initials": "KRM", "orcid": "0000-0002-2615-7730", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5186bb78617340128628091c878daeda.json"}}, {"family": "Pan", "given": "Chin-Chen", "initials": "C"}, {"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": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Olsson", "given": "Henrik", "initials": "H"}, {"family": "Valkonen", "given": "Masi", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K"}], "type": "journal article", "published": "2023-05-00", "journal": {"title": "Histopathology", "issn": "1365-2559", "issn-l": "0309-0167", "volume": "82", "issue": "6", "pages": "837-845"}, "abstract": "There is strong evidence that cribriform morphology indicates a worse prognosis of prostatic adenocarcinoma. Our aim was to investigate its interobserver reproducibility in prostate needle biopsies.\r\n\r\nA panel of nine prostate pathology experts from five continents independently reviewed 304 digitised biopsies for cribriform cancer according to recent International Society of Urological Pathology criteria. The biopsies were collected from a series of 702 biopsies that were reviewed by one of the panellists for enrichment of high-grade cancer and potentially cribriform structures. A 2/3 consensus diagnosis of cribriform and noncribriform cancer was reached in 90% (272/304) of the biopsies with a mean kappa value of 0.56 (95% confidence interval 0.52-0.61). The prevalence of consensus cribriform cancers was estimated to 4%, 12%, 21%, and 20% of Gleason scores 7 (3 + 4), 7 (4 + 3), 8, and 9-10, respectively. More than two cribriform structures per level or a largest cribriform mass with \u22659 lumina or a diameter of \u22650.5 mm predicted a consensus diagnosis of cribriform cancer in 88% (70/80), 84% (87/103), and 90% (56/62), respectively, and noncribriform cancer in 3% (2/80), 5% (5/103), and 2% (1/62), respectively (all P < 0.01).\r\n\r\nCribriform prostate cancer was seen in a minority of needle biopsies with high-grade cancer. Stringent diagnostic criteria enabled the identification of cribriform patterns and the generation of a large set of consensus cases for standardisation.", "doi": "10.1111/his.14867", "pmid": "36645163", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [], "notes": [], "created": "2024-11-05T16:10:21.528Z", "modified": "2024-11-29T10:41:30.167Z"}, {"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": "09a071792cf64b67ab7ab31033715e90", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/09a071792cf64b67ab7ab31033715e90.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/09a071792cf64b67ab7ab31033715e90"}}, "title": "Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies.", "authors": [{"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.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"}, {"family": "Olsson", "given": "Henrik", "initials": "H"}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}, {"family": "Lindskog", "given": "Cecilia", "initials": "C"}, {"family": "H\u00e4kkinen", "given": "Tomi", "initials": "T"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P"}], "type": "journal article", "published": "2021-06-00", "journal": {"title": "Virchows Arch.", "issn": "1432-2307", "issn-l": "0945-6317", "volume": "478", "issue": "6", "pages": "1109-1116"}, "abstract": "Numerous studies have shown a correlation between perineural invasion (PNI) in prostate biopsies and outcome. The reporting of PNI varies widely in the literature. While the interobserver variability of prostate cancer grading has been studied extensively, less is known regarding the reproducibility of PNI. A total of 212 biopsy cores from a population-based screening trial were included in this study (106 with and 106 without PNI according to the original pathology reports). The glass slides were scanned and circulated among four pathologists with a special interest in urological pathology for assessment of PNI. Discordant cases were stained by immunohistochemistry for S-100 protein. PNI was diagnosed by all four observers in 34.0% of cases, while 41.5% were considered to be negative for PNI. In 24.5% of cases, there was a disagreement between the observers. The kappa for interobserver variability was 0.67-0.75 (mean 0.73). The observations from one participant were compared with data from the original reports, and a kappa for intraobserver variability of 0.87 was achieved. Based on immunohistochemical findings among discordant cases, 88.6% had PNI while 11.4% did not. The most common diagnostic pitfall was the presence of bundles of stroma or smooth muscle. It was noted in a few cases that collagenous micronodules could be mistaken for a nerve. The distance between cancer and nerve was another cause of disagreement. Although the results suggest that the reproducibility of PNI may be greater than that of prostate cancer grading, there is still a need for improvement and standardization.", "doi": "10.1007/s00428-021-03039-z", "pmid": "33534005", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8203540"}, {"db": "pii", "key": "10.1007/s00428-021-03039-z"}], "notes": [], "created": "2024-11-05T16:08:21.035Z", "modified": "2024-11-29T10:45:08.430Z"}, {"entity": "publication", "iuid": "09ab7a2cbe9041f19abb08c0d6434642", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/09ab7a2cbe9041f19abb08c0d6434642.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/09ab7a2cbe9041f19abb08c0d6434642"}}, "title": "The utility of artificial intelligence in the assessment of prostate pathology.", "authors": [{"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K"}, {"family": "Olsson", "given": "Henrik", "initials": "H"}, {"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": "Eklund", "given": "Martin", "initials": "M"}], "type": "editorial", "published": "2020-05-00", "journal": {"title": "Histopathology", "issn": "1365-2559", "issn-l": "0309-0167", "volume": "76", "issue": "6", "pages": "790-792"}, "abstract": null, "doi": "10.1111/his.14060", "pmid": "32402150", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [], "notes": [], "created": "2024-11-05T16:10:26.497Z", "modified": "2024-11-29T10:45:28.264Z"}]}