Journal of Pathology Informatics最新文献

{"title":"Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method","authors":"E. Malamutmann ,&nbsp;M. Platte ,&nbsp;S. Theurer ,&nbsp;M. Jalc ,&nbsp;J. Rashidi-Alavijeh ,&nbsp;K. Willuweit ,&nbsp;J.-W. Treckmann ,&nbsp;A. Oezcelik ,&nbsp;F. Nensa ,&nbsp;J. Haubold ,&nbsp;U. Neumann ,&nbsp;D.P. Hoyer","doi":"10.1016/j.jpi.2026.100656","DOIUrl":"10.1016/j.jpi.2026.100656","url":null,"abstract":"<div><h3>Background</h3><div>Macrovesicular steatosis (MaS) affects liver transplant outcomes. Traditional visual biopsy assessment is subjective and shows inter-observer variability (weighted κ = 0.595 in our cohort), which complicates allocation and prognostication.</div></div><div><h3>Methods</h3><div>We developed a semi-automated image analysis method using HALCON Progress (evaluation license, 2019–2023) to quantify MaS in H&amp;E whole-slide images (WSIs). The pipeline processes native SVS files at full resolution (average runtime 1.26 ± 0.53 min/WSI) without tiling, downsampling, or format conversion. Four shape features (area, roundness, circularity, and compactness) guide classification. Results were compared with three specialized pathologists using Pearson and Spearman correlations.</div></div><div><h3>Results</h3><div>Across 129 WSIs (≈52,000 lipid droplets), artificial intelligence–pathologist correlations were statistically significant (Pearson <em>r</em> = 0.526–0.642; Spearman ρ = 0.498–0.615; all <em>p</em> &lt; 0.001; <em>n</em> = 48–95 per pathologist). Correlation with the mean pathologist assessment reached R² = 0.64 (<em>r</em> = 0.80, ρ = 0.782), within the inter-pathologist range (R² = 0.34–0.62; weighted κ = 0.595). Using four shape features, the pipeline separates vacuoles from vessels and processing artifacts during interactive review. Processing native SVS at full resolution avoids extra compute while preserving precision.</div></div><div><h3>Conclusions</h3><div>The method provides a rapid, objective readout of MaS and performs on par with inter-pathologist agreement (R² = 0.64 vs. weighted κ = 0.595). It is best used as decision-support that supplies percentages and overlays for expert review. Main limitations are underestimation in severe, confluent steatosis (&gt;30%), limited coverage of microvesicular fat, and single-center validation. Multi-center evidence across scanners and patient groups is still needed before routine use.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100656"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding diagnostic access in the Amazon through digital pathology: A real-world experience","authors":"Denilson José Silva Feitosa-Junior,&nbsp;Carlos Augusto Moreira Silva","doi":"10.1016/j.jpi.2026.100617","DOIUrl":"10.1016/j.jpi.2026.100617","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100617"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive cell-level Ki-67 quantification benchmark dataset for breast cancers","authors":"Gyu Yeong Kim ,&nbsp;Jihee Sohn ,&nbsp;Na Yoon Ji ,&nbsp;Taehwan Seok ,&nbsp;Seungkyun Lee ,&nbsp;Sungyong Jang ,&nbsp;Hyungbin Kim ,&nbsp;Suyeon Kim ,&nbsp;Dajeong Park ,&nbsp;Yeongjun Song ,&nbsp;Chungyeul Kim","doi":"10.1016/j.jpi.2026.100600","DOIUrl":"10.1016/j.jpi.2026.100600","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100600"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148181757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federal synergy in digital pathology: Advancing AI and readiness through VA–DoW collaboration","authors":"Joel Moncur ,&nbsp;Jessica Wang-Rodriguez ,&nbsp;Aaron Auerbach ,&nbsp;Mark Lyman","doi":"10.1016/j.jpi.2026.100557","DOIUrl":"10.1016/j.jpi.2026.100557","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimbursement task force update - Navigating the US reimbursement landscape","authors":"Michael Rivers ,&nbsp;Jennifer Archer ,&nbsp;Deborah Godes ,&nbsp;Paul Gerrard","doi":"10.1016/j.jpi.2026.100554","DOIUrl":"10.1016/j.jpi.2026.100554","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100554"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathology in the digital age: Empowering patient engagement","authors":"John Groth ,&nbsp;Jennifer Picarsic ,&nbsp;Richard Levenson ,&nbsp;Michele Mitchell ,&nbsp;Bethany Williams ,&nbsp;Linda Fildey","doi":"10.1016/j.jpi.2026.100552","DOIUrl":"10.1016/j.jpi.2026.100552","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100552"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The augmented pathologist: Agentic workflows in practice","authors":"Matthew Cecchini","doi":"10.1016/j.jpi.2026.100558","DOIUrl":"10.1016/j.jpi.2026.100558","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100558"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How digital pathology facilitates remote pathologist-patient consultations","authors":"Sylvia Asa","doi":"10.1016/j.jpi.2026.100575","DOIUrl":"10.1016/j.jpi.2026.100575","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100575"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing tissue detection accuracy in digital pathology through uncertainty-aware AI","authors":"Asim Waqas ,&nbsp;Ehsan Ullah ,&nbsp;Poombal ,&nbsp;Muhammad Ali Afridi ,&nbsp;Ashwin Mukund ,&nbsp;Marilyn Bui ,&nbsp;Ghulam Rasool","doi":"10.1016/j.jpi.2026.100615","DOIUrl":"10.1016/j.jpi.2026.100615","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100615"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven virtual staining: Generating CK5 IHC from H&E WSIs in vulvar carcinoma using deep learning","authors":"Nickolas Littlefield,&nbsp;Ahmad P. Tafti,&nbsp;T. Rinda Soong,&nbsp;Qiangqiang Gu","doi":"10.1016/j.jpi.2026.100584","DOIUrl":"10.1016/j.jpi.2026.100584","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100584"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}