Journal of Pathology Informatics最新文献

{"title":"AI in melanoma pathology: From tumor risk stratification to lymph node metastasis detection","authors":"Nada Shaker , Nuha Shaker","doi":"10.1016/j.jpi.2026.100606","DOIUrl":"10.1016/j.jpi.2026.100606","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100606"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184264","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":"Rethinking image management: A PACS-based workflow as the future of digital pathology","authors":"Jacob Christofi , Carson Clark , Swati Satturwar , Sansar Tiwari , Zaibo Li , Anil Parwani , Giovanni Lujan","doi":"10.1016/j.jpi.2026.100611","DOIUrl":"10.1016/j.jpi.2026.100611","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100611"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178240","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":"Electronic medical record-based checklist & workflow redesign to optimize transfusion stewardship","authors":"Chandini Madeswaran, Yomna Amer, Gunja Piya, Sirisha Kundrapu","doi":"10.1016/j.jpi.2026.100580","DOIUrl":"10.1016/j.jpi.2026.100580","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100580"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148181104","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":"Erratum regarding missing Declaration of Competing Interest statements in previously published articles","authors":"","doi":"10.1016/j.jpi.2026.100668","DOIUrl":"10.1016/j.jpi.2026.100668","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100668"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148001606","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":"Computational pathology with dynamic convolutional and adaptive kernels","authors":"Taymaz Akan ,&nbsp;Richa Aishwarya ,&nbsp;Md. Shenuarin Bhuiyan ,&nbsp;Steven A. Conrad ,&nbsp;John A. Vanchiere ,&nbsp;Mohammad Alfrad Nobel Bhuiyan","doi":"10.1016/j.jpi.2026.100662","DOIUrl":"10.1016/j.jpi.2026.100662","url":null,"abstract":"<div><div>Data processing and learning have become essential to the advancement of medicine, with pathology and lab medicine being no exception. Integrating scientific research with clinical informatics into clinical practice facilitates novel methodologies for patient care. Computational pathology is a burgeoning subspecialty in pathology that promises a better-integrated solution to histopathological images and clinical informatics. Deep-learning methods in computational pathology have demonstrated considerable advances in automated histopathological image analysis. However, convolutional neural networks (CNNs) face fundamental limitations when dealing with the significant morphological heterogeneity present in disease tissues. Conventional CNNs use fixed convolutional kernels, which restrict their effectiveness in adaptively extracting features from histopathological images that exhibit diverse pathological patterns, staining intensities, and tissue architecture. To address this substantial limitation, we present an optimized variant of Omni-Dimensional Dynamic Convolution (ODConv) networks for distinguishing diseased tissue from healthy tissue. Compared with prior dynamic convolution methods that attend to a single kernel dimension, ODConv applies multi-dimensional attention across spatial positions, input channels, output channels, and kernel candidates, enabling more flexible and adaptive feature extraction. We evaluated our approach on wheat-germ agglutinin-stained and hematoxylin and eosin-stained skeletal muscle images from multiple disease models, including G93A*SOD1 transgenic mice (amyotrophic lateral sclerosis) and Akita mice (Type I diabetes). ODConv, trained entirely from scratch without ImageNet pretraining, achieved competitive classification performance relative to seven fine-tuned pretrained architectures across both staining modalities, demonstrating the effectiveness of omni-dimensional dynamic kernels in learning discriminative morphological representations directly from domain data. The study reports strong statistical agreement metrics, proving effective class balance handling and stable decision boundaries. These findings confirm ODConv as a strong computational pathology framework that advances automated diagnosis of neurodegenerative and metabolic skeletal muscle disorders.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100662"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858391","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":"Redefining thyroid cytology with AIxTHY: Improved accuracy and efficiency in FNAC interpretation","authors":"Swati Satturwar ,&nbsp;Zaibo Li ,&nbsp;Chi-Shun Yang ,&nbsp;Shu-Jiuan Lin ,&nbsp;I-An Ke ,&nbsp;Cheng-Hung Yeh ,&nbsp;Shih-Wen Hsu ,&nbsp;Yi-Siou Liu ,&nbsp;Guowei Shao ,&nbsp;Wei-Lei Yang ,&nbsp;Ming-Yu Lin ,&nbsp;Tien-Jen Liu ,&nbsp;Barbara Crothers ,&nbsp;Chih-Jung Chen","doi":"10.1016/j.jpi.2026.100564","DOIUrl":"10.1016/j.jpi.2026.100564","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100564"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177706","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":"Toward transparent AI in computational pathology: Concept learning for clinical AI","authors":"Gregory Verghese ,&nbsp;Enrico Parisini ,&nbsp;Anthony Baptista ,&nbsp;Anita Grigoriadis ,&nbsp;Tapabrata Chakraborty ,&nbsp;Christopher Banerji","doi":"10.1016/j.jpi.2026.100574","DOIUrl":"10.1016/j.jpi.2026.100574","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100574"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177988","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":"Pathological features associated with false-negative multiparametric MRI in prostate biopsies","authors":"Ramin Nateghi ,&nbsp;Marina Schnauss ,&nbsp;Clayton Neill ,&nbsp;Sai Kaushik Shankar Ramesh Kumar ,&nbsp;Adam Murphy ,&nbsp;Ximing Yang ,&nbsp;Lee Cooper ,&nbsp;Ashley Ross ,&nbsp;Hiten Patel","doi":"10.1016/j.jpi.2026.100576","DOIUrl":"10.1016/j.jpi.2026.100576","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100576"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177990","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":"Harnessing large language models for image-based diagnosis of rare and ambiguous pathological cases","authors":"Fang Bian,&nbsp;Xiao Zhou,&nbsp;Franz Fogt","doi":"10.1016/j.jpi.2026.100597","DOIUrl":"10.1016/j.jpi.2026.100597","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178332","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":"Towards scalable molecular & spatial analysis of tumor microenvironment from digital pathology","authors":"Sushant Patkar ,&nbsp;Tamara Jamaspishvili ,&nbsp;Palak Patel ,&nbsp;Alina Basnet ,&nbsp;Stephanie Harmon ,&nbsp;Peter Choyke ,&nbsp;Baris Turkbey","doi":"10.1016/j.jpi.2026.100569","DOIUrl":"10.1016/j.jpi.2026.100569","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"21 ","pages":"Article 100569"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148181106","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}