Pub Date : 2025-11-01Epub Date: 2025-11-11DOI: 10.1016/j.jpi.2025.100528
Wei Huang , Huihua Li , Philipos Tsourkas , Sean Mcilwain , Irene Ong , Christos E. Kyriakopoulos , Brian Johnson , Steve Y. Cho , Shane A. Wells , Alejandro Roldan Alzate , David F. Jarrard , Erika Heninger , Joshua M. Lang
Accurate assessment of partial pathological response rate (ppRR) to neoadjuvant chemotherapy (NAT) is critical for assessing the efficacy of therapy and for optimal clinical management. Because of a lack of accurate estimation of baseline cancer burden, assessment of ppRR has never been attempted in prostate histologically. We presented a novel morphometric approach assessing ppRR in patients who underwent NAT and then correlated the ppRR with patients' outcomes. A control cohort consisted of 39 NAT-naïve Caucasian patients who had high-risk PCa (defined as Gleason Grade Group >2) and an adequate biopsy sample (defined as the size of the biopsy PCa area, including PCa epithelium and stroma >2 mm2). A study cohort included 26 patients with high-risk PCa (defined as clinical stage T3a or higher, serum PSA >20 ng/mL, or GGG of 4–5, or with oligometastatic disease) who underwent androgen deprivation therapy plus docetaxel. Using the PCa epithelial to stromal ratio (E/S) as a metric, surrogate BCB for the study cohort was predicted from the pre-treatment biopsy samples, and ppRR was calculated. Correlation analysis of patients' ppRR with progression-free survival was performed using ppRR >80% as a cut-off.
Nine of the 26 patients from the study cohort experienced a significant response to NAT (ppRR > 80%) using the PCa E/S-based approach, and these patients had significantly better progression-free survival (p = 0.006). ppRR to NAT can be reliably assessed using PCa E/S as a surrogate metric from biopsy and RP samples, and ppRR can be used to predict patients' outcomes.
{"title":"Quantifying partial pathological response rate in prostate cancer patients who underwent neoadjuvant chemotherapy using a novel morphometric approach","authors":"Wei Huang , Huihua Li , Philipos Tsourkas , Sean Mcilwain , Irene Ong , Christos E. Kyriakopoulos , Brian Johnson , Steve Y. Cho , Shane A. Wells , Alejandro Roldan Alzate , David F. Jarrard , Erika Heninger , Joshua M. Lang","doi":"10.1016/j.jpi.2025.100528","DOIUrl":"10.1016/j.jpi.2025.100528","url":null,"abstract":"<div><div>Accurate assessment of partial pathological response rate (ppRR) to neoadjuvant chemotherapy (NAT) is critical for assessing the efficacy of therapy and for optimal clinical management. Because of a lack of accurate estimation of baseline cancer burden, assessment of ppRR has never been attempted in prostate histologically. We presented a novel morphometric approach assessing ppRR in patients who underwent NAT and then correlated the ppRR with patients' outcomes. A control cohort consisted of 39 NAT-naïve Caucasian patients who had high-risk PCa (defined as Gleason Grade Group >2) and an adequate biopsy sample (defined as the size of the biopsy PCa area, including PCa epithelium and stroma >2 <sup>mm2</sup>). A study cohort included 26 patients with high-risk PCa (defined as clinical stage T3a or higher, serum PSA >20 ng/mL, or GGG of 4–5, or with oligometastatic disease) who underwent androgen deprivation therapy plus docetaxel. Using the PCa epithelial to stromal ratio (E/S) as a metric, surrogate BCB for the study cohort was predicted from the pre-treatment biopsy samples, and ppRR was calculated. Correlation analysis of patients' ppRR with progression-free survival was performed using ppRR >80% as a cut-off.</div><div>Nine of the 26 patients from the study cohort experienced a significant response to NAT (ppRR > 80%) using the PCa E/S-based approach, and these patients had significantly better progression-free survival (<em>p</em> = 0.006). ppRR to NAT can be reliably assessed using PCa E/S as a surrogate metric from biopsy and RP samples, and ppRR can be used to predict patients' outcomes.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100528"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690694","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}
Pub Date : 2025-11-01Epub Date: 2025-09-08DOI: 10.1016/j.jpi.2025.100516
Liron Pantanowitz, Anil Parwani
{"title":"The path forward: Evolving standards for a smarter digital pathology ecosystem","authors":"Liron Pantanowitz, Anil Parwani","doi":"10.1016/j.jpi.2025.100516","DOIUrl":"10.1016/j.jpi.2025.100516","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267200","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}
Pub Date : 2025-11-01Epub Date: 2025-12-13DOI: 10.1016/j.jpi.2025.100507
Blaise Clarke , Charlotte Carment-Baker , Amiee Langan , Christine Bruce , George M. Yousef
{"title":"Digital pathology implementation in a multi-site hospital network: the devil is in the details","authors":"Blaise Clarke , Charlotte Carment-Baker , Amiee Langan , Christine Bruce , George M. Yousef","doi":"10.1016/j.jpi.2025.100507","DOIUrl":"10.1016/j.jpi.2025.100507","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100507"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796629","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}
Pub Date : 2025-11-01Epub Date: 2025-12-13DOI: 10.1016/j.jpi.2025.100478
Andrew Johnson , Olivia Sagan , Alexander Besen , Vektra Casler , Sarah Findeis
{"title":"“Stream” lining the resident workflow: a pilot program for the application of stream deck technology","authors":"Andrew Johnson , Olivia Sagan , Alexander Besen , Vektra Casler , Sarah Findeis","doi":"10.1016/j.jpi.2025.100478","DOIUrl":"10.1016/j.jpi.2025.100478","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796988","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}
Pub Date : 2025-11-01Epub Date: 2025-12-13DOI: 10.1016/j.jpi.2025.100490
Carly Maucione , Nathan McLamb , Mark A. Zaydman , Nicholas C. Spies
{"title":"Machine learning identifies unrecognized IV fluid contamination of complete blood counts that motivates potentially unnecessary red blood cell transfusions","authors":"Carly Maucione , Nathan McLamb , Mark A. Zaydman , Nicholas C. Spies","doi":"10.1016/j.jpi.2025.100490","DOIUrl":"10.1016/j.jpi.2025.100490","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100490"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797143","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}
Pub Date : 2025-11-01Epub Date: 2025-11-10DOI: 10.1016/j.jpi.2025.100526
Meredith K. Herman , Sania Qazi BS , Elisa Farrell BS , Julie Song BS , Matthew Cecchini MD, PhD , Kamran M. Mirza MD, PhD , Marilyn M. Bui MD, PhD , Sean M. Hacking MD
The rise of artificial intelligence (AI)-driven tools like ChatGPT is transforming professional fields, including pathology. This study provides early insights into how pathology trainees and practicing pathologists are integrating AI into their training and clinical practice. To assess adoption, usage patterns, perceptions, and challenges related to AI-driven tools, including large language models and vision-language models, among pathology professionals. The study also explores future directions for AI integration. A cross-sectional, anonymous survey was distributed electronically to pathology residents, fellows, and attending pathologists through the Accreditation Council for Graduate Medical Education program director registry, professional organizations, and social media (X, Reddit, LinkedIn, and The Pathologist email listserv). The survey included multiple-choice, Likert-scale, and open-ended questions on AI familiarity, usage, perceived benefits/risks, and institutional policies. Data were analyzed using descriptive and inferential statistics, with qualitative responses categorized thematically. A total of 268 respondents participated, primarily residents (41%), attendings (39%), and fellows (7%), representing 23 countries (65% from the USA). Most were affiliated with academic medical centers (72%) and aged 25–44. Whereas 73% reported some familiarity with AI, actual use was limited, 31% reported rare use and 29% no use at all, especially among residents and attendings. ChatGPT was the most used tool (84%), applied mainly for document drafting (57%), research (54%), and administrative tasks (34%). Diagnostic use was minimal. Top concerns included accuracy (81%), over-reliance (65%), and data security (63%). Only 10% reported having clear institutional AI guidelines. Familiarity was strongly associated with usage frequency (p < 0.00001). AI is increasingly used in non-diagnostic areas of pathology but adoption remains cautious. Significant gaps in clinical application, trust, and institutional support persist. Clear guidelines, targeted education, and robust validation are essential for safe, effective AI integration into pathology practice and training.
{"title":"The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook","authors":"Meredith K. Herman , Sania Qazi BS , Elisa Farrell BS , Julie Song BS , Matthew Cecchini MD, PhD , Kamran M. Mirza MD, PhD , Marilyn M. Bui MD, PhD , Sean M. Hacking MD","doi":"10.1016/j.jpi.2025.100526","DOIUrl":"10.1016/j.jpi.2025.100526","url":null,"abstract":"<div><div>The rise of artificial intelligence (AI)-driven tools like ChatGPT is transforming professional fields, including pathology. This study provides early insights into how pathology trainees and practicing pathologists are integrating AI into their training and clinical practice. To assess adoption, usage patterns, perceptions, and challenges related to AI-driven tools, including large language models and vision-language models, among pathology professionals. The study also explores future directions for AI integration. A cross-sectional, anonymous survey was distributed electronically to pathology residents, fellows, and attending pathologists through the Accreditation Council for Graduate Medical Education program director registry, professional organizations, and social media (X, Reddit, LinkedIn, and <em>The Pathologist</em> email listserv). The survey included multiple-choice, Likert-scale, and open-ended questions on AI familiarity, usage, perceived benefits/risks, and institutional policies. Data were analyzed using descriptive and inferential statistics, with qualitative responses categorized thematically. A total of 268 respondents participated, primarily residents (41%), attendings (39%), and fellows (7%), representing 23 countries (65% from the USA). Most were affiliated with academic medical centers (72%) and aged 25–44. Whereas 73% reported some familiarity with AI, actual use was limited, 31% reported rare use and 29% no use at all, especially among residents and attendings. ChatGPT was the most used tool (84%), applied mainly for document drafting (57%), research (54%), and administrative tasks (34%). Diagnostic use was minimal. Top concerns included accuracy (81%), over-reliance (65%), and data security (63%). Only 10% reported having clear institutional AI guidelines. Familiarity was strongly associated with usage frequency (<em>p</em> <!--><<!--> <!-->0.00001). AI is increasingly used in non-diagnostic areas of pathology but adoption remains cautious. Significant gaps in clinical application, trust, and institutional support persist. Clear guidelines, targeted education, and robust validation are essential for safe, effective AI integration into pathology practice and training.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100526"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797159","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}
Pub Date : 2025-11-01Epub Date: 2025-10-21DOI: 10.1016/j.jpi.2025.100523
Michael N. Wicks , Michael Glinka , Bill Hill , Derek Houghton , Bernard Haggarty , Jorge Del-Pozo , Ingrid Ferreira , Florian Jaeckle , David Adams , Shahida Din , Irene Papatheodorou , Kathryn Kirkwood , Albert Burger , Richard A. Baldock , Mark J. Arends
The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive “spreadsheet” style presentation of image data and associated analysis data. The software was developed to enable pathologists and other clinical and research users to compare histopathological images of diseased and/or normal tissues between different samples of the same or different patients/species. The CPW provides a grid layout of cells in rows and columns so that images that correspond to matching data can be organized in the form of an image-enabled “spreadsheet”. An individual workbench or bench can be shared with other users with read-only or full edit access as required. In addition, each bench cell or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. Here, we present the updated system based on 2 years of active use in the field that generated constructive feedback. The updates deliver new capabilities, including automated importation of entire image collections, sorting image collections, long running tasks, public benches, uploading miscellaneous image types, refining search facilities, enabling use of tags, and improving efficiency, speed, and user-friendliness.
{"title":"The comparative pathology workbench: An update","authors":"Michael N. Wicks , Michael Glinka , Bill Hill , Derek Houghton , Bernard Haggarty , Jorge Del-Pozo , Ingrid Ferreira , Florian Jaeckle , David Adams , Shahida Din , Irene Papatheodorou , Kathryn Kirkwood , Albert Burger , Richard A. Baldock , Mark J. Arends","doi":"10.1016/j.jpi.2025.100523","DOIUrl":"10.1016/j.jpi.2025.100523","url":null,"abstract":"<div><div>The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive “spreadsheet” style presentation of image data and associated analysis data. The software was developed to enable pathologists and other clinical and research users to compare histopathological images of diseased and/or normal tissues between different samples of the same or different patients/species. The CPW provides a grid layout of cells in rows and columns so that images that correspond to matching data can be organized in the form of an image-enabled “spreadsheet”. An individual workbench or bench can be shared with other users with read-only or full edit access as required. In addition, each bench cell or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. Here, we present the updated system based on 2 years of active use in the field that generated constructive feedback. The updates deliver new capabilities, including automated importation of entire image collections, sorting image collections, long running tasks, public benches, uploading miscellaneous image types, refining search facilities, enabling use of tags, and improving efficiency, speed, and user-friendliness.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100523"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525751","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}
Pub Date : 2025-11-01Epub Date: 2025-12-13DOI: 10.1016/j.jpi.2025.100505
Fatemeh Zabihollahy , Holden H. Wu , Sohaib Naim , Anthony E. Sisk , Robert E. Reiter , Steven S. Raman , Neil E. Fleshner , George M. Yousef , KyungHyun Sung
{"title":"AI tool for spatial alignment of prostate whole-mount histopathology and magnetic resonance imaging","authors":"Fatemeh Zabihollahy , Holden H. Wu , Sohaib Naim , Anthony E. Sisk , Robert E. Reiter , Steven S. Raman , Neil E. Fleshner , George M. Yousef , KyungHyun Sung","doi":"10.1016/j.jpi.2025.100505","DOIUrl":"10.1016/j.jpi.2025.100505","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796708","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号