Pub Date : 2025-11-01DOI: 10.1016/j.jpi.2025.100481
Christopher Williams
{"title":"Using a Git service provider and the web browser as an application server for clinical pathology job aids","authors":"Christopher Williams","doi":"10.1016/j.jpi.2025.100481","DOIUrl":"10.1016/j.jpi.2025.100481","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100481"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797070","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 : 2025-11-01DOI: 10.1016/j.jpi.2025.100522
Andrew L. Valesano, Stephanie L. Skala , Mustafa Yousif
Serous tubal intraepithelial carcinoma (STIC) is an uncommon, non-invasive carcinoma that occurs more frequently in individuals with germline BRCA mutations and is an established precursor to high-grade serous ovarian carcinoma. STIC can be challenging to detect during pathologist evaluation, as it can manifest as a small focus of atypia in an otherwise benign salpingectomy specimen. There is a clinical need for scalable, weakly supervised computational approaches to aid in the detection of STIC. We developed a deep learning model to identify STIC and serous tubal intraepithelial lesions (STIL) in whole-slide images. We obtained fallopian tube specimens diagnosed as STIC (n = 49), STIL (n = 48), and benign fallopian tube (n = 83) at a single academic medical center. We trained a weakly supervised, attention-based multiple instance learning model and evaluated performance on independent datasets, including an additional unbalanced dataset (n = 40 benign, n = 2 STIL, n = 1 STIC) and cases diagnosed descriptively as benign reactive atypia (n = 53). The model achieved high sensitivity and specificity on the balanced validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.96 (95% CI: 0.90–1.00), and demonstrated similarly strong performance on unbalanced validation cohorts (AUROC 0.98). Interpretability analyses indicated that model decisions were based on epithelial atypia. These results support the potential of integrating deep learning screening tools into clinical workflows to augment pathologist efficiency and diagnostic accuracy in fallopian tubes.
{"title":"Weakly supervised deep learning-based detection of serous tubal intraepithelial carcinoma in fallopian tubes","authors":"Andrew L. Valesano, Stephanie L. Skala , Mustafa Yousif","doi":"10.1016/j.jpi.2025.100522","DOIUrl":"10.1016/j.jpi.2025.100522","url":null,"abstract":"<div><div>Serous tubal intraepithelial carcinoma (STIC) is an uncommon, non-invasive carcinoma that occurs more frequently in individuals with germline <em>BRCA</em> mutations and is an established precursor to high-grade serous ovarian carcinoma. STIC can be challenging to detect during pathologist evaluation, as it can manifest as a small focus of atypia in an otherwise benign salpingectomy specimen. There is a clinical need for scalable, weakly supervised computational approaches to aid in the detection of STIC. We developed a deep learning model to identify STIC and serous tubal intraepithelial lesions (STIL) in whole-slide images. We obtained fallopian tube specimens diagnosed as STIC (<em>n</em> = 49), STIL (<em>n</em> = 48), and benign fallopian tube (<em>n</em> = 83) at a single academic medical center. We trained a weakly supervised, attention-based multiple instance learning model and evaluated performance on independent datasets, including an additional unbalanced dataset (<em>n</em> = 40 benign, <em>n</em> = 2 STIL, <em>n</em> = 1 STIC) and cases diagnosed descriptively as benign reactive atypia (<em>n</em> = 53). The model achieved high sensitivity and specificity on the balanced validation cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.96 (95% CI: 0.90–1.00), and demonstrated similarly strong performance on unbalanced validation cohorts (AUROC 0.98). Interpretability analyses indicated that model decisions were based on epithelial atypia. These results support the potential of integrating deep learning screening tools into clinical workflows to augment pathologist efficiency and diagnostic accuracy in fallopian tubes.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525750","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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 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 : 2025-11-01DOI: 10.1016/j.jpi.2025.100527
Sahar Narimani, Sophie Pirenne, Birgit Weynand
Introduction
The Ki67 proliferation index is mandatory for grading, prognostication, and clinical decision-making in pancreatic neuroendocrine tumors (PanNETs). Automatic Ki67 quantification on cytology has been shown to be at least as accurate, less time-consuming, and more consistent than the current gold-standard manual determination. After a thorough literature review, we aimed to validate the Visiopharm image analysis software for automatic Ki67 quantification on diagnostic cell block material from PanNETs.
Methods
We conducted a retrospective study and assembled a cohort of 69 PanNETs from clinical routine with available endoscopic ultrasound fine needle aspiration cell block, Ki67, and synaptophysin immunostained slides. The manual Ki67 index, if available, was obtained from the original pathology report. Otherwise, a manual count was performed by a pathologist using a cell counter. The automatic Ki67 index was quantified through four consecutive algorithms from the Visiopharm Image Analysis software on aligned serial sections.
Results
Automatic Ki67 quantification showed a strong correlation with manual counting based on the non-parametric Spearman correlation coefficients of r = 0.786 [95% confidence interval (CI): 0.650–0.873, p < 0.001] and r = 0.721 (95% CI: 0.558–0.830, p < 0.001]), for absolute Ki67 values and grades, respectively. Grade concordance showed excellent agreement for Grade 1 and Grade 3 tumors (91.89% and 83.3%) and rather moderate agreement for Grade 2 lesions (59.09%) due to underestimation. Bland–Altman analysis obtained excellent results, with a mean underestimation of digital versus manual quantification of 0.2265%.
Conclusion
Our findings show accurate assessment of the proliferation index from PanNETs using the Visiopharm software for digital Ki67 quantification and provide a prevalidation framework for the implementation of this technique in pathology practice. Discrepancies were mainly seen in Grade 2 tumors due to tumor heterogeneity of Grade 2 lesions. To this end, future research should seek refinement of the digital algorithms and examine the reliability of prognosis and clinical endpoints based on this technique.
Ki67增殖指数是胰腺神经内分泌肿瘤（PanNETs）分级、预后和临床决策的强制性指标。细胞学上的自动Ki67定量已被证明至少与目前的金标准手工测定一样准确，更少耗时，更一致。经过全面的文献综述，我们旨在验证Visiopharm图像分析软件对PanNETs诊断细胞块材料的Ki67自动定量。方法采用内镜超声细针穿刺细胞阻滞、Ki67和synaptophysin免疫染色玻片，对69例临床常规PanNETs进行回顾性研究。手工Ki67索引（如果有的话）是从原始病理报告中获得的。否则，由病理学家使用细胞计数器进行手动计数。自动Ki67指数通过Visiopharm图像分析软件在对齐的序列切片上连续四种算法进行量化。结果Ki67的绝对值和分级的非参数Spearman相关系数分别为r = 0.786[95%置信区间（CI）： 0.650-0.873, p <； 0.001]和r = 0.721 (95% CI: 0.558-0.830, p < 0.001])，自动Ki67定量显示与人工计数有很强的相关性。分级一致性显示1级和3级肿瘤的一致性非常好（91.89%和83.3%），由于低估，2级病变的一致性相当中等（59.09%）。Bland-Altman分析获得了极好的结果，与人工量化相比，数字量化的平均低估率为0.2265%。结论使用Visiopharm软件可准确评估PanNETs的增殖指数，并为该技术在病理实践中的应用提供了预验证框架。由于2级病变的肿瘤异质性，差异主要见于2级肿瘤。为此，未来的研究应寻求数字算法的改进，并检查基于该技术的预后和临床终点的可靠性。
{"title":"Ki67 in cytological specimens of pancreatic neuroendocrine tumors: A literature review and validation of automated quantification","authors":"Sahar Narimani, Sophie Pirenne, Birgit Weynand","doi":"10.1016/j.jpi.2025.100527","DOIUrl":"10.1016/j.jpi.2025.100527","url":null,"abstract":"<div><h3>Introduction</h3><div>The Ki67 proliferation index is mandatory for grading, prognostication, and clinical decision-making in pancreatic neuroendocrine tumors (PanNETs). Automatic Ki67 quantification on cytology has been shown to be at least as accurate, less time-consuming, and more consistent than the current gold-standard manual determination. After a thorough literature review, we aimed to validate the Visiopharm image analysis software for automatic Ki67 quantification on diagnostic cell block material from PanNETs.</div></div><div><h3>Methods</h3><div>We conducted a retrospective study and assembled a cohort of 69 PanNETs from clinical routine with available endoscopic ultrasound fine needle aspiration cell block, Ki67, and synaptophysin immunostained slides. The manual Ki67 index, if available, was obtained from the original pathology report. Otherwise, a manual count was performed by a pathologist using a cell counter. The automatic Ki67 index was quantified through four consecutive algorithms from the Visiopharm Image Analysis software on aligned serial sections.</div></div><div><h3>Results</h3><div>Automatic Ki67 quantification showed a strong correlation with manual counting based on the non-parametric Spearman correlation coefficients of <em>r</em> <!-->=<!--> <!-->0.786 [95% confidence interval (CI): 0.650–0.873, <em>p</em> <!--><<!--> <!-->0.001] and <em>r</em> <!-->=<!--> <!-->0.721 (95% CI: 0.558–0.830, <em>p</em> <!--><<!--> <!-->0.001]<em>)</em>, for absolute Ki67 values and grades, respectively. Grade concordance showed excellent agreement for Grade 1 and Grade 3 tumors (91.89% and 83.3%) and rather moderate agreement for Grade 2 lesions (59.09%) due to underestimation. Bland–Altman analysis obtained excellent results, with a mean underestimation of digital versus manual quantification of 0.2265%.</div></div><div><h3>Conclusion</h3><div>Our findings show accurate assessment of the proliferation index from PanNETs using the Visiopharm software for digital Ki67 quantification and provide a prevalidation framework for the implementation of this technique in pathology practice. Discrepancies were mainly seen in Grade 2 tumors due to tumor heterogeneity of Grade 2 lesions. To this end, future research should seek refinement of the digital algorithms and examine the reliability of prognosis and clinical endpoints based on this technique.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100527"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690583","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}
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