Journal of Pathology Informatics最新文献

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“Stream” lining the resident workflow: a pilot program for the application of stream deck technology “溪流”内衬居民工作流程：溪流甲板技术应用的试点项目

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100478

Andrew Johnson , Olivia Sagan , Alexander Besen , Vektra Casler , Sarah Findeis

引用次数: 0

Machine learning identifies unrecognized IV fluid contamination of complete blood counts that motivates potentially unnecessary red blood cell transfusions 机器学习可以识别未被识别的全血细胞计数的静脉输液污染，从而激发可能不必要的红细胞输注

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100490

Carly Maucione , Nathan McLamb , Mark A. Zaydman , Nicholas C. Spies

引用次数: 0

The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook 人工智能病理学家：一项全球调查，绘制了人工智能采用和前景的初步趋势

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 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.

ChatGPT等人工智能驱动工具的兴起正在改变包括病理学在内的专业领域。这项研究为病理学实习生和执业病理学家如何将人工智能融入他们的培训和临床实践提供了早期的见解。评估病理学专业人员对人工智能驱动工具（包括大型语言模型和视觉语言模型）的采用、使用模式、认知和挑战。该研究还探讨了人工智能集成的未来方向。横断面匿名调查通过研究生医学教育项目主任注册认证委员会、专业组织和社交媒体（X、Reddit、LinkedIn和the Pathologist email listserv）以电子方式分发给病理学住院医师、研究员和主治病理学家。该调查包括多项选择题、李克特量表和开放式问题，涉及人工智能的熟悉程度、使用情况、感知的利益/风险和制度政策。使用描述性和推断性统计分析数据，并按主题对定性反应进行分类。共有268名受访者参与，主要是住院医生（41%）、主治医生（39%）和研究员（7%），代表23个国家（65%来自美国）。大多数人隶属于学术医疗中心（72%），年龄在25-44岁之间。尽管73%的人表示对人工智能有所了解，但实际使用有限，31%的人表示很少使用，29%的人根本不使用，尤其是在住院医生和主治医生中。ChatGPT是最常用的工具（84%），主要用于文档起草（57%）、研究（54%）和管理任务（34%）。诊断应用很少。最令人担忧的问题包括准确性（81%）、过度依赖（65%）和数据安全性（63%）。只有10%的受访者表示有明确的机构人工智能指导方针。熟悉度与使用频率密切相关（p < 0.00001）。人工智能越来越多地用于非诊断病理学领域，但采用仍然谨慎。在临床应用、信任和机构支持方面存在重大差距。明确的指导方针、有针对性的教育和强有力的验证对于安全、有效地将人工智能整合到病理学实践和培训中至关重要。

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引用次数: 0

Quantifying partial pathological response rate in prostate cancer patients who underwent neoadjuvant chemotherapy using a novel morphometric approach 量化前列腺癌患者接受新辅助化疗的部分病理反应率使用一种新的形态计量方法

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 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.

准确评估新辅助化疗（NAT）的部分病理反应率（ppRR）对于评估治疗效果和优化临床管理至关重要。由于缺乏对基线癌症负担的准确估计，从未尝试在前列腺组织学上评估ppRR。我们提出了一种新的形态计量学方法来评估接受NAT治疗的患者的ppRR，然后将ppRR与患者的预后联系起来。对照队列包括39例NAT-naïve高危PCa高加索患者（定义为Gleason分级组>；2）和足够的活检样本（定义为活检的PCa区域大小，包括PCa上皮和间质>；2 mm2）。研究队列包括26例高危PCa患者（定义为临床分期T3a或更高，血清PSA >；20 ng/mL，或GGG为4-5，或患有少转移性疾病），接受雄激素剥夺治疗加多西他赛。以前列腺癌上皮细胞与间质比率（E/S）为指标，从治疗前活检样本中预测研究队列的替代BCB，并计算ppRR。以ppRR >；80%为截止值，对患者ppRR与无进展生存期进行相关性分析。研究队列中26例患者中有9例使用基于PCa E/ s的方法对NAT有显著反应（ppRR >； 80%），这些患者的无进展生存期明显更好（p = 0.006）。使用PCa E/S作为活检和RP样本的替代指标，可以可靠地评估ppRR到NAT，并且ppRR可用于预测患者的预后。

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引用次数: 0

Ki67 in cytological specimens of pancreatic neuroendocrine tumors: A literature review and validation of automated quantification 胰腺神经内分泌肿瘤细胞学标本中的Ki67：文献综述和自动定量验证

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 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。

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引用次数: 0

AI tool for spatial alignment of prostate whole-mount histopathology and magnetic resonance imaging 用于前列腺全挂载组织病理学和磁共振成像空间对齐的人工智能工具

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 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

引用次数: 0

Quantitative evaluation of HALO-AI phenotyping algorithm for detecting immune cells from multiplex immunofluorescence images 从多重免疫荧光图像中检测免疫细胞的HALO-AI表型算法的定量评价

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100468

Sepideh Mojtahedzadeh , Sripad Ram

引用次数: 0

Analysis of histopathology data drift in an eight-year cohort reveals laboratory- and instrumentation-induced variability 对一个8年队列的组织病理学数据漂移的分析揭示了实验室和仪器诱发的变异

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100499

Mathew Francis , Wenchao Han , Christopher A. Garcia , Mark D. Zarella

引用次数: 0

A web-based application for streamlined serum electrophoresis interpretation using federated queries across clinical and laboratory databases 一个基于web的应用程序，用于流线型血清电泳解释，使用跨临床和实验室数据库的联邦查询

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100496

Michael Olp , Keluo Yao , Roomi Nusrat , David Manthei , David Keren , Ulysses Balis

引用次数: 0

Implementation and validation of whole slide imaging for primary diagnosis across multiple affiliate locations at an academic medical center 在一个学术医疗中心的多个分支机构实施和验证用于初级诊断的全幻灯片成像

Q2 Medicine

Journal of Pathology Informatics

Pub Date : 2025-11-01 DOI: 10.1016/j.jpi.2025.100469

John Joseph , Nicola Dundas

引用次数: 0

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