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Observational data on the impact of on-site cytopathology evaluation (OCE) during endoscopic ultrasonography-guided fine needle aspiration (EUS-FNA) of pancreatic masses have reported conflicting results. We aimed to compare the diagnostic yield of malignancy and proportion of inadequate specimens between patients undergoing EUS-FNA of pancreatic masses with and without OCE. In this multicenter randomized controlled trial, consecutive patients with solid pancreatic mass underwent randomization for EUS-FNA with or without OCE. The number of FNA passes in the OCE+ arm was dictated by the on-site cytopathologist, whereas seven passes were performed in OCE- arm. EUS-FNA protocol was standardized, and slides were reviewed by cytopathologists using standardized criteria for cytologic characteristics and diagnosis. A total of 241 patients (121 OCE+, 120 OCE-) were included. There was no difference between the two groups in diagnostic yield of malignancy (OCE+ 75.2% vs. OCE- 71.6%, P=0.45) and proportion of inadequate specimens (9.8 vs. 13.3%, P=0.31). Procedures in OCE+ group required fewer EUS-FNA passes (median, OCE+ 4 vs. OCE- 7, P<0.0001). There was no significant difference between the two groups with regard to overall procedure time, adverse events, number of repeat procedures, costs (based on baseline cost-minimization analysis), and accuracy (using predefined criteria for final diagnosis of malignancy). There was no difference between the two groups with respect to cytologic characteristics of cellularity, bloodiness, number of cells/slide, and contamination. Results of this study demonstrated no significant difference in the diagnostic yield of malignancy, proportion of inadequate specimens, and accuracy in patients with pancreatic mass undergoing EUS-FNA with or without OCE.

Context.— Gastrointestinal (GI) and pancreatobiliary tracts contain a variety of neuroendocrine cells that constitute a diffuse endocrine system. Neuroendocrine tumors (NETs) from these organs are heterogeneous tumors with diverse clinical behaviors. Recent improvements in the understanding of NETs from the GI and pancreatobiliary tracts have led to more-refined definitions of the clinicopathologic characteristics of these tumors. Under the 2010 World Health Organization classification scheme, NETs are classified as grade (G) 1 NETs, G2 NETs, neuroendocrine carcinomas, and mixed adenoneuroendocrine carcinomas. Histologic grades are dependent on mitotic counts and the Ki-67 labeling index. Several new issues arose after implementation of the 2010 World Health Organization classification scheme, such as issues with well-differentiated NETs with G3 Ki-67 labeling index and the evaluation of mitotic counts and Ki-67 labeling. Hereditary syndromes, including multiple endocrine neoplasia type 1 syndrome, von Hippel-Lindau syndrome, neurofibromatosis 1, and tuberous sclerosis, are related to NETs of the GI and pancreatobiliary tracts. Several prognostic markers of GI and pancreatobiliary tract NETs have been introduced, but many of them require further validation. Objective.— To understand clinicopathologic characteristics of NETs from the GI and pancreatobiliary tracts. Data Sources.— PubMed (US National Library of Medicine) reports were reviewed. Conclusions.— In this review, we briefly summarize recent developments and issues related to NETs of the GI and pancreatobiliary tracts.

Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day’s routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the “MSK Slide Viewer”. Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.

Objective To quantify the accuracy and reproducibility of pathologists’ diagnoses of melanocytic skin lesions.Design Observer accuracy and reproducibility study.Setting 10 US states.Participants Skin biopsy cases (n=240), grouped into sets of 36 or 48. Pathologists from 10 US states were randomized to independently interpret the same set on two occasions (phases 1 and 2), at least eight months apart.Main outcome measures Pathologists’ interpretations were condensed into five classes: I (eg, nevus or mild atypia); II (eg, moderate atypia); III (eg, severe atypia or melanoma in situ); IV (eg, pathologic stage T1a (pT1a) early invasive melanoma); and V (eg, ≥pT1b invasive melanoma). Reproducibility was assessed by intraobserver and interobserver concordance rates, and accuracy by concordance with three reference diagnoses.Results In phase 1, 187 pathologists completed 8976 independent case interpretations resulting in an average of 10 (SD 4) different diagnostic terms applied to each case. Among pathologists interpreting the same cases in both phases, when pathologists diagnosed a case as class I or class V during phase 1, they gave the same diagnosis in phase 2 for the majority of cases (class I 76.7%; class V 82.6%). However, the intraobserver reproducibility was lower for cases interpreted as class II (35.2%), class III (59.5%), and class IV (63.2%). Average interobserver concordance rates were lower, but with similar trends. Accuracy using a consensus diagnosis of experienced pathologists as reference varied by class: I, 92% (95% confidence interval 90% to 94%); II, 25% (22% to 28%); III, 40% (37% to 44%); IV, 43% (39% to 46%); and V, 72% (69% to 75%). It is estimated that at a population level, 82.8% (81.0% to 84.5%) of melanocytic skin biopsy diagnoses would have their diagnosis verified if reviewed by a consensus reference panel of experienced pathologists, with 8.0% (6.2% to 9.9%) of cases overinterpreted by the initial pathologist and 9.2% (8.8% to 9.6%) underinterpreted.Conclusion Diagnoses spanning moderately dysplastic nevi to early stage invasive melanoma were neither reproducible nor accurate in this large study of pathologists in the USA. Efforts to improve clinical practice should include using a standardized classification system, acknowledging uncertainty in pathology reports, and developing tools such as molecular markers to support pathologists’ visual assessments.

Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology. To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation. An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks. Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

Context.— Colorectal cancer is a heterogeneous disease entity with 3 molecular carcinogenesis pathways and 2 morphologic multistep pathways. Right-sided colon cancers and left-sided colon and rectal cancers exhibit differences in their incidence rates according to geographic region, age, and sex. A linear tendency toward increasing frequencies of microsatellite instability–high or CpG island methylator phenotype–high cancers in subsites along the bowel from the rectum to the cecum or the ascending colon accounts for the differences in tumor phenotypes associated with these subsites. The molecular subtypes of colorectal cancers exhibit different responses to adjuvant therapy, which might be responsible for differences in subtype-specific survival. Objectives.— To review the clinicopathologic and molecular features of the molecular subtypes of colorectal cancer generated by combined CpG island methylator phenotype and microsatellite statuses, to integrate these features with the most recent findings in the context of the prognostic implications of molecular subtypes, and to emphasize the necessity of developing molecular markers that enable the identification of adenocarcinomas involving the serrated neoplasia pathway. Data Sources.— Based on the authors' own experimental data and a review of the pertinent literature. Conclusions.— Because colorectal cancers arise from 2 different morphologic multistep carcinogenesis pathways with varying contributions from 3 different molecular carcinogenesis pathways, colorectal cancer is a heterogeneous and complex disease. Thus, molecular subtyping of colorectal cancers is an important approach to characterizing their heterogeneity with respect to not only prognosis and therapeutic response but also biology and natural history.

This study aimed to enhance medical education by integrating virtual reality (VR) tours into the clinical pathology curriculum, comparing VR with traditional didactic methods. Seven 360° VR tours were developed for the Microbiology and Transfusion Services laboratories. A controlled crossover study involved 171 medical students (83% MS4) from April 2021 to April 2023. Students were randomly assigned to either the VR or PowerPoint (PP) presentation groups. Surveys and assessments measured understanding, interactivity, relevance, and engagement. With more than a 90% response rate, VR participants rated the interactivity significantly higher than PP participants (mean, 4.48 vs 3.48; P < .001). The VR format also showed higher scores for understanding the laboratory environment (mean, 4.38; P = .6) and engagement (mean, 4.21; P = .004). Although assessment scores were slightly lower for VR participants (6.2 vs 6.5; P = .1), the VR tours increased engagement and provided a more interactive learning experience. Integrating 360° VR tours into the clinical pathology curriculum enhances interactivity and learner engagement, offering a scalable solution for remote learning. This method addresses the limitations of traditional remote learning, promoting a more immersive educational experience.

Digital pathology (DP) implementations vary in scale, based on aims of intended operation. Few laboratories have completed a full-scale DP implementation, which may be due to high overhead costs that disrupt the traditional pathology workflow. Neither standardized criteria nor benchmark data have yet been published showing practical return on investment after implementing a DP platform. To provide benchmark data and practical metrics to support operational efficiency and cost savings in a large academic center. Metrics reviewed include archived pathology asset retrieval; ancillary test request for recurrent/metastatic disease; cost analysis and turnaround time (TAT); and DP experience survey. Glass slide requests from the department slide archive and an off-site surgery center showed a 93% and 97% decrease, respectively. Ancillary immunohistochemical orders, compared in 2014 (52%)-before whole slide images (WSIs) were available in the laboratory information system-and 2017 (21%) showed $114 000/y in anticipated savings. Comprehensive comparative cost analysis showed a 5-year $1.3 million savings. Surgical resection cases with prior WSIs showed a 1-day decrease in TAT. A DP experience survey showed 80% of respondents agreed WSIs improved their clinical sign-out experience. Implementing a DP operation showed a noteworthy increase in efficiency and operational utility. Digital pathology deployments and operations may be gauged by the following metrics: number of glass slide requests as WSIs become available, decrease in confirmatory testing for patients with metastatic/recurrent disease, long-term decrease in off-site pathology asset costs, and faster TAT. Other departments may use our benchmark data and metrics to enhance patient care and demonstrate return on investment to justify adoption of DP.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.