Endocervical adenocarcinomas (EACs) are a group of malignant neoplasms associated with diverse pathogenesis, morphology, and clinical behavior. As a component of the International Society of Gynecological Pathologists International Endocervical Adenocarcinoma Project, a large international retrospective cohort of EACs was generated in an effort to study potential clinicopathological features with prognostic significance that may guide treatment in these patients. In this study, we endeavored to develop a robust human papillomavirus (HPV)-associated EAC prognostic model for surgically treated International Federation of Gynecology and Obstetrics (FIGO) stage IA2 to IB3 adenocarcinomas incorporating patient age, lymphovascular space invasion (LVSI) status, FIGO stage, and pattern of invasion according to the Silva system (traditionally a 3-tier system). Recently, a 2-tier/binary Silva pattern of invasion system has been proposed whereby adenocarcinomas are classified into low-risk (pattern A/pattern B without LVSI) and high-risk (pattern B with LVSI/pattern C) categories. Our cohort comprised 792 patients with HPV-associated EAC. Multivariate analysis showed that a binary Silva pattern of invasion classification was associated with recurrence-free and disease-specific survival (P < 0.05) whereas FIGO 2018 stage I substages were not. Evaluation of the current 3-tiered system showed that disease-specific survival for those patients with pattern B tumors did not significantly differ from that for those patients with pattern C tumors, in contrast to that for those patients with pattern A tumors. These findings underscore the need for prospective studies to further investigate the prognostic significance of stage I HPV-associated EAC substaging and the inclusion of the binary Silva pattern of invasion classification (which includes LVSI status) as a component of treatment recommendations.

The Silva pattern-based classification of HPV-associated endocervical adenocarcinoma has become an integral part of the histologic assessment of these tumors. Unfortunately, the Silva system reproducibility has had mixed results in past studies, and clinical practice still favors the FIGO stage assessment in directing therapeutic interventions for patients. In our study, we aimed to assess our institution\'s concordance including not only gynecologic pathologists, but also pathology trainees through a series of 69 cases. The grouped total kappa concordance from all participants was 0.439 (Moderate), with an overall trainee kappa of 0.417 (moderate) and an overall pathologist kappa of 0.460 (moderate). Perfect concordance among all 10 study participants was seen in 8/69 cases (11.6 %), corresponding to 5/22 Pattern A cases (22.7 %), 0/16 Pattern B cases (0 %), and 3/31 Pattern C cases (9.7 %), with similar findings between trainees and pathologists when compared within their own cohorts. Recurrence was identified in 2 Pattern A cases, indicating a potential issue with limited excisional specimens which may not fully appreciate the true biologic aggressiveness of the lesions.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Sucralose, a sugar substitute first approved for use in 1991, is a non-caloric sweetener regulated globally as a food additive. Based on numerous experimental animal studies (dating to the 1980s) and human epidemiology studies, international health agencies have determined that sucralose is safe when consumed as intended. A single lifetime rodent carcinogenicity bioassay conducted by the Ramazzini Institute (RI) reported that mice fed diets containing sucralose develop hematopoietic neoplasia, but controversy continues regarding the validity and relevance of these data for predicting health effects in humans. The present paper addresses the controversy by providing the perspective of experienced pathologists on sucralose-related animal toxicity and carcinogenicity data generally, and the RI carcinogenicity bioassay findings specifically, using results from publicly available papers and international regulatory authority decisions. In the authors\' view, flaws in the design, methodology, data evaluation, and reporting of the RI carcinogenicity bioassay for sucralose diminish the value of the data as evidence that this agent represents a carcinogenic hazard to humans. This limitation will remain until the RI bioassay is repeated under Good Laboratory Practices and the design, data, and accuracy of the pathology diagnoses and interpretations are reviewed by qualified pathologists with experience in evaluating potential chemically-induced carcinogenic hazards.

BACKGROUND: Liver histopathologic assessment is the accepted surrogate endpoint in NASH trials; however, the scoring of NASH Clinical Research Network (CRN) histologic parameters is limited by intraobserver and interobserver variability. We designed a consensus panel approach to minimize variability when using this scoring system. We assessed agreement between readers, estimated linear weighted kappas between 2 panels, compared them with published pairwise kappa estimates, and addressed how agreement or disagreement might impact the precision and validity of the surrogate efficacy endpoint in NASH trials.
METHODS: Two panels, each comprising 3 liver fellowship-trained pathologists who underwent NASH histology training, independently evaluated scanned whole slide images, scoring fibrosis, inflammation, hepatocyte ballooning, and steatosis from baseline and month 18 biopsies for 100 patients from the precirrhotic NASH study REGENERATE. The consensus score for each parameter was defined as agreement by ≥2 pathologists. If consensus was not reached, all 3 pathologists read the slide jointly to achieve a consensus score.
RESULTS: Between the 2 panels, the consensus was 97%-99% for steatosis, 91%-93% for fibrosis, 88%-92% for hepatocyte ballooning, and 84%-91% for inflammation. Linear weighted kappa scores between panels were similar to published NASH CRN values.
CONCLUSIONS: A panel of 3 trained pathologists independently scoring 4 NASH CRN histology parameters produced high consensus rates. Interpanel kappa values were comparable to NASH CRN metrics, supporting the accuracy and reproducibility of this method. The high concordance for fibrosis scoring was reassuring, as fibrosis is predictive of liver-specific outcomes and all-cause mortality.

Wide-area transepithelial sampling with 3-dimensional computer-assisted analysis (WATS 3D ) has been shown to increase the detection rate of dysplasia (and intestinal metaplasia) in patients with Barrett\'s esophagus (BE). The purpose of this study was to evaluate the interobserver variability and accuracy of diagnosing BE-associated dysplasia in WATS 3D specimens among gastrointestinal (GI) pathologists without prior experience with this technology.
Five GI pathologists underwent a 4-hour in-person (at microscope) and virtual training session and then evaluated digital images of discrete cellular foci from 60 WATS 3D cases with BE (20 nondysplastic BE [NDBE], 20 low-grade dysplasia [LGD], and 20 high-grade dysplasia/esophageal adenocarcinoma [HGD/EAC]). Each case consisted of 1 hematoxylin and eosin-stained image (cell block), and 1 liquid cytology or papanicolaou-stained smear image (120 images in total).
The overall kappa value among the 5 study pathologists was excellent (overall kappa = 0.93; kappa = 0.93 and 0.97 for cell block and smear specimens, respectively). There were no significant differences noted in kappa values in interpretation of the cell block vs smear specimens or in any of the individual diagnostic categories when the latter were evaluated separately. Furthermore, agreement was perfect (100%) regarding detection of neoplasia (either LGD, HGD, or EAC). Diagnoses were made with complete confidence in 91% of instances.
We conclude that GI pathologists, without any prior experience in interpretation of WATS 3D specimens, can undergo a short training session and then diagnose these specimens with a very high level of accuracy and reproducibility.

The burden of oral cancer in Nigeria is increasing. Different studies have shown how public education on oral cancer have increased knowledge of oral cancer across populations, however, it is not known if these practices are adopted by oral physicians, oral and maxillofacial surgeons, and oral pathologists in Nigeria.
To investigate the patient oral cancer education strategies adopted by oral physicians, oral and maxillofacial surgeons, and oral pathologists in Nigeria.
This study adopted an analytical cross-sectional study design. This study surveyed practicing oral physicians, oral and maxillofacial surgeons, and oral pathologists in Nigeria. An e-questionnaire was used for this study. The data were analyzed using the SPSS Version 20 software, and a p-value of <.05 was used to determine the level of statistical significance.
The study\'s response rate was 46.6% (75/161). The 75 participants were from the six geopolitical zones in Nigeria responded to the survey questionnaire. Even though more than half (43/75, 57.3%) of the respondents have never received any training since their post-bachelor\'s degree qualification on the strategies that can be used in educating patients on oral cancer, majority (54/75, 72.0%) of them knew at least one education strategy; also, the most known (36/54, 66.7%) and utilized (33/54, 61.3%) strategy among those respondents who were aware of patient education strategy was health talk. Only 38.7% (29/75) of the respondents reported that health learning materials (posters, leaflets, fliers, and flipcharts) are available in their clinics, all of which were in insufficient quantities. Also, 93.3% (70/75) of the respondents opined that it is worthwhile that dental clinics/hospitals in Nigeria invest in the provision of oral cancer learning materials for patient use. Inferential statistical analysis did not reveal any significant relationship between the respondents\' characteristics and their awareness and practice on patient oral cancer education strategies.
This study identified that many oral physicians, oral and maxillofacial surgeons, and oral pathologists in Nigeria lack the needed capacity to educate their patients on oral cancer. There is a need to strengthen their capacity by giving them training on patient oral cancer education strategies, and by providing them with good quality and enough teaching aids.

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists\' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist\'s expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT\'s superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.

Background Large language models (LLMs), such as ChatGPT-3.5, Google Bard, and Microsoft Bing, have shown promising capabilities in various natural language processing (NLP) tasks. However, their performance and accuracy in solving domain-specific questions, particularly in the field of hematology, have not been extensively investigated. Objective This study aimed to explore the capability of LLMs, namely, ChatGPT-3.5, Google Bard, and Microsoft Bing (Precise), in solving hematology-related cases and comparing their performance. Methods This was a cross-sectional study conducted in the Department of Physiology and Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India. We curated a set of 50 cases on hematology covering a range of topics and complexities. The dataset included queries related to blood disorders, hematologic malignancies, laboratory test parameters, calculations, and treatment options. Each case and related question was prepared with a set of correct answers to compare with. We utilized ChatGPT-3.5, Google Bard Experiment, and Microsoft Bing (Precise) for question-answering tasks. The answers were checked by two physiologists and one pathologist. They rated the answers on a rating scale from one to five. The average score of the three models was compared by Friedman\'s test with Dunn\'s post-hoc test. The performance of the LLMs was compared with a median of 2.5 by a one-sample median test as the curriculum from which the questions were curated has a 50% pass grade. Results The scores among the three LLMs were significantly different (p-value < 0.0001) with the highest score by ChatGPT (3.15±1.19), followed by Bard (2.23±1.17) and Bing (1.98±1.01). The score of ChatGPT was significantly higher than 50% (p-value = 0.0004), Bard\'s score was close to 50% (p-value = 0.38), and Bing\'s score was significantly lower than the pass score (p-value = 0.0015). Conclusion The LLMs reveal significant differences in solving case vignettes in hematology. ChatGPT exhibited the highest score, followed by Google Bard and Microsoft Bing. The observed performance trends suggest that ChatGPT holds promising potential in the medical domain. However, none of the models was capable of answering all questions accurately. Further research and optimization of language models can offer valuable contributions to healthcare and medical education applications.