OBJECTIVE: We conducted the first Irish national study assessing the value of multidisciplinary team meeting review in pathology practice and its impact on error detection before treatment.
METHODS: Public and private pathology laboratories across Ireland capture their quality activities using standardized codes and submit their data to a centralized database (National Quality Assurance Intelligence System) overseen by the National Histopathology Quality Improvement (NHQI) program. A total of 1,437,746 histopathology and cytopathology cases submitted to the NHQI program over a 60-month period (January 2017 to December 2021) were included in this study. Cases were analyzed with respect to multidisciplinary team meeting peer review and the presence of a revised report (amended or corrected report), a surrogate marker for error detection before treatment.
RESULTS: Across all cases assessed, 13.74% (197,587) underwent multidisciplinary team meeting discussion. Cases discussed at review had a statistically significantly higher rate of revised reports (1.25% [2470]) than cases not discussed at review (0.16% [1959]) (Pearson χ2, 6619.26; P < .0001; odds ratio, 8.00 [95% CI, 7.54-8.49]). Overall, multidisciplinary team meeting review made it 8 times more likely to detect an error before treatment. Cancer resections had the highest rate of review at 55.29% (46,806), reflecting the prioritization of oncology case discussion at review meetings.
CONCLUSIONS: The multidisciplinary team meeting review process plays a valuable role in pathology error detection. A pathologist\'s participation in the review process comes with a clinically significant workload that needs to be recognized for future workforce planning. This study highlighted the positive role pathologists play in enhancing patient safety.

BACKGROUND: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases.
OBJECTIVE: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).
METHODS: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.
RESULTS: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported.
CONCLUSIONS: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice.
RESULTS: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Current guidelines recommend using Bland-Altman plots (BA-plots), also called Difference plots, as part of method comparison evaluation in the veterinary clinical pathology laboratory. Analysis of differences can meaningfully augment linear regression techniques and allows fuller summarization of the performance of two methods relative to each other. This work summarizes the current literature on BA-plot composition and evaluation. Model data is used to demonstrate data evaluation approaches based on the observed differences, the combined inherent imprecision of the methods, and clinically relevant performance goals. Common limitations of the approaches, including points of frequent misinterpretation, are presented. BA-plot analysis can be part of an intentionally crafted method comparison study that provides analytically and clinically relevant data.

UNASSIGNED: Pigmented purpuric dermatosis (PPD) is known as a chronic recurrent eruption which usually presents with petechiae and pigmented macules on the lower extremities. Dermoscopy is a noninvasive diagnostic tool in identifying pigmented and vascular lesions, which can also be beneficial in the evaluation of PPD.
UNASSIGNED: We aimed to analyze the common dermoscopic characteristics of PPD, and correlate those findings with the histopathologic features. Additionally, dermoscopic and pathological findings in this study population were compared with other similar studies from the literature review.
UNASSIGNED: A retrospective analysis was performed using data of 60 patients who were diagnosed as PPD by skin biopsy and had dermoscopic examination. The pathologic analysis was performed by categorizing the pattern into lichenoid, perivascular, interface, and spongiotic subtype, and the dermoscopic assessment was performed by the three authors independently.
UNASSIGNED: In dermoscopy, 96.7% of the patients showed red globules and dots, followed by brownish patch, coppery-red pigmentation, and annular comma-like vessels. The pathologic pattern analysis revealed statistically significant association of lichenoid pattern with coppery red pigmentation, perivascular pattern with annular/comma-like vessels, and spongiosis pattern with reticular pigmented network and linear vessels. The interrater similarity test showed total kappa value of 0.811 which referred to \"very good\".
UNASSIGNED: In this study, the prevalence of dermoscopic features in Asian PPD patients was identified, which was similar with previous studies. The dermoscopic-pathologic correlation was found in four dermoscopic features. We suggest that dermoscopic examination is helpful in clinical diagnosis and pathological prediction of PPD.

The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement was developed to provide guidance for inclusion of key methodological components in clinical trial protocols. However, these standards do not include guidance specific to pathology input in clinical trials. This systematic review aims to synthesise existing recommendations specific to pathology practice in clinical trials for implementation in trial protocol design. Articles were identified from database searches and deemed eligible for inclusion if they contained: (1) guidance and/or a checklist, which was (2) pathology-related, with (3) content relevant to clinical trial protocols or could influence a clinical trial protocol design from a pathology perspective and (4) were published in 1996 or later. The quality of individual papers was assessed using the AGREE-GRS (Appraisal of Guidelines for REsearch & Evaluation - Global Rating Scale) tool, and the confidence in cumulative evidence was evaluated using the GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative research) approach. Extracted recommendations were synthesised using the best fit framework method, which includes thematic analysis followed by a meta-aggregative approach to synthesis within the framework. Of the 10 184 records screened and 199 full-text articles reviewed, only 40 guidance resources met the eligibility criteria for inclusion. Recommendations extracted from 22 guidance documents were generalisable enough for data synthesis. Seven recommendation statements were synthesised as follows: (1) multidisciplinary collaboration in trial design with early involvement of pathologists, particularly with respect to the use of biospecimens and associated biomarker/analytical assays and in the evaluation of pathology-related parameters; (2) funding and training for personnel undertaking trial work; (3) selection of an accredited laboratory with suitable facilities to undertake scheduled work; (4) quality assurance of pathology-related parameters; (5) transparent reporting of pathology-related parameters; (6) policies regarding informatics and tracking biospecimens across trial sites; and (7) informed consent for specimen collection and retention for future research.

Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.
To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies.
This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists.
An AI-based assistive tool for Gleason grading of prostate biopsies.
Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies.
Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement.
In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

The coronavirus disease 2019 pandemic has affected business on numerous fronts in unprecedented and abrupt ways. From site closures and local \"stay-at-home orders\" to travel advisories and restrictions, the day-to-day practice of toxicologic pathology has been impacted dramatically and rapidly. A critical function of Toxicologic Pathologists is performing pathology peer review for nonclinical studies. Traditionally, corroborating the findings of histological assessment could be achieved through shipment of histopathological slides to the peer review pathologist, or by the peer review pathologist traveling to the location of the slides (eg, the test facility). Since early 2020, many pathologists have been unable to perform the latter due to local, regional, national, test facility, company, and/or personal restrictions. The disruption for some has been minimal, while others are working from home for the first time. We recommend that contingency plans for all peer review procedures and personnel should be in-place to accommodate sudden and unexpected workflow transitions. Now, more than ever, approaching peer reviews with enhanced adaptability will help ensure success.

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence-based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods.

BACKGROUND: Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature.
OBJECTIVE: We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process.
METHODS: A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies.
RESULTS: Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%).
CONCLUSIONS: The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.

To describe consecutive vulvar biopsy cases and to create an educational template for pathology trainees and practicing pathologists.
We reviewed 189 consecutive biopsies from the female genital area skin and mucosa. We classified them based on etiologies and examined limited clinical information.
We classified diagnoses as squamous intraepithelial neoplasia (21.5%), melanocytic neoplasia (17.9%), lichenoid dermatoses (15.9%), nonlichenoid dermatoses (11.3%), infectious (6.2%), reparative (4.6%), or miscellaneous (22.6%). The miscellaneous diagnoses included common entities (polyps and cysts) and rarer entities (calcinosis cutis, adnexal neoplasms, or basal cell carcinoma) and nonspecific descriptive diagnoses. Clinicians most often included the actual diagnosis in their differential for melanocytic lesions (83%) and least often for inflammatory lesions (32%). However, some cases included a clinical description without a differential diagnosis (14%) or no helpful clinical information (4%). The distribution of whether correct diagnoses were included in the clinical differential was similar between submitting physicians and midlevel providers.
Understanding squamous and melanocytic pathology and the various lichenoid and other inflammatory diagnoses is critical for signing out female genital tract skin pathology. The cases examined in this report can serve as an educational template for trainees and practicing pathologists.