Expert Pathologists Research Articles

Deep learning and vision transformers-based framework for breast cancer and subtype identification

Abstract Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer detection and subtype identification yielded promising results. This advanced framework shows great promise in medical practice, offering an economical, efficient solution for generating accurate, clinically relevant results and enhancing diagnostic accuracy in hospitals, research centers, and pathology laboratories. Nonetheless, further studies are needed to validate its effectiveness across various environments and larger datasets.

Pediatric eosinophilic fasciitis: similarities and differences with adult forms.

The purpose of this study was to identify pediatric eosinophilic fasciitis, which is an extremely rare condition, in order to describe their clinical, paraclinical, and therapeutic characteristics. We made a call for observations via societies for pediatric rheumatology in France and surrounding countries and collected clinical and paraclinical data of the cases fulfilling the diagnostic criteria. Eight patients under 18years of age with confirmed eosinophilic fasciitis followed between April 2004 and July 2022 in France, Germany, Italy, and Spain were included. The median age of onset of symptoms was 8.7years (range 3 to 12.6). All patients had skin and joint involvement at diagnosis. Eosinophilia was present at diagnosis in 5/8 patients and 5/7 patients presented hypergammaglobulinemia. All the patients had an MRI, and in 6, we observed thickened fascia with a T2 hypersignal. Five patients had undergone a full-thickness biopsy showing a polymorphic lymphoplasmacytic infiltrate of the fascia in all and the presence of eosinophils in 4 of them. All the patients were treated with corticosteroids with variable regimens and all received at least an immunosuppressant. Conclusion: To our knowledge, this is the largest pediatric series of eosinophilic fasciitis. The clinical and paraclinical presentation seems similar to that of adults except for a form that appears to be distinguished with isolated joint contractures, and hypergammaglobulinemia which appears to be more frequent in children. Dermatological and pathological expertise and MRI are key elements of the diagnosis. The most consensual treatment includes physiotherapy, prolonged corticosteroid therapy, and methotrexate as first-line therapy. What is Known •Eosinophilic fasciitis is a rare condition, especially in children with approximately 60 reportedpediatric cases. •Some pediatric specificities tend to emerge from somereports. What is New •Possible isolated joints contractures andmore frequent hypergammaglobulinemia seem to characterize pediatric eosinophilic fasciitis incomparison with adult forms. •The most frequently used first-line therapy combinesphysiotherapy, corticosteroids and methrotrexate.

Prevalence of occult endometrial carcinoma in patients with endometrial intraepithelial neoplasia who underwent hysterectomy

ObjectivesTo determine the prevalence of occult endometrial carcinoma in patients with endometrial intraepithelial neoplasia (EIN) post-hysterectomy and identify pre-hysterectomy risk factors predictive of occult carcinoma.MethodsThis retrospective study included patients diagnosed with EIN between 2007 and 2021 who underwent hysterectomy as primary treatment. An expert gynecologic pathologist reviewed pathological slides. Data collected from medical records included demographic and gynecologic information, sonographic findings, and surgical and pathological outcomes. The prevalence of occult endometrial carcinoma was calculated. Descriptive statistics evaluated carcinoma incidence, and logistic regression analysis identified independent risk factors.ResultsA total of 113 patients were evaluated. The median time to hysterectomy was 9.1 weeks (range 5.8–12.8 weeks). Post-hysterectomy, 36 patients (31.8%) were diagnosed with endometrial carcinoma, all endometrioid type. Of these, 88.9% were stage I per the International Federation of Gynecology and Obstetrics classification system, and 11.1% were at high risk for nodal metastasis. Predictive factors for occult carcinoma included the intraoperative gross lesion size (2 cm or larger and less than 2 cm) and endometrial aspiration. Adjusted odds ratios were 6.723 (95% CI 2.338 to 19.333) for lesions 2 cm or larger, 3.381 (95% CI 1.128 to 10.132) for lesions less than 2 cm, and 2.752 (95% CI 1.092 to 6.936) for endometrial aspiration.ConclusionsOccult endometrial carcinoma was identified in 31.8% of patients with a pre-hysterectomy EIN diagnosis. The significant predictors were endometrial aspiration and the presence of a gross lesion during surgery.

P0517 Role of radiomics in predicting the degree of intestinal fibrosis in patients with Crohn’s Disease

Abstract Background During their lifetime, patients with Crohn’s disease (CD) frequently develop strictures as a complication, where various degrees of inflammation and fibrosis coexist. Distinguishing between inflammatory and fibrotic strictures is crucial for deciding on surgery or treatment changes, yet it is currently challenging in clinical practice. Cross-sectional imaging techniques (e.g. magnetic resonance imaging MRI) provide a reasonably accurate estimate of bowel wall fibrosis. However, only histopathological examination can determine its true extent. Radiomics uses artificial intelligence to analyze quantitative features from diagnostic images and can predict clinical outcomes. This study aims to assess the role of radiomics in classifying patients according to the degree (no/mild vs severe) of intestinal fibrosis in a cohort of CD patients who underwent surgery for strictures disease Methods We retrospectively selected 48 CD patients who underwent MR-enterography and subsequent surgery within 4 months between 2017-2023. An expert pathologist evaluated the degree of fibrosis in histopathological specimens from surgical samples using the Chiorean scoring system from 0 (no fibrosis) to 2 (severe fibrosis), and the degree of inflammation using Geboes score. For each MR-enterography, typical lesions of CD were searched by an expert radiologist on T2-weighted axial images generating a region of interest (ROI) segmentation for each lesion. We extracted 100 radiomic features from each ROI of the pre-processed MRI images using Pyradiomics. Feature selection included univariate analysis (Wilcoxon-Mann-Whitney test) and Spearmann correlation to exclude highly correlated features. A logistic regression model with stepwise regression was built with the selected features and evaluated by computing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Model’s sensitivity and specificity were calculated using the best cut-off according to the Youden index method. Results Among the 48 patients, 48% (n=23) showed severe fibrosis on the surgical specimen, whereas 52% (n=25) had either no or mild fibrosis. Two radiomic features were selected and resulted statistically significant in the fitted logistic regression model(p < 0.05): original_shape_Maximum2DDiameterColumn and original_firstorder_90Percentile. Results with their 95%CI were: AUC 0.79(0.64-0-90), sensitivity 0.69(0.50-0.75) and specificity 0.84(0.66-0.94). Bootstrap corrected (mean optimism) values of AUC and metrics were: AUC 0.77 (0.02), sensitivity 0.68(0.01), specificity 0.81(0.02). Conclusion Our study suggests that MRI-based radiomics modeling could help predict the degree of intestinal fibrosis. Future research will confirm these findings in a larger cohort

P0396 Combined clinical and histopathological risk stratification for prediction of (severe) endoscopic postoperative recurrence in patients with Crohn’s disease after ileocolic resection

Abstract Background The predictive value of histopathologic features of the intestinal resection specimen for the risk of Crohn’s disease (CD) recurrence after ileocolic (re-)resection (ICR) remains a matter of debate. Therefore, this study assessed the association of histopathological features in the resection specimen of CD patients with (severe) endoscopic postoperative recurrence (ePOR) in a large prospective, multicenter cohort study. Methods CD patients (≥16 years) scheduled for ICR were included from an ongoing prospective cohort study. Outcome measures were ePOR (modified Rutgeerts’ score ≥i2b) and severe ePOR (≥i3) at six months postoperatively. Proposed histopathological risk factors for postoperative recurrence (active inflammation, de novo granulomas, myenteric and submucosal plexitis, and transmural inflammation) were assessed in the ileal and colonic resection margins by expert pathologists blinded for outcomes. In addition, presence of chronic inflammatory cells, abceding inflammation, fat necrosis, fibrosis and de novo was assessed in the mesentery/mesocolon. Logistic regression was performed and ROC curves were delineated to explore the association and accuracy of histopathology with (severe) ePOR. Results In total, 293 patients were eligible for inclusion. Baseline characteristics are displayed in Table 1. Only moderate to severe inflammation at the ileal resection margin (OR 2.5; 95%CI 1.1-5.6) was associated with ePOR in multivariable analysis. Significant higher rates of ePOR were observed in patients with active inflammation at the ileal or colonic margin (p=0.01; p=0.03), submucosal plexitis at the ileal margin (p=0.04) and transmural inflammation (p<0.01). Transmural inflammation or submucosal plexitis at the ileal margin (p=0.02; p=0.01) and mesenteric granulomas (p<0.01) were significantly higher in patients with severe ePOR. Area under the curve (AUC) for individual histopathological risk factors varied between 0.53-0.58 for ePOR (active inflammation at the resection margins, submucosal plexitis and transmural inflammation) and 0.69-0.71 for severe ePOR (submucosal plexitis, transmural inflammation and mesenteric granulomas)(Figure 1). Clinical risk factors alone (active smoking and postoperative prophylactic medication) had an AUC of 0.66 and 0.74 for ePOR and severe ePOR. Combined histopathological and clinical risk stratification increased the AUC up to 0.71 for ePOR and up to 0.79 for severe ePOR. Conclusion Only moderate to severe inflammation at the ileal margin was independently associated with ePOR. A combined approach of clinical risk stratification and assessment of histopathological features in the resection specimen provides an adequate predictive value for (severe) ePOR after ICR in patients with CD.

Challenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions.

This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological interpretation and allocation decisions driven by biopsy findings. With the increasing use of artificial intelligence (AI) in digital pathology, it is timely to evaluate whether these advancements can overcome current challenges and improve organ allocation amidst a growing organ shortage. Significant inconsistencies exist in biopsy methodologies, including core versus wedge sampling, frozen versus paraffin-embedded processing, and variability in pathologist expertise. These differences complicate study comparisons and limit the reproducibility of histological assessments. Emerging AI-driven tools and digital pathology show potential for standardizing assessments, enhancing reproducibility, and reducing dependence on expert pathologists. However, few studies have validated their clinical utility or demonstrated their predictive performance for long-term outcomes. Novel AI-driven tools hold promise for improving the standardization and accuracy of preimplantation kidney biopsy assessments. However, their clinical application remains limited due to a lack of proven associations with posttransplant outcomes and insufficient evaluation of predictive performance metrics. Future research should prioritize longitudinal studies using large-scale datasets, rigorous validation, and comprehensive assessments of predictive performance for both short- and long-term outcomes to fully establish their clinical utility.

Impact of stain variation and color normalization for prognostic predictions in pathology

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is the variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early-stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study, we obtained a new series of histologic slides from the adjacent recuts of the same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUCcross-batch = 0.52 - 0.53 compared to AUCsame-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.

Discordance, accuracy and reproducibility study of pathologists’ diagnosis of melanoma and melanocytic tumors

Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata. Complete agreement is achieved in 53.5% of cases (424/792), and a majority vote ( ≥ five pathologists) in 90.9% (720/792). Considerable discordance is observed for non-invasive melanomas (complete agreement in only 10/73 cases). The expert panel disagrees with the local pathologists’ and dermatologists’ diagnoses in 14.9% and 33.5% of cases, respectively. This variability highlights the diagnostic challenges of early-stage melanomas and the need to reconsider how ground truth is established in routine care and AI research. Including at least two pathologists or virtual panels may contribute to more consistent diagnostic results.

Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation

Tumors of unknown origin (TUO) generally result in poor patient survival and are clinically difficult to address. Identification of the site of origin in TUO patients is paramount to their improved treatment and survival but is difficult to obtain with current methods. Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples. Our classifier achieves high accuracy in primary site identification when applied to both publicly available and internal validation samples, with 97% of samples classified correctly and 85% receiving high probability scores (≥0.9). Moreover, by employing pathologist expertise and t-SNE visualization, the TUO classifier can assign samples to 46 different sites of origin/disease classes. This strategy also revealed multiple classes of yet unknown significance for future exploration. Overall, the presented TUO classifier represents a significant step forward in the diagnosis of TUO tumors.

Diagnosing Helicobacter pylori using autoencoders and limited annotations through anomalous staining patterns in IHC whole slide images.

This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time-demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localize the pathogen in images, a limited number of annotations might be available in an initial setting. Our goal is to design an approach that, using a limited set of annotations, is capable of obtaining results good enough to be used as a support tool. We propose to use autoencoders to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image in HSV space. ROC analysis is used to set the optimal threshold of this measure and the percentage of positive patches in a sample that determines the presence of H. pylori. Our method has been tested on an own database of 245 whole slide images (WSI) having 117 cases without H. pylori and different density of the bacteria in the remaining ones. The database has 1211 annotated patches, with only 163 positive patches. This dataset of positive annotations was used to train a baseline thresholding and an SVM using the features of a pre-trained RedNet-18 and ViT models. A 10-fold cross-validation shows that our method has better performance with 91% accuracy, 86% sensitivity, 96% specificity and 0.97 AUC in the diagnosis of H. pylori . Unlike classification approaches, our shallow autoencoder with threshold adaptation for the detection of anomalous staining is able to achieve competitive results with a limited set of annotated data. This initial approach is good enough to be used as a guide for fast annotation of infected patches.

Treatment of Pleural Mesothelioma: ASCO Guideline Update.

To provide evidence-based recommendations to practicing physicians and others on the management of pleural mesothelioma (PM). ASCO convened an Expert Panel of medical oncology, thoracic surgery, radiation oncology, pathology, cancer genetics, and advocacy experts to conduct an updated literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2016 through 2024. Outcomes of interest included survival, disease-free or recurrence-free survival, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. The literature search identified 110 additional relevant studies to inform the evidence base for this guideline. Evidence-based recommendations were developed for surgical cytoreduction, immunotherapy, chemotherapy, pathology, and germline testing in patients with PM.Additional information is available at www.asco.org/thoracic-cancer-guidelines.

Tumor-selective dye-based histological electrophoresis enables intraoperative tumor diagnosis via tumor-specific enhancement.

Solid tissue biopsy is fundamental in guiding surgeons during intraoperative and peri-operative management of cancer patients. However, conventional histopathologic methods depend heavily on the expertise of trained pathologists, facing challenges in accuracy and efficiency. Methods: Here, we show that unbiased labeling of proteins within tissue sections using tumor-selective dyes enhances tumor-specific signals, enabling robust and accurate differentiation of tumors from normal tissues in less than 45 min. This diagnostic approach combines a tumor-selective dye labeling strategy and a three-dimensional (3D) histological electrophoresis separation strategy to visualize protein differences between tissues and exclude off-target interference. Results: We successfully diagnose and delineate malignant tissue from frozen and fresh surgical specimens from 34 patients across six types of cancer (mean AUC = 0.93). Furthermore, we apply this method to distinguish different histological characteristics in liver cancer surgical specimens, as well as identify and quantify the degree of inflammation in tumor-surrounding tissues. Conclusion: This rapid, accurate, unbiased, and marker-free approach may enhance intraoperative detection of multiple types of tumor specimens.

Korean Practice Guidelines for Gastric Cancer 2024: An Evidence-based, Multidisciplinary Approach (Update of 2022 Guideline).

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area. Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version. Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Cryptococcus neoformans/gattii and Histoplasma capsulatum var. capsulatum infections on tissue sections: Diagnostic pitfalls and relevance of an integrated histomolecular diagnosis.

Cryptococcus neoformans/gattii and Histoplasma capsulatum var. capsulatum may present atypical histopathological features inducing diagnostic errors. We aimed to estimate the frequency of these atypical features in formalin-fixed tissue (FT) samples and to assess the relevance of an integrated histomolecular diagnosis using specific H. capsulatum PCR and panfungal PCR followed by Sanger sequencing and/or targeted massive parallel sequencing (MPS). A total of 27 FT from 23 patients with a histopathological diagnosis of cryptococcosis (n=16 FT from 13 patients) or histoplasmosis (n=11 FT from 10 patients) were retrospectively included. All FT were consultation cases. Mycological identifications on equivalent fresh tissue were available for 11/23 (47.8%) patients. The expert pathologist review modified the diagnosis suggested by the initial pathologist in 7/27 (25.9%) FT. Fungal morphology and tissue inflammation were compared between both mycoses. The most discriminant atypical criterion was the presence of dented-looking yeasts, observed in 68.75% (11/16) of C. neoformans/gattii and none (0/11) of H. capsulatum var. capsulatum (P=.002). For the 12/23 (52.2%) patients without mycological identification on fresh tissue, an integrated histomolecular diagnosis on FT using specific PCR or panfungal PCR followed by Sanger sequencing and/or MPS led to fungal identification in 9/12 (75%) cases; for cryptococcosis, the targeted MPS sensitivity was higher than that of Sanger sequencing (P=.041). Thus, because atypical histopathological features may be tricky, integrated histomolecular diagnosis is essential for optimal patient care.

AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We introduce a benchmark RBC image dataset named Anemic RBC (AneRBC) to overcome this problem. This dataset is divided into two versions: AneRBC-I and AneRBC-II. AneRBC-I contains 1000 microscopic images, including 500 healthy and 500 anaemic images with 1224 × 960 pixel resolution, along with manually generated ground truth of each image. Each image contains approximately 1550 RBC elements, including normocytes, microcytes, macrocytes, elliptocytes, and target cells, resulting in a total of approximately 1 550 000 RBC elements. The dataset also includes each image's complete blood count and morphology reports to validate the CNN model results with clinical data. Under the supervision of a team of expert pathologists, the annotation, labeling, and ground truth for each image were generated. Due to the high resolution, each image was divided into 12 subimages with ground truth and incorporated into AneRBC-II. AneRBC-II comprises a total of 12 000 images, comprising 6000 original and 6000 anaemic RBC images. Four state-of-the-art CNN models were applied for segmentation and classification to validate the proposed dataset. Database URL: https://data.mendeley.com/preview/hms3sjzt7f?a=4d0ba42a-cc6f-4777-adc4-2552e80db22b.

Diagnostic interobserver variability of atypia assessment in columnar cell lesions among a group of expert breast pathologists in the United Kingdom and the Republic of Ireland, on behalf of the UK national coordinating committee for breast pathology.

Atypical ductal hyperplasia and flat epithelial atypia (FEA) have defined diagnostic criteria, yet there is variation in the interpretation of these criteria, particularly when the atypia is present in a background of columnar cell lesions (CCLs). This study focuses upon cases which are especially challenging or difficult to classify reproducibly according to existing criteria. Thirteen breast pathology experts were asked to classify 10 challenging cases with CLLs as atypical or non-atypical. Interobserver agreement was calculated. After two consensus meetings, which explored the morphological features underlying the decision, the cases were reassessed. Finally, a photomontage was compiled as a visual aid for practising pathologists representing a range of straightforward cases and others where subjective interpretation causes disagreement within current diagnostic criteria. Overall interobserver agreement and pairwise pathologist agreement coefficients were both in the fair range (κ = 0.22 and κ = 0.3-0.4, respectively). This improved to moderate or substantial agreement (κ = 0.6-0.8) after two consensus meetings. The most controversial cases were atypical cases that lacked the regular rounded nuclei of FEA, and non-atypical cases that had florid architectural changes bordering on architectural atypia. Among expert breast pathologists, interobserver agreement in the diagnosis of atypia in CCLs was higher in cases with classical features of FEA. Consensus was difficult to achieve if nuclear or architectural atypia fell outside the classical definition of FEA, suggesting that this category does not encompass the range of low-grade cytological atypia in CLLs. This study provides rationale for expanding the definition of atypia in CCLs other than FEA.

Computational Pathology-Enabled Residual Tumor Estimation Is a Prognostic Factor for Overall Survival in Anal Squamous Cell Carcinoma.

The incidence of anal squamous cell carcinoma (SCC) has increased, and treatment has shifted from surgery to chemoradiotherapy (CRT), with salvage abdominoperineal resection being reserved for persistent/recurrent cases. This study evaluates the utility of different tumor regression scoring systems in predicting survival in anal SCC patients, using pathologists' observations and digital pathology. Data of cases managed surgically from 2005 to 2019 were collected. Residual tumor was assessed by multiple methods (gross tumor size, largest focus of tumor on hematoxylin and eosin (H&E) slide, average of residual tumor in all submitted H&E slides, Japanese Esophageal Society, Chirieac, Schneider, Hermann, and College of American Pathologists scoring system). Three expert pathologists individually estimated ("eyeballed") the residual tumor percentage based on residual tumor/tumor bed (single representative H&E slide). The QuPath software was used to measure tumor volume on the same slide. The American Joint Committee on Cancer eighth staging and outcome data were retrieved from electronic medical records. The study involved 48 participants, predominantly female (56%), with a median age of 57 years. Most were Caucasian. Human papillomavirus-positive was present in 77% of those assessed (17/22). Initial treatment included CRT, followed by abdominoperineal resection (79%) or pelvic exenteration (21%). Complications (13%), persistent disease (33%), and recurrence (54%) led to surgical interventions. Fifty-one percent had moderately differentiated SCC, whereas 42% were poorly differentiated. Lymphovascular invasion (44%), perineural invasion (38%), and lymph node metastasis (13%) were present. Distant metastasis was rare (2%). Median overall survival was 3.2 years. Positive margins (hazard ratio, 4.12; 95% CI, 1.83-9.28) and larger tumor size (hazard ratio, 1.02; 95% CI, 1.01-1.03) were associated with an increased hazard of death. Most residual tumor measurement methods were not significantly associated with overall survival. Interobserver agreement (based on "eyeballing") was moderate (kappa, 0.4). Computational pathology-based residual tumor percentage was the only method significantly associated with outcome, with each 10% increase in the residual tumor percentage corresponding to a 1.23-fold higher hazard death (95% CI, 1.03; 1.46; P= .024). This study highlights computational pathology's important role in predicting outcomes in anal SCC treated with CRT and surgery. Specifically, the computational assessment of the residual tumor percentage proves to be a strong predictor of overall survival, outperforming other established tumor regression scoring systems methods.

Trends and Challenges of the Modern Pathology Laboratory for Biopharmaceutical Research Excellence.

Pathology, a fundamental discipline that bridges basic scientific discovery to the clinic, is integral to successful drug development. Intrinsically multimodal and multidimensional, anatomic pathology continues to be empowered by advancements in molecular and digital technologies enabling the spatial tissue detection of biomolecules such as genes, transcripts, and proteins. Over the past two decades, breakthroughs in spatial molecular biology technologies and advancements in automation and digitization of laboratory processes have enabled the implementation of higher throughput assays and the generation of extensive molecular data sets from tissue sections in biopharmaceutical research and development research units. It is our goal to provide readers with some rationale, advice, and ideas to help establish a modern molecular pathology laboratory to meet the emerging needs of biopharmaceutical research. This manuscript provides (1) a high-level overview of the current state and future vision for excellence in research pathology practice and (2) shared perspectives on how to optimally leverage the expertise of discovery, toxicologic, and translational pathologists to provide effective spatial, molecular, and digital pathology data to support modern drug discovery. It captures insights from the experiences, challenges, and solutions from pathology laboratories of various biopharmaceutical organizations, including their approaches to troubleshooting and adopting new technologies.

Diagnosis of fibrotic interstitial lung diseases based on the combination of label-free quantitative multiphoton fiber histology and machine learning.

Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional H&E staining primarily reveals cellular inflammation with limited detail on fibrosis. To address these issues, we introduce a pioneering label-free quantitative multiphoton fiber histology (MPFH) technique that delineates the intricate characteristics of collagen and elastin fibers for ILDs diagnosis. We acquired co-located multiphoton and H&E-stained images from a single tissue slice. Multiphoton imaging was performed on the deparaffinized section to obtain fibrotic tissue information, followed by H&E staining to capture cellular information. This approach was tested in a blinded diagnostic trial among 7 pathologists involving 14 relatively normal lung patients and 31 ILD patients (11 idiopathic pulmonary fibrosis (IPF) / usual interstitial pneumonia (UIP), 14 nonspecific interstitial pneumonia (NSIP), and 6 pleuroparenchymal fibroelastosis (PPFE)). A customized algorithm extracted quantitative fiber indicators from multiphoton images. These indicators, combined with clinical and radiological features, were used to develop an automatic multi-class ILDs classifier. Using MPFH, we can acquire high-quality, co-localized images of collagen fibers, elastin fibers, and cells. We found that the type, distribution, and degree of fibrotic proliferation can effectively distinguish between different subtypes. The blind study showed MPFH enhanced diagnostic consistency (kappa values from 0.56 to 0.72) and accuracy (from 73.0% to 82.5%, p=0.0090). The combination of quantitative fiber indicators effectively distinguished between different tissues, with areas under the receiver operating characteristic curves exceeding 0.92. The automatic classifier achieved 93.8% accuracy, closely paralleling the 92.2% accuracy of expert pathologists. The outcomes of our research underscore the transformative potential of MPFH in the field of f-ILD diagnostics. By integrating quantitative analysis of fiber characteristics with advanced machine learning algorithms, MPFH facilitates the automatic and accurate identification of various fibrotic disease subtypes, showcasing a significant leap forward in precision diagnostics.

Clinical significance of HER2 in urothelial carcinoma and analysis of its correlation with glycolytic metabolic characteristics.

This study aimed to explore the clinical relevance of Human Epidermal Growth Factor Receptor 2 (HER2) in urothelial carcinoma (UC) and its association with glycolytic metabolic markers, insulin resistance, and beta-cell function, shedding light on potential therapies targeting both HER2 pathways and cancer metabolism. In this retrospective analysis, 237 UC patients from the Departments of Urology and Pathology at Shandong Provincial Hospital were examined. From 1 January 2023, to 1 October 2024, patients underwent HER2 testing using immunohistochemistry (IHC). We investigated the relationships between HER2 expression and metabolic indicators such as the Homeostatic Model Assessment for insulin resistance (HOMA-IR), beta-cell function (HOMA-β), the triglyceride-glucose (TyG) index, and lactate dehydrogenase (LDH) levels. HER2 status was determined using a standardized scoring system from the 2021 Clinical Pathological Expert Consensus on HER2 Testing in UC, China. Statistical analysis followed CDC guidelines, using multivariate logistic regression to assess the independent impacts of HER2 on metabolic traits. Of the 237 evaluated UC samples, 87.76% exhibited positive HER2 expression. A significant correlation was found between positive HER2 status, advanced tumor stages, and increased LDH levels, suggesting a link between HER2 expression and heightened glycolytic activity. No significant relationships were observed between HER2 status and TyG levels, HOMA-IR, or HOMA-B. Subgroup analyses confirmed the consistency of the relationship between HER2 expression and LDH levels across different demographics and lifestyle factors. Our findings confirm the significant role of HER2 as a prognostic marker and therapeutic target in UC. The association of HER2 positivity with advanced tumor stages and high LDH levels underscores its complex involvement in disease progression. This study highlights the need to explore HER2's biological mechanisms further and pursue combined therapeutic strategies.