Journal of Clinical Pathology

Despite significant advancements in diagnostic methodologies, the fundamental approach to venous blood collection has remained largely unchanged since its widespread adoption in the mid-20th century. This stagnation poses considerable challenges, particularly in scenarios involving difficult venous access (DVA). The Bloody-Easy (BE) device represents a novel, passive, low-volume blood collection system engineered to optimize phlebotomy outcomes, especially in these challenging clinical contexts. Our prospective, randomized, crossover study involving 90 healthy volunteers demonstrates that BE achieved comparable or superior sample quality while significantly reducing the volume of blood drawn per session. Furthermore, the device garnered substantial positive feedback from both patients and clinicians. BE offers a potentially cost-neutral and low-risk solution for improving blood collection efficiency and patient experience in critical care, emergency medicine, and paediatric settings, where conventional phlebotomy techniques frequently encounter limitations.

BackgroundPlacental growth and function are imperative for healthy fetal growth; data on placentas can inform research and clinical care. Measuring placental size after delivery should be easy, but current methods are hard to standardize and error prone. We developed PlacentaVision using artificial intelligence (AI)-based models, to automatically, accurately, and precisely measure placentas from digital photographs. ObjectiveWe aimed to compare placental disc morphology between gross pathology examination (human measurements) and our automated PlacentaVision model (AI measurements). MethodsPlacentaVision is a multi-site study to assess placental morphology, features, and pathologies from digital photographs. We built a large dataset of digital placenta photographs and clinical data from singleton births at three large hospitals: Northwestern Memorial (Chicago; n=24,933), UPMC Magee-Womens (Pittsburgh; n=1198) and Mbarara Regional Referral (Uganda, n=1715). Data and images were from the medical record for Northwestern, part of a biobank study for Magee, and from our prospective studies for Mbarara. We compared long and short disc axis length (defined by Amsterdam criteria) between human and AI-based PlacentaVision measurements by calculating the difference and using Bland-Altman; we stratified by site, disc shape, infant sex, and term/preterm birth. ResultsMean (SD) disc length was 19.2 (3.1) and 18.6 (3.1) cm from PlacentaVision and human measurement, respectively, with a difference of 0.57 (2.19) cm. Disc width was 16.3 (2.3) cm and 16.1 (2.4) cm from PlacentaVision and human measurement, respectively, with a difference of 0.25 (1.85) cm. Bland-Altman limits of agreement were -3.7 to 4.9 cm for length and -3.4 to 3.9 cm for width. Irregularly-shaped placentas had a greater difference between PlacentaVision and human measurements compared to those with round/oval shapes (length differences of 1.53 and 0.45 cm respectively). Further, there were length differences by site (Northwestern 0.6, Magee 0.0, and Mbarara 0.4) and gestational age at birth (preterm 0.71, term 0.53 cm), but similar results for male and female placentas. Results for width were similar to length. ConclusionsAI-based measurements were less than a cm from human measurements overall. Our findings of larger differences for irregular shapes and preterm may indicate it is difficult for humans to measure irregular or small placentas according to protocol. PlacentaVision can automate and standardize the process.

ObjectiveTo explore the application value of dual-staining for specific AT sequence binding protein 2 (SATB2) immunohistochemistry and elastic lamina in detecting elastic lamina invasion (ELI) in pT3 colon cancer, and to assess its association with clinicopathological characteristics, staging, and prognosis. MethodsThis retrospective cohort study enrolled 176 pT3 colon cancer patients who underwent radical resection at Affiliated Jinhua Hospital Zhejiang University School of Medicine. The deepest tumor-infiltrated paraffin blocks were collected for SATB2 immunohistochemistry and elastin dual-staining. Correlations between ELI status and clinicopathological characteristics and prognosis were analyzed. Survival data of 74 pT4a stage patients were collected for comparative analysis. ResultsELI (+) was positively associated with high tumor budding grade, vascular invasion, lymph node metastasis, and reduced tumor infiltrating lymphocytes (TILs) (all P < 0.001). No correlations were observed with age, gender, tumor location, histological subtype, tumor grade, or perineural invasion (all P > 0.05). The ELI (+) group exhibited significantly shorter disease-free survival (DFS) and overall survival (OS) compared to ELI (-) group (P < 0.05). Additionally, the ELI (+) group demonstrated inferior OS than the pT4a group, though DFS did not differ significantly. ConclusionDual-staining of SATB2 immunohistochemistry and elastic lamina provides a reproducible and objective method for assessing ELI. ELI correlates with key clinicopathological features and functions as an independent adverse prognostic indicator in pT3 colon cancer.

BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related death in sub-Saharan Africa (SSA). Differentiating primary HCC from metastatic liver tumors remains a significant diagnostic challenge. Understanding the prevalence and clinical predictors of HCC is crucial for improving diagnosis and patient care. This study examined the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV), and HCC, and clinical predictors of HCC. MethodsWe used immunohistochemical markers on archived liver tumor biopsies and analyzed the data using descriptive and logistic regression analysis. ResultsAmong 58 liver carcinoma cases, 37.9% had HCC, and 62% had metastatic liver carcinoma (MLC). HCC was most common (61.5%) among middle-aged adults (50-59 years). HCC was more frequent in males (47.2%) than in females (22.7%). Over half of the patients (51.7%) tested positive for HBV. HCC was more prevalent in HBV-positive patients than HBV-negative ones (43.3% vs 32.1%). Hepatic fibrosis was identified in 27.6% of cases. HCC was more common in patients with fibrosis (56.2%) than in those without (31%). HCV infection was rare (6.9%) in this study. In multivariable logistic regression analysis, none of the examined predictors reached statistical significance (P>0.05). Patients aged 50-59 years, males, those with HBV infection, and hepatic fibrosis showed higher odds of HCC. Hepatocyte Paraffin-1 (Hep Par-1) demonstrated 97% specificity and a 95% positive predictive value (PPV) for differentiating HCC from MLC. The combined marker pattern of Hep Par-1 positive and AE1/AE3 negative was highly predictive of HCC (100% specificity, 100% PPV, and 93.2% diagnostic accuracy). ConclusionsOur findings indicate that while the assessed risk factors tend to show directional association with HCC, as expected, larger studies are needed to determine their independent effects. The combined Hep Par-1 AE1/AE3 immunophenotype is more accurate than either marker alone. Therefore, this combined test is a valuable diagnostic tool for confirming HCC in resource-limited settings.

BackgroundIn 2004, South Africas public health system faced the dual challenge of rapidly scaling up antiretroviral therapy (ART) while reducing the cost of laboratory monitoring. At the time, conventional CD4 testing methods were expensive, labour-intensive, and impractical for sustaining a national testing network. This study aimed to assess the financial impact and cost savings associated with the implementation of the PanLeucogated CD4 (PLG/CD4) enumeration method between 2004 and 2024 in the public-sector in South Africa. MethodsA longitudinal cost analysis was conducted using annual test volumes and state tariffs for PLG/CD4 testing and the 4-colour CD3/CD4/CD8/CD45 T-cell enumeration reference method. Annual cost savings were calculated in United States Dollars (USD) by applying historical South African Rands (ZAR) to United States Dollars (USD) exchange rates. The state prices for tariff codes PLG/CD4 and the reference method were provided by calendar year in ZAR and converted to USD based on the prevailing exchange rate. The USD test prices were multiplied by annual test volumes. Cost savings were calculated by multiplying annual test volumes and the difference in test prices in USD (difference between PLG/CD4 and the reference method). ResultsThere were 50,745,848 PLG/CD4 tests performed over 20-years. The cost-per-test of PLG/CD4 was consistently lower than the reference method, ranging from $4,06 to $9,40, compared to $13,06 to $28,21. Cumulative national savings amounted to USD 626 million. The peak annual savings of $64,6 million occurred in 2011, coinciding with the height of ART enrolment. Cost savings persisted despite a doubling in the exchange rate over the study period. ConclusionThe PLG/CD4 implementation enabled cost-efficient, scalable, quality-assured CD4 testing as part of the national HIV response, reducing reliance on complex/costly technologies while improving coverage. These findings support the critical role of context-specific diagnostic innovation to strengthen health system resilience.

BackgroundFamilial dysbetalipoproteinemia (FDB) is a genetic lipoprotein disorder that can develop in patients homozygous for the APOE2 genotype ({varepsilon}2/{varepsilon}2). It is associated with decreased clearance of remnant lipoproteins and increased atherosclerotic cardiovascular disease (ASCVD) risk disproportionate to their level of LDL-C. A goal of this study was to develop a screening test for the {varepsilon}2/{varepsilon}2 genotype based on routinely available lipid tests and to determine those at most risk for ASCVD. MethodsAfter assembly of a primary prevention cohort from the UK Biobank (n= 269,895), gene array and exome data was utilized to classify patients as being {varepsilon}2/{varepsilon}2 genotype positive or negative. Lipid profiles and APOB levels were extracted and the number of ASCVD events was tabulated during a 15-year follow-up period. ResultsUsing a newly developed equation for estimating APOB (eAPOB) with lipid panel test results, the ratio of measured APOB to eAPOB was better than any other individual lipid test or ratio for identifying patients with the {varepsilon}2/{varepsilon}2 genotype (AUC: APOB/eAPOB: 0.990 (0.986-0.994), nonHDL-C/APOB: 0.961 (0.952-0.970), APOB: 0.955 (0.949-0.961), VLDL/TG: 0.788 (0.771-0.804)). The majority of {varepsilon}2/{varepsilon}2 patients could be identified with the APOB/eAPOB ratio even before they expressed the FDB phenotype with elevated TG and nonHDL-C. The PCE or PREVENT risk equations were the most accurate method for identifying higher risk patients (AUC: PREVENT: 0.690 (0.637-0.742), PCE: 0.697 (0.645-0.749)). ConclusionThe APOB/eAPOB ratio can be used to accurately identify the {varepsilon}2/{varepsilon}2 genotype and conventional risk equations are the best method for determining those at risk for ASCVD.

BackgroundHistologic descriptors such as lymphovascular invasion (LVI), visceral pleural invasion (VPI), spread through air spaces (STAS), and grading system have each been associated with adverse outcomes in lung adenocarcinoma (LUAD). However, with the exception of VPI, these features are not formally incorporated into the TNM staging system. We evaluated the prognostic value and incremental contribution of these histologic descriptors within the framework of the 9th edition TNM staging system. MethodsIn total, 1,745 individuals diagnosed with stage I-III invasive non-mucinous lung adenocarcinoma (NM-LUAD) were included in this study, comprising 1139 French-Canadian patients who underwent surgical resection at IUCPQ-Universite Laval (discovery cohort) and 606 patients from the National Cancer Center Hospital in Tokyo, Japan (validation cohort). The objective of this study was to assess the prognostic contribution of histologic descriptors, including STAS, and LVI, as complements to conventional 9th edition TNM staging. ResultsGrade 3 tumors, LVI, and STAS were identified in 880 (50.4%), 809 (46.4%), and 775 (44.4%) of 1745 cases, respectively. Histologic grade and LVI demonstrated the strongest associations, particularly in early-stage disease, while STAS exhibited a stage-dependent effect, being more impactful in stages II-III. VPI showed less consistent prognostic value. Incorporating these histologic descriptors into TNM staging improved prognostic model performance, with the largest gains driven by histologic grade and LVI, while STAS provided additional, complementary prognostic refinement. ConclusionThese findings demonstrate that key histologic descriptors--including grading system, LVI, and STAS--represent robust and reproducible prognostic parameters. Importantly, these descriptors provide complementary, stage-dependent information that may enhance risk stratification and inform refinement of future TNM staging frameworks, including the forthcoming 10th edition.

IntroductionKidney biopsy reports contain rich information that is clinically actionable and useful for research. However, the narrative format hinders scalable reuse. We here investigated whether open-source large language models (LLMs) can extract relevant, standardized readouts from native kidney biopsy pathology reports. MethodsGerman free-text native kidney biopsy reports were parsed with three open-source LLMs (Llama3 70B, Llama3 8B, MedGemma) to generate structured JSON outputs covering relevant report elements (e.g., diagnosis, glomerular counts, histopathological patterns). Two independent observers manually curated the same report elements; disagreements between the two were resolved by an experienced nephropathologist to create the final ground truth. Performance was assessed using strict and soft matching and summarized accuracy. Inter-rated agreement was quantified using Cohens and Lights Kappa with 95% confidence intervals via 1000-times bootstrapping. ResultsLlama3 70B achieved the highest overall accuracy (93.3% strict, 97.1% soft), followed by MedGemma. These larger models showed near perfect performance for explicit and discrete variables and positivity of immunohistochemistry markers, while accuracy decreased for report elements requiring interpretation (e.g., primary diagnosis, interstitial inflammation in fibrosis vs. non-fibrotic cortex). Human raters showed strong agreement for the primary diagnosis ({kappa} = 0.74, 95% CI 0.64-0.84). Adding Llama3 70B or MedGemma as a third rater increased overall agreement (0.82, 95% CI 0.74-0.89 and 0.78, 95% CI 0.69-0.85, respectively), whereas Llama3 8B reduced it. ConclusionsOpen-source LLMs can accurately transform narrative nephropathology reports into a structured and machine-readable format, potentially supporting scalable retrospective cohort building. While some report elements can be extracted without supervision, interpretation-dependent elements should be supervised by a human observer. Lay SummaryRetrospective data collection from nephropathology reports is essential for building informative cohorts in computational nephropathology research, yet manual processing of narrative reports is time-consuming and limits scalability. In this study, we demonstrate that open-source large language models can reliably extract key diagnostic, quantitative, and descriptive data elements from kidney biopsy reports with high accuracy. While factual and clearly stated report elements can be extracted automatically, findings that require contextual or interpretative judgment still benefit from expert supervision. Overall, this approach substantially reduces manual effort and enables efficient generation of structured datasets from diagnostic routine, facilitating the development of kidney registries and future computational nephropathology research. In addition, such systems could be implemented into the routine diagnostic workflow, to directly transform narrative reports into structured data.

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic clinical features, such as autoantibodies, and require prompt steroid treatment to prevent progression to liver failure. Liver biopsy currently remains the gold standard to differentiate acute DILI from AIH; however, general pathologists face significant diagnostic challenges due to overlapping histopathological features. This study integrates pathology expertise with deep learning-based artificial intelligence (AI) to differentiate DILI from AIH using histopathological images. Our AI model demonstrates promising classification accuracy (Accuracy 74%, AUC 0.81). This paper presents a detailed pathological analysis alongside AI methods, discusses the current model performance and limitations, and proposes directions for future improvements.

Background and AimsArtificial intelligence (AI) is increasingly applied to histological assessment in inflammatory bowel disease (IBD), but most approaches quantify features in isolation and ignore their anatomical location within the mucosa. We developed and validated PAIR-IBD (Perspectum AI Reading in IBD), an AI system that quantifies inflammatory cell populations, crypt injury, and epithelial damage within defined mucosal compartments to distinguish active disease, remission, and equivocal cases in ulcerative colitis (UC). MethodsA deep learning ensemble was trained on three IBD biopsy datasets to identify lymphocytes, neutrophils, eosinophils, and plasma cells, and to segment crypts, lamina propria (LP), and muscularis mucosae. Inflammatory cell densities and crypt injury metrics (mucin depletion, solidity, roughness, branching, and abscess formation) were quantified. PAIR-IBD outputs were compared between histologically active and remissive UC, evaluated in inconclusive cases, and correlated with manual pathology grading. ResultsNeutrophil density increased 3.5-fold in the LP and 15-fold within crypts in active UC (p<0.0001). Eosinophil density doubled and LP lymphocytes increased 1.4-fold. Active UC showed increased mucin depletion, crypt branching, and crypt abscesses, with reduced crypt solidity (p<0.0001 for all). PAIR-IBD metrics correlated with manual inflammatory and crypt injury scores (rs=0.23-0.72) and global indices (rs=0.27-0.65). Up to 89% of inconclusive cases aligned with remission-like profiles based on multiple independent AI metrics. ConclusionPAIR-IBD provides spatially resolved, quantitative assessment of inflammation and epithelial injury in UC, improving disease stratification and resolution of equivocal histology, with potential to support scoring consensus and improve accuracy of histological endpoints in clinical trials.

POLE sequencing for somatic mutations (POLEmut) guides adjuvant therapy in endometrial cancer (EC), but cost and infrastructural considerations lead to limited uptake. Omission of POLE testing leads to unnecessary exposure to radiotherapy and/or chemotherapy. We developed POLARIX, a multiple instance deep learning model with attention pooling, which predicts POLE mutation status from routine hematoxylin and eosin whole-slide images (WSIs). Trained on 2,238 cases from eleven EC cohorts, POLARIX showed clinical-grade discrimination across three external cohorts (Pooled: AUC=0.95, 95% CI: 0.91-0.98; n=68/481 POLEmut/POLEwt). Attention maps highlight POLE morphologies. Clinical applicability is demonstrated using predefined thresholds based on three resource scenarios. The most sensitive threshold ("Low") yields a test reduction of 77% (73%-81%) (sensitivity: 93% (85%-99%), specificity: 89% (87%-92%)). POLARIX is an interpretable and cost-efficient approach to reduce POLE testing in women with endometrial cancer, broadening access to precision oncology.

T-cell lymphomas are often histologically indistinguishable from benign T-cell infiltrates. Clonality testing is frequently required for diagnosis. It lacks the spatial context and is slow and expensive, relying on complex, multiplexed PCR reactions, interpreted by experienced scientists or pathologists. We previously published details of a pair of highly specific monoclonal antibodies against the two alternatively used, but very similar, T-cell receptor {beta} constant regions, TCR{beta}1 and TCR{beta}2. We demonstrated the feasibility of immunohistochemical detection of TCR{beta}1 and TCR{beta}2 in formalin-fixed, paraffin-embedded (FFPE) tissue as a novel diagnostic strategy for T-cell lymphomas. Here we validate an improved pairing of TCR{beta}1/2 rabbit monoclonal antibodies, and demonstrate their utility for single and double immunostaining, including with a chimeric mouse anti-TCR{beta}2 antibody. Finally, we show that this staining is amenable to automated cell counting, permitting accurate calculation of the TCR{beta}2:TCR{beta}1 ratio.

ObjectivesTo develop and evaluate JADE, a proof-of-concept retrieval-augmented generation (RAG) diagnostic assistive system was designed to enhance large language model (LLM) reasoning for the assessment of jawbone lesions. This study examined whether integrating structured retrieval with GPT-5 improves diagnostic accuracy and stability compared with standalone LLMs. MethodsJADE was developed as a cloud-based application integrating GPT-5 with a curated oral radiology and pathology database using a hybrid semantic-keyword retrieval strategy. Clinical and radiographic characteristics were imported as a structured query to guide retrieval and support diagnostic reasoning. Performance was compared with standalone GPT-5, Claude Sonnet 4.5, DeepSeek-R1, and Gemini 2.5 Flash across 25 cases. Accuracy was analysed using Cochrans Q test with post-hoc McNemars tests and Bonferroni correction. Intra-model stability was measured using the majority agreement ratio, and response time was recorded to assess real-time usability. ResultsJADE showed the highest diagnostic performance, correctly identifying 20 out of 25 cases and outperforming all standalone LLMs. Significant differences were observed across models (Cochrans Q = 33.2, df = 4, p < 0.001), with post-hoc analyses confirming that JADE significantly outperformed GPT-5, Gemini 2.5 Flash, and Claude Sonnet 4.5 (p < 0.01). JADE also exhibited the greatest run-to-run stability (mean MAR = 0.90 {+/-} 0.18). The average prediction time of 6 {+/-} 0.5 seconds supported its feasibility for real-time clinical use. ConclusionsJADE improved diagnostic accuracy and stability over standalone LLMs, underscoring the value of RAG reasoning in jawbone lesion assessment and its potential for real-time clinical use.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer spanning a biologic continuum from atypical ductal hyperplasia (ADH) to high-grade lesions with variable risk of progression to invasive ductal carcinoma (IDC), yet diagnostic accuracy remains limited when based on morphologic assessment via hematoxylin and eosin (H&E) alone. TRPV4, a mechanosensitive ion channel we previously demonstrated to exhibit pathology-dependent spatial distribution patterns in DCIS, offers a biologically motivated immunohistochemical (IHC) marker that may refine classification beyond routine H&E assessment. We evaluated whether deep learning models trained on TRPV4 IHC outperform those trained on H&E for DCIS classification. We assembled a multi-institutional dataset of paired H&E and TRPV4 IHC whole-slide images from 108 patients (24,248 image tiles), with both stains available for most cases in an internal development cohort (n=69) and an external test cohort (n=39). Each cohort was digitized on different scanners to assess cross-platform robustness. Tiles from annotated regions were grouped into four ordered classes reflecting DCIS progression: normal/benign, ADH/low-grade DCIS, high-grade DCIS, and IDC. Xception and EfficientNet-B0 convolutional neural networks were trained with patient-level 3-fold cross-validation on the development cohort and evaluated as ensembles on the test cohort. On external testing at the patient level, H&E-based ensembles showed moderate performance (macro-F1=0.43-0.44, macro-AUC=0.73-0.80), whereas TRPV4 IHC-based models substantially improved classification (macro-F1=0.68-0.72, macro-AUC=0.91-0.92). Across tile-level predictions, 68-79% of errors were between adjacent grades, consistent with an ordinal DCIS spectrum. Per-class tile-level analyses on the external test cohort showed the greatest improvement with TRPV4 IHC over H&E for ADH/low-grade DCIS (AUC 0.83-0.84 vs 0.70-0.81) and IDC (AUC 0.74-0.79 vs 0.65-0.66), supporting classification across the DCIS progression spectrum. These findings support TRPV4 IHC as a mechanistically grounded complement to H&E, improving deep learning-based DCIS classification in a pilot multi-institutional setting.

Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.

ObjectiveBrain branks preserve extensive material relevant to neurodegenerative disease research. As these collections age, tissue becomes archival, raising the question of whether long-term fixed and stored human brain tissue remains suitable for contemporary immunohistochemical analyses. Materials and MethodsForty-one autopsy brains collected between 1946 to 1980 were examined. For each case, midbrain and hippocampus were available both as original paraffin-embedded blocks and as tissue stored long term in fixative. New paraffin blocks were prepared from the long-term fixated tissue. Sections from original and newly prepared blocks were immunohistochemically stained for -synuclein, hyperphosphorylated tau and amyloid-{beta}. Immunoreactivity was assessed using semi-quantitative scoring. ResultsOriginal blocks consistently showed good staining intensity and morphological preservation for each protein pathology. Newly prepared blocks showed slightly lower semi-quantitative scores for Lewy-related pathology, without statistically significant differences, except for astrocytic -synuclein in the substantia nigra in cases from the 1960s. Tau pathology displayed modestly reduced labelling, particularly of the neuropil threads and neurofibrillary tangles, most evident in cases from the 1950s. Amyloid-{beta}-positive senile plaques showed similar or slightly higher scores in newly prepared blocks, with no significant differences across regions. ConclusionHuman brain tissue preserved as paraffin-embedded blocks or stored in fixative for up to 78 years remains suitable for immunohistochemical analyses. Adequate-to-good detection of aggregated of -synuclein, hyperphosphorylated tau and amyloid-{beta} is achievable, indicating preserved pathological hallmarks of Lewy Body Disease and Alzheimers Disease in archival tissue.

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

Stromal tumor-infiltrating lymphocytes (sTILs) are promising biomarkers for predicting therapeutic outcomes in triple-negative breast cancer (TNBC), with higher sTIL levels correlating with improved chemotherapy response and survival outcomes. Currently, sTILs are manually evaluated by pathologists, which is prone to inter-reader variability. In this study, we have developed an AI-driven TIL segmentation pipeline to process entire diagnostic hematoxylin-and-eosin-stained whole slide images for reproducible scoring (global TILseg scoring) and reliable prognostication. This pipeline was optimized and tested using two independent TNBC patient cohorts (n = 57 in the discovery cohort, n = 43 in the validation cohort) with clinical outcomes and follow-up data. The global scores generated by TILseg showed moderate to high concordance with expert scoring (Spearman R = 0.84-0.89) and improved patient stratification (p-value = 0.0191) as compared to manual scoring (p-value = 0.0663). Additionally, we investigate how the spatial localization of sTILs (spatial TILseg) impact survival outcomes by identifying TILs in selected stromal subsets (0.02-2 mm from the epithelial clusters). Our findings have shown that TILs up to 50 {micro}m from epithelial regions prove to be most prognostic in predicting recurrence-free survival post-neoadjuvant chemotherapy with higher statistical significance than both manual and global TILseg scoring. Further, spatial TILseg scoring was more significantly associated with pathological complete response status in both patient cohorts. In summary, we present an AI-based digital tool for robust sTIL scoring and spatial mapping to enhance its potential as both a diagnostic and prognostic biomarker, particularly in TNBC patients. SIGNIFICANCEAn automated and spatially resolved AI tool for sTILs scoring enhances patient risk stratification based on both response to treatment and recurrence-free survival, establishing its relevance as an independent prognostic marker.

BackgroundPlacenta accreta spectrum (PAS) is characterized by abnormal trophoblastic invasion into the uterine myometrium and is a cause of postpartum hemorrhage and maternal death. Protease-activated receptor-1 (PAR-1) promotes various cellular actions, including invasion. Here, we analyzed the expression of PAR-1, platelet antigen, and fibrin in PAS. MethodsWe analyzed 49 PAS cases (placenta accreta vera [accreta vera], 31 cases; placenta increta [increta], 8 cases; placenta percreta [percreta], 10 cases, classified by the degree of placental villous invasion) and 33 control cases. We immunohistochemically examined the expression of PAR-1, platelet glycoprotein (GP) IIb/IIIa, and fibrin. ResultsThe frequency of previous cesarean section was higher in the increta and percreta groups than in the control and accreta vera groups. PAR-1 expression in placental villi was weak and limited in extent in control cases, whereas immunoreactivity and staining density increased in increta and percreta. Immunofluorescence revealed PAR-1 expression in cytotrophoblasts of placental villi and in aggregated platelets. PAR-1 expression scores in cytotrophoblasts increased significantly with the degree of villous invasion (accreta vera, increta, percreta) compared with controls. The immunopositive areas for GPIIb/IIIa and fibrin were significantly larger in PAS groups than in controls. Furthermore, the immunopositive areas for platelets and fibrin were positively correlated with the PAR-1 expression score. ConclusionThese results indicate that PAR-1 may play a role in placental villous invasion and that a thrombogenic placental environment may influence PAR-1 activation.