Modern Pathology

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.

Myeloproliferative neoplasms (MPNs) are characterized by progressive myelofibrosis that drives morbidity and mortality. Liquid biopsy approaches to noninvasively monitor fibrotic progression remain limited. We performed comparative transcriptomic profiling of CD45-depleted platelet-enriched and CD45+ leukocyte-enriched fractions from matched peripheral blood samples of 76 individuals (27 primary myelofibrosis, 17 polycythemia vera, 14 essential thrombocythemia, 18 healthy controls). Platelet RNA sequencing was performed in 2018-2020 on Illumina HiSeq 4000, while WBC RNA sequencing was conducted in 2023 on Illumina NovaSeq 6000 from cryopreserved CD45+ enriched fractions of specimens obtained at the identical time and from the same blood sample as the platelet RNA. Despite comparable library preparation protocols and higher sequencing depth in WBC samples, platelet transcriptomes exhibited 5.1-fold more differential expression in myelofibrosis (3,453 versus 681 genes, adjusted p<0.05, |log2FC|>1). Platelet signatures were enriched for proteostasis pathways including endoplasmic reticulum stress and unfolded protein response, reflecting megakaryocyte dysfunction in the fibrotic bone marrow niche. WBC signatures predominantly featured immune activation and proliferative pathways, indicating systemic inflammatory responses. Multinomial LASSO classification demonstrated superior performance of platelet-based models for myelofibrosis diagnosis (AUROC 0.85) compared to WBC-based (AUROC 0.77) or clinical models (AUROC 0.59). Combined platelet+WBC models did not improve performance (AUROC 0.80), indicating complementary but non-additive information. These findings establish platelet transcriptomic profiling as a superior noninvasive biomarker platform for monitoring myelofibrosis in MPNs, capturing megakaryocyte-driven fibrogenesis with greater sensitivity than peripheral leukocyte-based approaches. HighlightsUsing matched WBC and platelet RNA-seq from MPN patients, we identify myelofibrosis-associated transcriptomic signatures specifically enriched in platelets. Multinomial LASSO modeling highlights platelet-derived gene expression as a dominant and predictive biomarker of myelofibrosis, outperforming clinical parameters and WBC signatures. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/714941v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1d695aborg.highwire.dtl.DTLVardef@fc250forg.highwire.dtl.DTLVardef@1e52e8eorg.highwire.dtl.DTLVardef@15378e3_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mismatch repair-deficient (dMMR) endometrial tumors are often responsive to immune checkpoint inhibitors (ICI), yet recurrence and variable treatment responses remain significant clinical challenges. Characterization of the tumor microenvironment, including immune cell composition and spatial organization, may reveal predictors of recurrence and ICI responsiveness. We performed multiplex immunofluorescence imaging on 16 dMMR endometrial tumors using a 36-antibody panel. Cells were segmented, phenotyped by unsupervised clustering, and analyzed to quantify cell type proportions and spatial relationships among intratumoral, peritumoral, and whole-tissue cell populations across clinical groups. Non-recurrent tumors (n = 10) exhibited higher intratumoral CD8 T-cell proportions, tumor cell enrichment around CD8+ T cells, CD8+/CD4+ ratios, and PD-1Low CD4 T-cell proportions. In contrast, recurrent tumors (n = 6) showed higher CD4+ T cell proportions and endothelial cell enrichment surrounding CD8 and PD-1 CD8 T cells. Among the recurrent tumors, compared to non-responders (n = 2), ICI responders (n = 4) had a higher proportion of PD-1+Ki67+ CD8+ T cells. Macrophage spatial organization also differed; non-responders displayed separate clusters of CD163 macrophages and CD163- macrophages, whereas responders demonstrated more dispersed macrophages co-localized with PD-1+ CD8 T cells. Overall, these findings suggest that both immune cell composition and spatial arrangement are factors that contribute to recurrence and ICI response in dMMR endometrial cancer. Spatial profiling of the tumor microenvironment may provide biomarkers to guide patient stratification and precision immunotherapy strategies.

Pathology faces persistent challenges including a global shortage of specialists, uneven access to expertise, increasing diagnostic complexity, and a growing need for second-opinion consultations. While digital and telepathology platforms address parts of this problem, existing solutions often trade accessibility for structured, workflow-aware clinical integration. At the same time, multimodal medical AI shows promise for diagnostic support but raises concerns regarding transparency, automation bias, and clinical accountability. We present PaiX Net, a structured, AI-augmented second-opinion platform designed to support collaborative pathology consultation while preserving human decision ownership. The platform integrates standardized case templates, moderated expert discussion, and human-centered AI assistance within a scalable, browser-based architecture compliant with data protection requirements. AI support is embedded at defined workflow stages to assist with case structuring, summarization, and exploratory interpretation, while diagnostic conclusions remain under expert control. To mitigate automation bias, AI-generated content is visually separated, collapsed by default, and presented only after independent expert input. PaiX Net incorporates a multimodal medical AI model (MedGemma-4B), selected for its open availability and computational efficiency, and fine-tuned on curated, anonymized consultation cases. An illustrative retrospective evaluation demonstrates substantial reductions in case preparation time and modest but consistent improvements in diagnostically relevant summaries. PaiX Net illustrates how structured, human-centered AI integration can enhance access to expert second opinions while maintaining clinical accountability and supporting continuous human-AI learning in digital pathology.

Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of-origin classification can accelerate quality control and enable early detection of mislabeled specimens. We developed a deep learning model that classifies the organ of origin from H&E-stained slides using a single low-resolution thumbnail per slide in under one second. For training, we used thumbnails from 16,624 slides from the TCGA and CPTAC archives, which contain mostly primary tumor resections. The images were categorized into 14 classes based on the most common primary sites in TCGA: Bladder, Brain, Breast, Colorectal, Kidney, Liver, Lung, Pancreas, Prostate, Skin, Stomach, Thyroid gland, Uterus, and Other (encompassing the remaining tissue types). We evaluated our approach on two independent external cohorts: a 5-class cohort with 2,857 slides (Colorectal, Kidney, Liver, Pancreas, Prostate) and a comprehensive 14-class cohort (12,348 slides). The model achieved 90% balanced accuracy for the 5-class cohort and 62% for the full 14-class cohort. Notably, when considering only the predictions with high confidence, 53% of the large cohort could be classified with 74% balanced accuracy. Manual review of high-confidence misclassifications suggested that some may reflect errors in the ground truth rather than model error. Mean model inference time was 0.2s per slide on an NVIDIA L4 GPU. Our deep learning approach demonstrates high classification performance with very low inference time, indicating its potential for real-time and cost-effective quality control in digital pathology.

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.

BO_SCPLOWACKGROUNDC_SCPLOWThere is increasing momentum behind the clinical implementation of AI-based software for image analysis in digital pathology. As regulations, standards, and national approaches to the clinical use of AI continue to develop, the marketplace of AI products is expanding and evolving - presenting pathologists with a multitude of devices that offer the potential to improve pathology services. MO_SCPLOWETHODSC_SCPLOWTo maintain pace with this changing AI device landscape, we conducted a comprehensive search for, and analysis of, commercial AI products for image analysis in digital pathology. This included CE-marked and Research Use Only (RUO) products using images with histological stains (e.g., H&E) or immunohistochemical (IHC) labelling. Product information and published clinical validation studies were assessed, to understand the quality of supporting evidence on available products, and product details were compiled into a public register: https://osf.io/gb84r/overview. RO_SCPLOWESULTSC_SCPLOWIn total, we identified and assessed 90 CE-marked and 227 RUO AI products. We found that AI products for cancer detection in prostate and breast pathology comprised a substantial portion of the marketplace for H&E image analysis, while IHC products were almost exclusively for use in breast cancer. Clinical validation studies on these products have steadily increased; however, we found that published studies were only available for just over half of H&E products and just over a quarter of IHC products. For CE-marked products, the dataset quality and diversity for AI model performance validation was highly variable, and particularly limited for IHC products. Furthermore, only a limited number of products included studies that assessed measures of clinical utility. CO_SCPLOWONCLUSIONC_SCPLOWAs clinical deployment of AI products for image analysis in histopathology grows, there is a need for transparency, rigorous validation, and clear evidence supporting clinical utility and cost-effectiveness. Independent scrutiny of the expanding offering of AI products provides insight into the opportunities and shortcomings in this domain.

Vision-language models (VLMs) provide a unified framework for multimodal reasoning, yet their representations are primarily learned from natural image-text corpora and often exhibit semantic misalignment when transferred to histopathology, particularly under data-limited diagnostic settings. To address this limitation, we propose HistoSB-Net, a semantic bridging network designed to adapt pre-trained VLMs to multimodal histopathological diagnosis while preserving their original semantic structure. HistoSB-Net introduces a constrained semantic bridging (CSB) module that operates within the self-attention projection space of both vision and text encoders. Instead of employing explicit cross-attention or full fine-tuning, CSB adaptively modulates pre-trained attention projections through a lightweight nonlinear semantic bottleneck, enabling structured cross-modal regulation with limited additional parameters. The framework supports both patch-level and whole-slide image (WSI)-level diagnosis within a unified architecture. Experiments on six pathology benchmarks, comprising two WSI-level and four patch-level datasets, demonstrate consistent improvements over zero-shot inference across 36 backbone-dataset combinations under limited supervision. Further analysis of prototype-based margin distributions and confusion matrices shows that these improvements are accompanied by enhanced intra-class compactness and increased inter-class separation in the embedding space. These results indicate that CSB provides an effective and computationally manageable strategy for adapting pre-trained VLMs to data-limited digital pathology tasks.

The Updated Sydney System (USS) provides a standardized framework for grading gastritis and stratifying gastric cancer risk. However, subjective observer variability and labor-intensive workflows impede its routine clinical use. To address these challenges, we developed SydneyMTL, a multi-task deep learning framework that uses Multiple Instance Learning (MIL) with task-specific attention pooling to predict severity grades across all five USS attributes simultaneously. Trained on an unprecedented cohort of 50,765 whole-slide images (WSIs), SydneyMTL generates interpretable histologic evidence for clinical practice. In retrospective evaluations against 24 board-certified pathologists, the model achieved an overall mean lenient accuracy of 89.1%, with 22 pathologists exhibiting >80% agreement with the model. When evaluated on an expert-adjudicated "Golden dataset," the models performance improved to 90.2%, demonstrating its capacity to align with multi-expert consensus and filter individual annotator noise. Latent space analysis confirmed that SydneyMTL captures the ordinal structure of the USS, by representing disease severity as a continuous biological spectrum rather than as disjoint categories. Finally, a randomized crossover reader study showed that AI-assisted review significantly reduced interpretation time and improved inter-observer agreement, establishing SydneyMTL as a scalable tool for supporting standardized gastric cancer risk stratification. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=154 HEIGHT=200 SRC="FIGDIR/small/26346304v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@bf83adorg.highwire.dtl.DTLVardef@15dff59org.highwire.dtl.DTLVardef@274874org.highwire.dtl.DTLVardef@105f759_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISydneyMTL is the first unified framework to simultaneously predict the full 4-tier severity grades across all five Updated Sydney System attributes. C_LIO_LITrained on a massive cohort of 50,765 whole slide images, the model aligns with multi-expert consensus on a rigorous "Golden dataset". C_LIO_LIAI assistance significantly reduces pathologist reading time and harmonizes inter-observer variability in real-world clinical workflows. C_LIO_LILatent space analysis confirms that SydneyMTL preserves the biological ordinality of disease severity without explicit ordinal constraints. C_LI The bigger pictureGastritis is among the most frequent diagnoses in gastrointestinal pathology, and its histologic severity is central to gastric cancer prevention. In routine practice, pathologists convert subtle mucosal changes into semi-quantitative, ordinal grades using the Updated Sydney System, which evaluates five co-existing histologic dimensions. While this framework provides a shared language, grading is labor intensive and inherently dependent on reader-specific thresholds, creating variability that affects risk stratification and surveillance. A key concept motivating our study is that gastritis is not defined by a single finding but by multiple criteria that co-occur and interact. This suggests that computational models should learn these criteria jointly - capturing their biological correlations and the continuum of severity - rather than treating each grade as an isolated classification task. SydneyMTL implements this perspective through a unified multi-task, weakly supervised approach that learns directly from a massive cohort of 50,765 routine whole-slide images. Beyond diagnostic accuracy, our work reveals that the model preserves the ordinality of severity in its representation space, supporting the biological view that discrete clinical categories approximate an underlying continuous biological spectrum. Its attention-based explanations also connect model outputs to interpretable tissue evidence, enhancing clinical trust. Crucially, by harmonizing inter-observer variability, SydneyMTL provides a more reliable foundation for gastric cancer risk assessment, ensuring that premalignant changes are captured with greater consistency. More broadly, our findings reposition AI for gastritis from narrow detection toward scalable, evidence-based decision support that can standardize grading practices and reduce cognitive burden on the global pathology workforce.

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.

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.

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.

BackgroundTumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. MethodsWe leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. ResultsBinary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). ConclusionCross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

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.

Diffuse large B-cell lymphoma (DLBCL) represents 30-40% of non-Hodgkin lymphoma cases and is curable in >60% of patients; however, approximately one-third ultimately relapse. Although prior studies in normal B cells and lymphoma models implicate p73 in B-cell lymphomagenesis, the functional role of individual p73 isoforms in DLBCL remains poorly defined. TP73, a TP53 family member located on chromosome 1p36, encodes both transcriptionally active (TAp73) and dominant-negative ({Delta}Np73) isoforms that differentially regulate apoptosis and proliferation. In this study, we characterized p73 locus alterations, isoform-specific expression patterns, and their biological relevance in DLBCL. Chromosomal analysis revealed disruption of the 1p36 locus--predominantly via heterozygous deletion--in 35% of patient samples, which significantly correlated with elevated {Delta}Np73 expression. Immunohistochemical profiling demonstrated a positive association between TAp73 and cleaved caspase-3, and between {Delta}Np73 and Ki-67. Conversely, TAp73 expression negatively correlated with the anti-apoptotic proteins Bcl-2 and Bcl-6. Functional studies in DLBCL cell lines further confirmed that TAp73 enhances sensitivity to serum deprivation and doxorubicin, whereas {Delta}Np73 overexpression promotes survival and chemoresistance. Together, these findings identify p73 isoform imbalance as a key contributor to DLBCL pathogenesis and therapeutic response, highlighting {Delta}Np73 as a potential biomarker of aggressive disease and treatment resistance, and TAp73 as a tumor-suppressive axis warranting further investigation. SummaryDiffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma, yet relapse remains a major challenge. The p73 gene produces two key isoforms with opposing functions: TAp73, which promotes apoptosis, and {Delta}Np73, which inhibits cell death and supports tumor growth. In DLBCL samples, 1p36 chromosomal disruption occurred in 35% of cases and was associated with elevated {Delta}Np73. TAp73 expression correlated with apoptosis markers, whereas {Delta}Np73 correlated with proliferation. Functional studies showed TAp73 sensitizes DLBCL cells to stress and chemotherapy, while {Delta}Np73 enhances resistance. These findings highlight {Delta}Np73 as a potential biomarker and therapeutic target in DLBCL.

Prostate cancer (PCa) is the second most common cancer and cause of cancer death in American men. Existing risk prediction methods have limited accuracy and reproducibility, resulting in difficulty in predicting disease severity. We demonstrate the development and external validation of an automated multimodal artificial intelligence algorithm using biparametric MRI (bpMRI) and clinical covariates for predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in PCa patients. Development cohort included 80% of patients from center 1 (n = 240) who underwent prostate MRI prior to RP between January 2008 and December 2018 with a minimum of two years of follow-up after RP. Test cohort included the remaining 20% of center 1 patients (n = 71), and the external validation cohort from center 2 (n = 168). Center 2 patients included those who underwent prostate MRI and RP between January 2015 and December 2024 with a minimum of two years of follow-up. Clinical comparisons were CAPRA-S (center 1) and ISUP grade group from post-RP biopsy (center 2). Models developed were a clinical model (M0), an automated clinical model (M1), a radiomics model (M2), and a multimodal model (M3). Clinical variables (M0) included PSA, age, primary Gleason, and ISUP grade group. Automated clinical variables (M1 and M3) included PSA and age. Radiomics features (M2 and M3) were extracted from bpMRI using a lesion detection algorithm. Accuracy, sensitivity, specificity, and AUC were calculated, and log-rank tests compared BCR-free survival to assess the models ability to discriminate relative to clinical standards. Intermediate-risk groups were also assessed. The multimodal model (M3) had the highest AUC across test sets (combined: 0.71; center 1: 0.70; center 2: 0.75) and was the only model to significantly differentiate BCR-free survival outcomes in intermediate-risk groups across both centers (p < 0.05). This automated multimodal model leveraging radiomics and clinical covariates can predict BCR after RP, approaches clinical gold standards, and may enhance imaging-based prognostication following further validation.

Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as "black boxes", lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method on a deep learning model trained for ovarian tumor histologic subtype prediction. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the models predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Non-destructive 3D pathology methods have emerged in recent years with the potential to enhance standard 2D histopathology by greatly increasing the amount of tissue sampled by imaging and by providing volumetric morphological context. Another key advantage is that tissues remain intact, allowing re-embedding after imaging for potential long-term storage and future histological or molecular analyses. However, the impact of 3D pathology protocols on biomolecules -- including DNA, RNA, and proteins -- and their compatibility with downstream assays, has not been systematically evaluated. Here, we applied a previously optimized 3D pathology protocol -- involving deparaffinization, fluorescent H&E-analog staining, optical clearing, and open-top light-sheet microscopy -- to formalin-fixed paraffin-embedded (FFPE) specimens of breast, prostate, and head and neck cancer. Following the protocol, tissues were re-embedded in paraffin and compared with paired FFPE controls that did not undergo 3D pathology processing. DNA and RNA were extracted and subjected to quality assessments. Amplifiability was tested by PCR and reverse transcription quantitative PCR (RT-qPCR) of housekeeping genes. Although the results showed a slight decrease in the average yield and increased fragmentation of both DNA and RNA, amplifiability was largely preserved. Sanger sequencing of the PCR products confirmed accurate sequence determinations, while total RNA sequencing indicated that the global transcriptomic profile was largely unchanged. IHC staining of common biomarkers produced comparable signals, suggesting those proteins are well preserved after the 3D pathology workflow. These results demonstrate the feasibility of combining 3D pathology with downstream molecular applications.

Secretory breast carcinoma (SBC) is typically indolent, yet mechanisms underlying aggressiveness and therapeutic resistance to tropomyosin receptor kinase inhibitors (TRKi) remain unclear. Autopsy-based longitudinal multi-organ high-dimensional profiling of metastatic TRKi-resistant SBC demonstrated histopathological heterogeneity, including secretory and squamous components, arising from a shared clonal origin. Integrated genomic and transcriptomic analyses revealed hierarchical transcriptional rewiring consistent with a lineage-plastic state, suggesting a potential link to tumor aggressiveness and therapeutic resistance.

There have been no systematic evaluations of purely spectral models for digital pathology tasks. We implemented and benchmarked four pipelines: binary classification on the BreaKHis dataset, multi-class region classification in glioblastoma, spatial transcriptomics, and denoising on Visium 10x. Across all tasks, extensive cross-validation and grouped splits showed that purely spectral models did not improve performance over CNN-only baselines, but offer useful complementary tools for interpretability and processing. Denoising showed strong performance that proves utility in data-scarce or heterogeneous image environments. Equivalence testing confirms that spectral and CNN model performances fall outside {+/-}3% AUC. Fusion models between CNNs and spectral models show higher balanced accuracy. Spectral models failed to generalize across spatial transcriptomics tasks, with low correlation despite stable training loss. These findings represent a systematic negative result: despite their theoretical richness, spectral geometric features and SNO embeddings prove to be complementary features for WSI classification or segmentation. Reporting such outcomes is essential to establish empirical boundaries for spectral methods and to encourage future work on conditions or data modalities where these approaches may hold greater promise.