Search Results (186)

Renal cell carcinoma (RCC) comprises histologic subtypes with distinct prognosis and treatment implications. This single-cohort study evaluated slide-level weakly supervised subtype classification for clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) using 928 diagnostic H&E whole-slide images (WSIs) from 928 patients in TCGA-RCC. We propose Swin-CLAM, a controlled modification of CLAM in which the conventional CNN patch encoder is replaced by an ImageNet-pretrained Swin-Tiny Transformer, while the CLAM-SB bag-level aggregation module is kept unchanged. WSIs were segmented, tiled into non-overlapping $256\times 256$ patches at an effective $20\times$ magnification, encoded offline, and classified using slide-level labels only. In five-fold patient-level cross-validation on TCGA-RCC, Swin-CLAM achieved a macro-averaged AUC of $0.976\pm 0.008$, an accuracy of $94.8\pm 1.0\%$, and a macro-F1 of $0.940\pm 0.012$, with the largest gain observed for chRCC. Attention heatmaps and t-SNE plots were used as qualitative, exploratory analyses rather than formal evidence of interpretability. These results suggest that stronger patch-level representation can improve CLAM-based RCC subtype classification under a fixed MIL aggregator. However, the study does not establish clinical readiness, and external validation, calibration, domain-shift analysis, and expert region-level assessment are needed before practical deployment. Full article

Background: Buparlisib, combined with paclitaxel, improved survival in BERIL-1 trial patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). However, predictive biomarkers of benefit remain undefined. Objective: To evaluate whether spatial biomarkers extracted from hematoxylin and eosin (H&E) slides using artificial intelligence (AI) can predict overall survival benefit from buparlisib. Methods: Whole-slide H&E images from BERIL-1 trial patients were analyzed using a deep learning model trained to segment tissue compartments and classify cell phenotypes. Three predefined spatial features were evaluated: tumor-infiltrating lymphocyte density, tumor microenvironment heterogeneity, and granulocyte fraction in the tumor invasive margin. Cox proportional hazards model assessed biomarker-treatment interactions. Results: Of 158 trial participants, 144 had available slides. High tumor-infiltrating lymphocyte density (>10%) was associated with significantly improved overall survival with buparlisib versus placebo (HR, 0.25 (95% CI, 0.01–0.64; p = 0.002)), as were high tumor microenvironment heterogeneity (HR, 0.47 (95% CI, 0.27–0.80; p = 0.005)) and granulocyte enrichment in the tumor invasive margin (HR, 0.51 (95% CI, 0.30–0.88; p = 0.01)); within-arm proximity analysis showed higher granulocyte–tumor-cell proximity correlated with improved overall survival on buparlisib (HR, 0.32 (95% CI, 0.18–0.58; p < 0.001)). AI-derived spatial metrics outperformed CD3 immunohistochemistry. Among oropharyngeal tumors, HPV-positive cases were more frequent in patients with high tumor-infiltrating lymphocytes. Conclusions: AI-extracted spatial features from H&E slides were associated with overall survival benefit from buparlisib in R/M HNSCC. These scalable biomarkers support image-based patient selection strategies and are being prospectively evaluated in the BURAN phase 3 trial. Full article

Neoadjuvant therapy (NAT) is a standard component of breast cancer treatment, yet response rates vary substantially across patients. Accurate prediction of pathological complete response remains an unmet clinical need to improve patient selection for NAT. This review summarizes current approaches of using computer vision to predict breast cancer response to NAT from histopathological slides. We examined studies employing computer vision and machine learning models on hematoxylin and eosin and immunohistochemically stained whole-slide images, focusing on morphological features of tumor cells, stroma and tumor-infiltrating lymphocytes associated with pathological complete response. Key morphological predictors of therapy resistance included low tumor cell density with cord-like patterns, necrosis, predominance of collagenous and fibroblast-rich stroma and tumor vascularization, while therapy sensitivity was associated with high nuclear staining intensity, high tumor cell density and lymphocyte infiltration. We highlighted the advantages of incorporating multimodal data to enhance predictive performance. Our analysis demonstrates that computer vision models can detect subtle morphological patterns that may be difficult for pathologists to evaluate, providing valuable insights for personalized therapy planning in breast cancer. Further development of cross-modal, interpretable artificial intelligence solutions may improve prediction accuracy and deepen our understanding of tumor biology relevant to NAT response. Full article

Background: Accurate and efficient analysis of mitotic figures in whole-slide images (WSIs) is essential for tumor grading and prognosis. Methods: In this work, we present a three-stage pipeline for WSI-scale mitosis analysis that balances accuracy with clinical throughput: (1) a YOLOv11-based detector to propose mitosis candidates; (2) an ultra-lightweight classifier to refine detections and suppress false positives; and (3) a downstream classifier to distinguish atypical from normal mitoses for deeper biological insight. Results: In benchmark datasets, the two-stage detector improves F1 over detection-only baselines, while the atypical/normal module achieves strong accuracy, demonstrating cross-domain generalization. We further perform a proof-of-concept survival analysis on early-stage (I–II) cases from the TCGA-BRCA cohort, suggesting that mitosis-derived features may provide modest incremental prognostic information beyond the clinical baseline and nuclei features. Conclusions: Overall, the method delivers accurate detection, robust atypical mitosis classification, and high efficiency, processing gigapixel WSIs in minutes on a single GPU, positioning it for large-scale translational studies and future clinical workflow validation. Full article

Background: Nasopharyngeal carcinoma (NPC) presents significant diagnostic and therapeutic challenges, often due to late-stage detection and its complex anatomical location. The increasing integration of artificial intelligence (AI) into oncology offers potential opportunities to enhance the precision of NPC management. This systematic review aims to synthesise the current evidence of AI applications in NPC diagnosis, prognostication, and treatment planning. Methods: A systematic literature search was conducted following PRISMA guidelines across multiple databases (PubMed, Scopus, Embase, Google Scholar, IEEE Xplore) for studies published up to June 2025. From an initial pool of 2549 articles, 55 studies meeting the inclusion criteria were selected for qualitative analysis. The review focuses on AI models applied to key diagnostic modalities: computed tomography (CT), magnetic resonance imaging (MRI), and histopathological whole-slide images (WSI). Results: AI, particularly deep learning (DL), shows promising performance in automating critical tasks across all modalities. For CT and MRI, models have been reported to achieve accurate tumor and organ-at-risk segmentation, potentially supporting radiotherapy planning, and show strong performance in predicting survival outcomes and treatment toxicity. In digital pathology, AI enables automated diagnosis and facilitates the extraction of prognostic “pathomic” features from WSIs, with some studies suggesting performance comparable to or exceeding traditional radiomics. The most significant advances are seen in multimodal AI systems that integrate radiological, pathological, and clinical data, which, in some studies, show modest improvements in prognostic performance compared to single-modality approaches. However, these findings are preliminary, as none of the reviewed multimodal models underwent rigorous external validation in large, multi-center cohorts. Reported performance varies considerably across studies, and claims of superiority should be interpreted with caution. Full article

Background/Objectives: Multiple instance learning (MIL) is widely used for automated classification of epithelial ovarian cancer subtypes from whole slide images (WSIs), but the relative contributions of feature extractor, aggregation strategy, and classification framework (flat vs. hierarchical) choices remain unclear under severe class imbalance. Methods: We evaluated 36 configurations on 510 WSIs from the UBC-OCEAN dataset using stratified five-fold cross-validation, comparing three pathology foundation models (Phikon-v2, CTransPath, UNI), six aggregators (mean/max pooling, ABMIL, CLAM-SB, DSMIL, DTP-TransMIL), and two classification strategies. Pathologist-annotated WSIs assessed attention map interpretability. Results: Feature extractor selection contributed substantially more variance than aggregator choice. Cascade balanced accuracy ranged from 0.538 (Phikon-v2) to 0.925 (UNI); CTransPath (~32 K pretraining WSIs) reached 0.870, exceeding Phikon-v2 (~58 K WSIs) and approaching UNI (~100 K+ WSIs), indicating that pretraining objective and architecture contribute as substantially as scale. The hierarchical cascade consistently improved high-grade serous carcinoma (HGSC) recall across all six evaluated configurations (+0.073 to +0.530), detecting 206 of 217 cases (0.949) with UNI max pooling. Quantitative spatial alignment analysis confirmed that both stronger feature extractors—CTransPath and UNI—generated significantly more spatially structured attention distributions than Phikon-v2 (paired Wilcoxon, p = 0.008 and p = 0.032, respectively). Conclusions: Feature extractor choice contributed more variance than aggregator selection, with the largest gap between Phikon-v2 and stronger extractors. Hierarchical cascades consistently improved HGSC recall across all configurations. Full article

Synaptogenesis-related neuron–glioma interactions are increasingly recognized in glioma, yet it remains unclear whether routine H&E morphology can capture these programs and improve prognostic stratification. We integrated H&E whole-slide images, transcriptomes, and clinical data from 434 TCGA gliomas. Deep learning and quantitative pathology yielded an integrated histomorphologic feature set of 2678 features. Synaptogenesis-related activity was quantified using ssGSEA for ninety-eight synaptogenesis-related genes. In the training cohort, Spearman analysis identified 149 correlated histomorphologic features, which were refined to thirty-five by elastic net regularization. Seventeen prognostic candidates were entered into the MIME1 framework, and the most parsimonious model, Enet[0.1], retained fourteen non-zero-coefficient features to define the synaptogenesis-associated histomorphologic signature and construct the pathology-derived risk score (PRS). Multi-omic analyses, Human Protein Atlas validation, and single-nucleus RNA-seq were used to investigate the hub gene and its cellular context. PRS robustly stratified survival in both training and validation cohorts and remained an independent prognostic factor after adjustment for age and 2021 WHO CNS grade. High-risk tumors showed increased stromal and immune scores and enrichment of immune, adhesion, and phagosome-related pathways. EFNB2 emerged as the hub gene and was enriched in glioblastoma, and EFNB2-positive malignant cells displayed prominent communication with neurons, including EFNB2-EPHB1 signaling. Exploratory re-analysis of the myeloid compartment further showed that glioblastoma was enriched for suppressive TAM-like states relative to astrocytoma grade 2, supporting a shift toward a more tumor-associated and potentially immunosuppressive microenvironment. Routine H&E histomorphology can capture synaptogenesis-related molecular programs in glioma. The resulting PRS provides clinically relevant prognostic stratification, while EFNB2-positive malignant cells may represent a candidate hub for neuron–tumor communication within a remodeled tumor ecosystem. Full article

Background/Objectives: The selection of neoadjuvant therapy for patients with non-metastatic pancreatic adenocarcinoma remains challenging. Methods: We performed a single-institution, retrospective analysis of 79 patients who underwent resection of their pancreatic adenocarcinoma after receiving neoadjuvant therapy. Clinical and pathologic data were collected. Tumor fibrosis was quantified using Masson’s trichrome staining, tumor-infiltrating lymphocytes (TIL) were evaluated by an AI-based analysis of whole-slide H&E images, and immune cell populations were quantified by multiplex immunohistochemistry. Correlation analyses were performed between neoadjuvant treatment regimen, tumor regression, immune phenotypes, and survival. Results: All patients received chemotherapy, 77% FOLFIRINOX and 23% Gemcitabine/nab-paclitaxel (Abraxane). Eighteen percent of patients went on to receive radiation. Tumor regression grade (TRG) correlated with the neoadjuvant regimen. A reduction in tumor markers and the baseline neutrophil-to-lymphocyte ratio (NLR) correlated with overall survival. Among patients with an NLR > 3.3, FOLFIRINOX conferred a survival benefit over Gemcitabine/nab-paclitaxel, and radiation trended towards improved survival. Radiation was associated with increased fibrosis and reduced infiltration of CD8⁺ and regulatory T cells (Tregs). Increased Tregs and PDL1⁺ stromal cells were associated with poor response to neoadjuvant therapy, and NLR > 3.3 correlated with increased Treg infiltration. Conclusions: Our data suggest that patients with a high baseline NLR may benefit from intensified neoadjuvant therapy with FOLFIRINOX and radiation. Combination immunotherapy targeting Tregs and the PD1/PDL1 axis may further improve outcomes. Full article

Background: Accurate prognostic stratification in hepatocellular carcinoma (HCC) remains challenging due to substantial tumor heterogeneity. Deep learning (DL)-based approaches, particularly convolutional neural networks (CNNs), have been widely applied for prognostic prediction using histopathological images. Recently, pathology foundation models combined with multiple-instance learning (MIL) have emerged as a promising alternative. In this study, we aimed to provide a comparative evaluation of conventional CNN-based and foundation model-based approaches for HCC prognosis prediction, with a focus on tissue selection strategies and cross-dataset generalizability. Methods: We compared a patch-level CNN approach with a foundation model-based MIL approach for overall survival (OS) prediction from hematoxylin and eosin-stained whole-slide images. A total of 256 patients from Seoul St. Mary’s Hospital (SSMH) and 334 patients from the TCGA dataset were included. Models were trained using either all tissue regions or tumor-only regions under five-fold cross-validation. Model performance was evaluated using the concordance index (C-index), Kaplan–Meier analysis, and time-dependent receiver operating characteristic analysis. Cross-dataset validation and combined-dataset training assessed generalizability. Results: In the SSMH dataset, the CNN model performed better with tumor-only regions (C-index 0.8308) than with all tissue regions (0.7498). In contrast, the foundation model-based MIL approach showed stable performance regardless of input regions (C-indices: 0.8701 and 0.8752). Similar stability was observed in the TCGA dataset (0.7744 and 0.7722). Cross-dataset validation showed reduced performance, indicating limited generalizability. Combining datasets did not lead to performance improvement. Subgroup analyses showed prognostic information beyond histologic grade, and feature visualization revealed relevant histopathologic patterns. Conclusions: A foundation model-based MIL approach provides robust and interpretable prognostic modeling in HCC. DL-based prediction offers complementary information beyond conventional clinicopathological variables. Future efforts integrating multimodal data and improving generalizability will be essential for clinical translation. Full article

Background/Objectives: While human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is pivotal for breast cancer management, its reliance on additional tissue processing beyond routine H&E staining remains a clinical burden. Although virtual staining offers a potential solution, current methods often fail to explicitly account for HER2 score-specific expression patterns. To address this gap, we developed a score-aware framework designed for the precise generation of virtual HER2 IHC images. Methods: We introduce the non-contrastive multi-task (NCMT) framework, which integrates negative-free patch alignment, style–content constraints, and auxiliary HER2 score supervision for high-fidelity H&E-to-IHC translation. For rigorous evaluation, the model was validated on the BCI dataset, utilizing an official split of 3896 training and 977 independent test images derived from 51 whole-slide images. Results: NCMT demonstrated superior virtual staining performance, achieving a Fréchet Inception Distance (FID) of 38.8, a Kernel Inception Distance (KID) of 5.6, and an average Perceptual Hash Value (PHV) of 0.439. In downstream HER2 scoring tasks, while virtual IHC images alone yielded an accuracy of 83.01%, the fusion of H&E and virtual IHC further elevated performance to 97.85% accuracy and a 98.23% F1 score. These findings suggest that our framework effectively preserves diagnostic features while providing complementary information to H&E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&E and can serve as a complementary tool for HER2 assessment in digital pathology. Full article

Stain variability in digital pathology affects both cross-center diagnostic consistency and the robustness of downstream computational analysis. In this work, we formulate stain normalization as a variational inverse problem and derive a Screened Poisson Normalization (SPN) model from the steady-state reaction–diffusion mechanism underlying histological staining. In the CIE $L^{*}{a}^{*}{b}^{*}$ space, the model couples a gradient-domain fidelity term with a chromatic anchoring term, yielding a screened Poisson equation that preserves tissue morphology while enforcing color consistency. We prove that the corresponding variational problem is well-posed in $H^1\left(\mathsf{\Omega }\right)$ and stable with respect to perturbations of the input data. We further show that the screening term induces an intrinsic localization length ${𝓁}_c\sim {\lambda }_c^{-1/2}$, so that boundary perturbations decay exponentially away from tile interfaces. Based on this locality, we develop a non-overlapping tiled DCT-based spectral solver for gigapixel whole-slide images, enabling consistent tile-wise stain normalization and seamless whole-slide reassembly without heuristic boundary blending. Experiments on multi-scanner, multi-protocol, and archival-fading pathology datasets show that SPN achieves stable stain normalization with competitive chromatic alignment and strong preservation of diagnostically relevant microstructure, particularly in full-slide and tiled reconstruction settings. Supplementary experiments on synthetic pathology-like images further support the robustness of SPN under controlled color perturbations and indicate good generalization across diverse staining variations. Full article

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology. Full article

Artificial intelligence (AI) in cardiovascular pathology involves the use of computational models, including machine learning and deep learning (DL), to analyse complex and heterogeneous data. These data include histopathological whole-slide images, cardiovascular imaging techniques such as cardiac magnetic resonance, echocardiography, computed tomography (CT), clinical parameters, and molecular information. The integration of these multimodal data sources allows AI to overcome the limitations of single-modality analysis, improving diagnostic accuracy, prognostic stratification, and personalised clinical decision-making while reducing inter-observer variability. Cardiovascular disease remains the leading cause of mortality worldwide, highlighting the need for more precise and timely diagnostic tools. AI has shown significant promise, particularly in digital pathology, where the digitisation of histological slides combined with advanced algorithms enables improved diagnosis, prognostic assessment, and translational research. This review summarises current AI applications in cardiovascular pathology, focusing on heart transplant rejection, cardiomyopathies, myocarditis, and atherosclerotic and valvular diseases. Automated methods offer important advantages, including diagnostic standardisation, quantitative histological analysis, and improved reproducibility. However, several challenges remain, such as the need for large, well-annotated shared datasets, limited interpretability of AI models, and ethical and legal issues related to clinical implementation. AI represents a promising tool for advancing cardiovascular pathology and personalised medicine, although robust multicentre validation is required before routine clinical adoption. Full article

Cervical cytology screening remains dependent on manual analysis, which is time-consuming and subject to variability. This study proposes a leakage-free hybrid deep learning framework for multiclass classification of cervical cells extracted from whole-slide Papanicolaou images. A fine-tuned DenseNet121 feature extractor was combined with three classifiers: Support Vector Machine (SVM), Stacked Extreme Learning Machine (SELM), and Cascaded Deep Forest (CDF). Experiments were conducted on the CRIC Cervix Collection dataset using slide-level data partitioning and group-aware stratified 7-fold cross-validation. Model comparison followed a paired non-parametric protocol (Friedman test with Wilcoxon post hoc and Holm correction). DenseNet121 + CDF achieved the highest cross-validation Accuracy (0.7370 ± 0.0357), significantly outperforming SVM (0.6644 ± 0.0287) and SELM (0.6431 ± 0.0471) (χ²(2) = 11.14, p = 0.0038; Kendall’s W = 0.79). Independent testing showed competitive generalization across models. These results support the statistical robustness of the Cascaded Deep Forest-based hybrid architecture for multiclass cervical cytology classification under realistic slide-level conditions. Full article

Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. This study aimed to evaluate the reliability and performance of two artificial intelligence (AI)-based image analysis systems in Ki-67 index assessment and to compare their results with expert pathologist evaluation in pulmonary neuroendocrine tumors. Methods: A total of 63 pulmonary neuroendocrine neoplasm cases, including typical carcinoid (n = 29), atypical carcinoid (n = 13), and large cell neuroendocrine carcinoma (n = 21), were retrospectively analyzed. Ki-67 proliferation indices were independently assessed by four pathologists within predefined hotspot regions, counting approximately 2000 tumor cells per case. The same regions were analyzed using two AI-based image analysis systems (Roche uPath Ki-67 and Virasoft Virasight Ki-67). Interobserver agreement among pathologists was evaluated using the intraclass correlation coefficient (ICC), and concordance between manual and AI-based assessments was assessed using Spearman’s correlation and linear regression analyses. To account for potential scanner/platform effects, slides were digitized using two different whole-slide scanners (VENTANA DP^® 600 and Leica Aperio AT2), and color normalization and quality control procedures were applied prior to AI-based analysis. For clinical interpretability, Ki-67 indices were stratified into categorical groups based on tumor subtype-specific thresholds (0–<10%: low, 10–25%: intermediate, >25%: high), and agreement between manual and AI-based categorical scoring was evaluated using Cohen’s kappa coefficient. Results: Among the 63 pulmonary neuroendocrine neoplasm cases, Ki-67 proliferation indices varied across tumor subtypes, with typical carcinoids showing low, atypical carcinoids intermediate, and large cell neuroendocrine carcinomas high proliferative activity. Interobserver agreement among four pathologists was excellent (ICC = 0.998, 95% CI: 0.996–0.998). Strong correlations were observed between manual Ki-67 assessments and AI-derived indices, with Spearman correlation coefficients of 0.961 (95% CI: 0.918–0.982) for Roche AI and 0.904 (95% CI: 0.821–0.949) for Virasoft AI, and 0.926 (95% CI: 0.842–0.968) between the two AI systems. Bland–Altman analyses demonstrated minimal mean differences and most cases within the 95% limits of agreement, indicating high concordance without systematic bias. Categorical agreement analysis, using subtype-specific Ki-67 thresholds (0–<10%: low; 10–25%: intermediate; >25%: high), showed excellent concordance between manual and AI-based scoring (Cohen’s kappa 0.877 for Roche AI and 0.827 for Virasoft AI; p < 0.001), confirming the clinical interpretability and reproducibility of AI-based Ki-67 assessment. Conclusions: AI-based Ki-67 index assessment shows strong concordance with expert pathologist evaluation and reflects biologically relevant differences among pulmonary neuroendocrine neoplasm subtypes. These results suggest that AI-assisted Ki-67 analysis may serve as a reproducible and objective adjunct to routine diagnostic practice in pulmonary neuroendocrine tumors. Full article