Oncology/Hematology

CTM-DETR-frequency-aware and statistically guided transformer for early infrared forest fire detection.

Early-stage infrared forest fire detection is severely hindered by strong background thermal interference and extremely weak fire radiation signals. Existing methods mainly rely on spatial-domain modeling and overlook the frequency-domain characteristics of flame thermal radiation, limiting robustness in complex environments. To address this challenge, we propose CTM-DETR, an end-to-end detection ...

Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer.

Molecular subtyping is essential for guiding systemic therapy in breast cancer but currently requires invasive biopsy. Conventional B-mode ultrasound offers rich anatomical information, yet lacks the functional dynamics needed to capture the comprehensive biology of tumors. Here, we present the first multimodal ultrasound spatiotemporal transformer, MUST-Sub, which integrates paired B-mode morphol...

Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer.

PURPOSE: The prognostic significance of tumor-infiltrating lymphocytes (TILs) in colorectal cancer (CRC) is well established; however, existing approa...

Multimodal deep learning model for multiclass classification of renal tumors.

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and vali...

Development and validation of a real-time AI model for differentiating benign and malignant gastric ulcers : a multicenter retrospective study.

AIM: To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during ...

Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.

BACKGROUND: Differentiating between spinal tuberculosis, pyogenic (bacterial) spondylitis and spinal metastasis remains a major diagnostic challenge b...

AI-driven integration of genomic and exposome data for cancer risk: the combined risk score (CRS).

Polygenic risk scores (PRS) have emerged as important tools for quantifying inherited susceptibility to cancer, and are increasingly combined with env...

CAD-SEL: A dual-modal colonoscopy dataset of subepithelial lesion.

Subepithelial lesions (SELs) of the gastrointestinal tract encompass a heterogeneous spectrum of histology, ranging from benign to malignant. Their de...

Automated counting of prostate cell types with image processing and machine learning.

Traditional cell counting in clinical and research settings often relies on hemocytometry, a manual technique that is labor-intensive and prone to hum...

Practical guidance for echocardiography for cancer therapy-related cardiac dysfunction: 2026 focused update.

BACKGROUND: Cancer therapy-related cardiac dysfunction (CTRCD) has become an important clinical issue with advances in cancer treatment and improved p...

AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.

INTRODUCTION: Hematoxylin & Eosin (H&E) stained slides are the gold standard for cancer diagnosis but are subject to labor-intensive review and inter-...

Comprehensive Multi-Omics Profiling of Tertiary Lymphoid Structures Reveals Immunogenetic Landscapes and Prognostic Subtypes in Lung Adenocarcinoma.

Tertiary lymphoid structures (TLSs) are key components of the tumor immune microenvironment and show prognostic relevance in many cancers. However, th...

No modality left behind: Adapting to missing modalities via knowledge distillation for brain tumor segmentation.

Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its abili...

Engineered artificial antigen-presenting cells for tumor immunotherapy: Design strategies, clinical applications, and translational challenges.

Immunotherapy has long played a pivotal role in cancer treatment, and antigen-presenting cell (APC)-based immunotherapy represents a promising strateg...

Deep learning approaches for predicting Ki-67 index in breast cancer histopathology images: A systematic review.

Accurately calculating the Ki-67 index, a critical biomarker for cellular proliferation, is pivotal in breast cancer (BC) treatment personalization. V...

From Genomic Instability to Epigenetic Signatures: The Evolving Landscape of Circulating Cell-free DNA in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a leading global health burden, yet its diagnosis and staging rely heavily...

Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.

BACKGROUND: Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain. PURPOSE: ...

Breast ultrasound images for segmentation and classification using multi-task U-Net.

Breast ultrasound imaging is widely used for the early detection of breast cancer due to its accessibility and effectiveness, particularly in dense br...

Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.

Identifying robust biomarkers for early cancer detection remains challenging, particularly when working with limited or heterogeneous datasets. Here, ...

Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.

PURPOSE: High-risk stage II colorectal cancer (CRC) shows heterogeneous outcomes despite adjuvant chemotherapy. We developed and validated an interpre...