Other Cancers

Performance of multimodal large language models for the detection and characterization of bone lesions on radiographs.

PURPOSE: Multimodal large language models (LLMs) offer emerging capabilities in medical image interpretation; however, their efficacy in orthopedic oncology remains unverified. This study aimed to evaluate and benchmark the performance of five contemporary LLMs-ChatGPT 5.2, Gemini 3 Flash, MedGemma 4B, Claude Sonnet 4.6, and DeepSeek-VL2-in detecting and characterizing bone lesions on plain radiog...

Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.

The vast combinatorial space of metal-organic frameworks (MOFs) has led to their widespread consideration across diverse application areas. That said, much remains unknown about what factors govern their thermodynamic stability. Herein, we use density functional theory to compute the formation energy and construct convex hull phase diagrams for 20,000+ MOFs and coordination polymers. Using the ene...

Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.

PURPOSE: Computational pathology has emerged as an attractive option for improving risk stratification in prostate cancer (PCa), but most approaches e...

Advances in Radiotherapy for Soft Tissue Sarcomas in 2025: A Review.

PURPOSE OF REVIEW: This review aims to synthesize and critically appraise the most impactful advances in radiotherapy (RT) for soft tissue sarcomas (S...

Identification of ribosomal stress related signature genes and immune microenvironment analysis in ovarian cancer based on multi-machine learning.

BACKGROUND: Ovarian cancer (OC) is a highly aggressive malignancy with poor prognosis and limited response to immunotherapy. Ribosomal stress, a cellu...

Pan-cancer Distant Metastasis Prediction Based on Graph Neural Network.

Distant metastasis (DM) is the primary driver of cancer-related mortality, and its clinical prediction remains challenging due to the lack of robust b...

Multimodal deep learning framework for recurrence risk stratification in soft tissue sarcoma: a multicenter study.

Accurate prediction of recurrence risk is essential to devise effective and personalized treatment strategies for patients with soft tissue sarcoma (S...

Comparative oncology of male and female breast cancer: diagnostic paradigms and machine learning approaches in treatment.

Breast cancer is associated mostly with women; however, breast cancer also appears in men, which dictates the need to know about gender-specific diffe...

Artificial Intelligence in Breast Cancer Lymph Node Evaluation: Is Neoadjuvant Chemotherapy the Achilles' Heel?

CONTEXT.—: Artificial intelligence (AI) has demonstrated high accuracy in detecting lymph node (LN) metastases in treatment-naïve invasive breast canc...

Single-cell and bulk omics uncover fibroblast heterogeneity and HSPH1 as a key driver in Barrett's esophagus to esophageal adenocarcinoma progression.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy with poor prognosis, often evolving from Barrett's esophagus (BE). Under...

Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.

The century-old vision of a "magic bullet" in oncology is being realized through the paradigm of precision theranostics, which formally integrates tar...

Artificial intelligence model predicts malignant transformation of oral leukoplakia and optimizes interventions.

PURPOSE: Oral leukoplakia (OL), the most common oral potentially malignant disorder (OPMD), poses a significant risk for transformation to oral squamo...

Single-cell profiling identifies a pro-tumoral VCAN positive macrophage subset and defines a prognostic signature in glioblastoma.

Glioblastoma (GBM), the most aggressive primary brain tumor, develops within a tumor microenvironment (TME) dominated by tumor-associated macrophages ...

Integrating machine learning and single-cell sequencing to reveal the role of kinase-related genes in subtype classification and prognostic significance of lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) exhibits poor prognosis and a highly complex tumor immune microenvironment (TIME), creating an urgent ...

Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.

Digital pathology involves the digitisation of histology slides, which is vital in modern healthcare, especially for cancer detection and diagnosis. S...

Machine learning and multi-dimensional transcriptomics reveal the key molecular network of benzo(a)pyrene/NNK in promoting laryngeal cancer and develop prognostic models.

Benzo(a)pyrene (BaP) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), as typical environmental carcinogens, are widely present in tobacco smo...

AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology.

Spatial transcriptomics (ST) assays are transforming our understanding of tumor heterogeneity, but their high cost limits their application in large-s...

Electrochemical Biosensors for Hepatocellular Carcinoma: Advances, Analytical Performance, and Clinical Translation.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide due to the limited sensitivity of current surveillance an...

Artificial intelligence-assisted confocal laser endomicroscopy for predicting invasion depth of superficial esophageal mucosal lesions: a cohort study.

INTRODUCTION: Accurate assessment of invasion depth in esophageal squamous cell carcinoma (ESCC) is essential for optimal treatment. Probe-based confo...

Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.

Pituitary neuroendocrine tumors (PitNETs) are common sellar neoplasms and represent a major component of routine pituitary pathology. In the 2022 Worl...