Other Cancers

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scR...

Era-specific prognostic factors and an interpretable machine-learning survival model for renal cell carcinoma brain metastases.

BACKGROUND: Brain metastases (BM) in renal cell carcinoma (RCC) are associated with poor prognosis and limited clinical guidance. We aimed to identify prognostic factors for overall survival (OS) in RCC BM and develop an interpretable machine-learning (ML) model for individualized risk prediction. PATIENTS AND METHODS: We retrospectively analyzed 929 patients with histologically confirmed clear ce...

Methodological Considerations on Central Pathology Review in Artificial Intelligence-Assisted Gastric Neoplasm Detection.

Reply to "Methodological Considerations on Central Pathology Review in Artificial Intelligence-Assisted Gastric Neoplasm Detection".

Explainable brain tumor detection in skull base CT using continuous neural representations: comparative evaluation of neural fields, neural operators, and vision transformers.

Reliable brain tumor detection in CT remains challenging due to low soft-tissue contrast, skull base complexity, and imaging artifacts. This study eva...

Real World Evaluation of AI-Based Tumor Cell Content Quantification for Molecular Tumor Profiling.

PURPOSE: Repeated evidence demonstrates limited reproducibility and accuracy of the visual quantification (VQ) of the tumor cell content (TCC) by clin...

Combined circulating tumor antigen model demonstrates additional prognostic value in first-line treatment of non-small cell lung cancer.

BACKGROUND: Circulating tumor antigens (ctA; tumor markers) are blood-based proteins that can offer prognostic value in non-small cell lung cancer (NS...

Fluorodeoxyglucose-PET in Melanoma and Soft-Tissue Sarcoma: Current Applications, Challenges, and Future Directions.

Fluorodeoxyglucose PET/computed tomography plays a central role in the management of melanoma and soft-tissue sarcoma by enabling comprehensive metabo...

Predictive biomarkers for immunotherapy response in renal cell carcinoma: why have they failed?

PURPOSE OF REVIEW: The heterogeneity of response to immunotherapies in renal cell carcinoma has created a strong need for predictive biomarkers to gui...

A primer on artificial intelligence for palliative care educators.

Artificial intelligence (AI) is rapidly being adopted in education in the health care professions, including in palliative care. Yet existing AI prime...

[Artificial intelligence in the care of geriatric patients-more of a curse or more of a blessing?].

BACKGROUND: Artificial intelligence (AI) is conquering medicine in many fields. With geriatric patients, it is important not only to understand the de...

Development and validation of an integrated machine learning model for recurrence-free survival prediction in clear cell renal cell carcinoma.

BACKGROUND: Integrated multimodal systems improve recurrence-free survival (RFS) prediction in surgically resected clear cell renal cell carcinoma (cc...

Artificial intelligence-assisted proteomic signatures for discriminating malignant from benign pulmonary nodules.

Lung cancer remains a major global health burden. Although low-dose CT (LDCT) is effective for early detection, its clinical application is limited by...

Rewiring immunometabolism in hepato-biliary-pancreatic cancers: Unraveling TME-mediated metabolic suppression and advancing next-generation immunotherapeutic approaches.

Hepato-biliary-pancreatic cancers, notorious for their pronounced heterogeneity and poor prognosis, continue to be a dominant factor in cancer-related...

Multi-modal Stacking Machine Learning Model for Predicting Lymph Node Metastasis in Cervical Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a multi-modal stacking machine learning model integrating intratumoral and peritumoral habitat radio...

[18F]FDG-PET in Lung Cancer: From Staging to Therapy Response.

Lung cancer, the most commonly diagnosed malignancy, is the leading cause of cancer-related mortality worldwide. Advancements in molecular imaging hav...

Uncertainty-Aware Risk Stratification in Pediatric Low-Grade Glioma Using Multimodal Data.

BACKGROUND AND PURPOSE: Risk stratification in pediatric low-grade glioma (pLGG) remains challenging due to biological and clinical heterogeneity. We ...

A hybrid ConvNeXt-ViT framework with differential evolution optimization for breast cancer classification.

Breast cancer, a leading cause of mortality among women worldwide, necessitates early detection through mammography. Yet, automated classification rem...

Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

The objective was to develop and validate an explainable artificial intelligence (AI)-based multimodal approach for preoperative risk stratification o...

Automated Body Composition from Computed Tomography Scans Improves Survival Prediction in Colorectal Cancer Patients.

Colorectal cancer is one of the most common cancers, but the current staging system is limited by the variability and paradoxical survival outcomes. B...