Other Cancers

Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.

Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deployment remains limited by tumor heterogeneity, dataset bias, and incomplete tumor-specific validation. This systematic review synthesizes developments from 2016 to 2025 in advanced DL-based MRI brain tumor classification, with a specific emphasis on m...

MRI Radiomics for Preoperative Microvascular Invasion Stratification in Hepatocellular Carcinoma: Comparative Analysis of Intratumoral, Peritumoral, and Combined Intratumoral-Peritumoral Approaches-A Systematic Review and Meta-analysis.

Microvascular invasion (MVI) represents a critical prognostic determinant in hepatocellular carcinoma (HCC), yet preoperative prediction remains challenging. MRI-based radiomics offers promise for noninvasive MVI stratification through quantitative feature extraction from intratumoral and peritumoral regions, with growing interest in integrating both compartments to capture tumor-host interactions...

Scleral image analysis via artificial intelligence to evaluate inflammatory status in inflammatory bowel disease.

PURPOSE: Among the extraintestinal ocular manifestations of patients with inflammatory bowel disease (IBD), episcleritis is associated with the inflam...

Pilot feasibility study of deep learning-based detection of malignant liver lesions in intraoperative ultrasound imaging.

BACKGROUND: Intraoperative ultrasound plays a central role in hepatobiliary surgery for the detection of liver lesions, but image interpretation is ch...

MRI radiomics combined with clinical factors to predict vessels encapsulating tumor clusters and early recurrence in hepatocellular carcinoma (≤ 3 cm).

OBJECTIVES: This study aims to develop a predictive model that combines MRI radiomics features with clinical factors to anticipate the preoperative pr...

ALDH18A1 silencing inhibits lung adenocarcinoma progression through mitochondrial dysfunction and PPAR signaling pathway activation.

BACKGROUND: Lung adenocarcinoma (LUAD) remains a prevalent malignant tumor characterized by a dismal prognosis. This study aimed to reveal potential m...

Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.

BACKGROUND: Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Alt...

Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.

OBJECTIVE: This review systematically evaluates the current research landscape, methodological characteristics, and translational challenges of artifi...

Dismantling the TGF-β Axis: A Critical Transition from Occupancy-based Kinase Inhibitors to Event-driven Degraders.

The TGF-β signaling pathway plays a dual role in cancer, acting as both a tumor suppressor and a potent driver of metastasis and immune evasion. Tradi...

Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.

BACKGROUND AND PURPOSE: While intravesical Bacillus Calmette-Guérin Vaccine (BCG) instillation remains standard adjuvant therapy for high-grade non-mu...

Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.

BACKGROUND: KRAS-mutant lung adenocarcinoma (LUAD) is associated with aggressive phenotypes and therapy resistance, which highlights an urgent need to...

Precision lung cancer classification using convolutional neural networks with enhanced image pre-processing and model optimization.

Among various types of cancers, lung cancer causes the highest mortality globally, necessitating prompt diagnosis for effective treatment. Traditional...

Multiscale systems modelling of communication networks in brain metastasis.

Brain metastasis remains a major clinical challenge because intracranial progression and therapeutic resistance arise not only from tumour-intrinsic p...

AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.

BACKGROUND: Neoantigens-tumor-specific peptides generated by somatic mutations-are central targets of effective anticancer T cell immunity and underpi...

Artificial intelligence-detected HER2 strong-positive tumor proportion predicts FISH positivity and treatment response in breast cancer.

Human epidermal growth factor receptor 2 (HER2)-targeted therapies have revolutionized breast cancer treatment, necessitating standardized HER2 testin...

Machine Learning-Assisted SERS Quantification of Sialylated Alpha-Fetoprotein: From Single-Cell Analysis to Hepatocellular Carcinoma Risk Assessment.

Sialylated alpha-fetoprotein (sAFP) is a very potential marker for the pathogenesis exploration and clinical assessment of hepatocellular carcinoma (H...

Vascular reprogramming in cancer: engineering the tumor microenvironment.

Tumor vasculature has traditionally been viewed as structurally and functionally abnormal and, therefore, is primarily targeted for inhibition. Howeve...

From tumor-centric to ecosystem-based hypotheses in brain tumor research and care: Proceedings from The second Brain Tumor Meeting by the Sea organized by Julie Gavard and Eric Chevet, held in St Malo (France), 23-25 March 2026.

Brain tumors remain among the most lethal cancers, largely due to their remarkable heterogeneity, plasticity, and resistance to therapy. The second Br...

Macrophage heterogeneity in breast cancer microenvironment and development of a prognostic model: exploring TFF1 as a novel therapeutic target.

BACKGROUNDS: Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by ...

Intraoperative technological advances and new frontiers in precision glioma surgery.

BACKGROUND: Diffuse gliomas remain among the most surgically challenging tumors, characterized by their infiltrative nature, proximity to eloquent bra...