Oncology/Hematology

Automatic Recognition and Prognostic Prediction of Colorectal Liver Metastases Using a Multi-Scale Deep Learning Framework: Model Development and Validation Study.

BACKGROUND: Colorectal cancer liver metastasis (CRLM) presents considerable challenges in both diagnosis and prognosis, as conventional approaches often are limited by subjectivity, variability, and limited efficiency. Recent advances in deep learning have shown great potential for automated extraction of pathological features, offering improved diagnostic accuracy and more reliable prognostic pre...

NQO1-Mediated Anoikis Resistance and Immune Evasion Define a High-Risk Multi-Omic Subtype for Precision Management of T1 High-Grade Bladder Cancer.

T1 high-grade (T1HG) bladder cancer represents an aggressive subset of non-muscle-invasive bladder cancer (NMIBC) with frequent Bacillus Calmette-Guérin (BCG) failure and a high risk of progression, yet current models inadequately guide treatment selection between early cystectomy and bladder preservation. Integrative multi-omics profiling of 147 tumors identifies two clinically distinct subtypes....

Inverse design of an ultra-wideband endfire grooved half-mode waveguide (G-HMWG) antenna based on the CNN approach.

This study presents an AI-assisted inverse design methodology for a compact and ultra-wideband grooved half-mode waveguide (G-HMWG) end-fire antenna. ...

Climate-aware hybrid Kolmogorov-Arnold networks for interpretable solar radiation forecasting.

Accurate short-term solar radiation forecasting is essential for the reliable integration of photovoltaic systems into modern power grids, particularl...

Significance of tumor infiltrating granulocytes and neutrophil extracellular traps in colorectal cancer.

BACKGROUND: Neutrophils are the most abundant granulocytes in the tumor microenvironment and exert both pro- and anti-cancer effects. Activated neutro...

Imaging-based stratification of Claudin 18.2-positive gastric adenocarcinoma using 18F-FDG PET/CT radiomics.

PURPOSE: This study aimed to develop and validate a non-invasive, multimodal radiomics model based on preoperative 1⁸F-FDG PET/CT to predict CLDN18.2 ...

Machine learning based prognostic model for oral squamous cell carcinoma using SEER data and external validation.

Oral squamous cell carcinoma (OSCC) is characterized by an insidious onset, pronounced aggressiveness, and a substantial impact on patient survival. C...

Artificial Intelligence in Skin Inflammation and Oncology: Diagnostic Innovations in Dermatology.

Skin cancer is a prevalent public health problem worldwide, caused by multiple factors like ultraviolet (UV) radiation, environmental toxins, and life...

Whole-transcriptome sequencing and machine learning detect molecular signatures of endometrial cancer in non-invasive vaginal swabs.

OBJECTIVE: Current tissue-based methods for ruling out endometrial cancer in symptomatic women are highly invasive. We explored whether non-invasive v...

Survival prediction in non-small cell lung cancer using layer-wise radiomics and stacked radscore integration: a multi-institutional study.

OBJECTIVES: To enhance prognostic modeling in patients with non-small cell lung cancer (NSCLC), we developed and externally validated a novel radiomic...

Explainable machine learning with SHAP decodes the heterogeneous burden of nasopharyngeal carcinoma in high-risk aging Asia.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a global public health concern, with a particularly high burden concentrated in East Asia, Southeast Asi...

Integrated Strategies for Nursing and Rehabilitation in Lung Cancer Patients.

This review delineates the pivotal role of nursing and rehabilitation in perioperative management and chemotherapy support for lung cancer patients, w...

Machine Learning to Predict Remission Between Six and 24 Months in Rheumatoid Arthritis: Insights from the JAK-pot Collaboration.

OBJECTIVE: To develop, externally validate, and simplify a machine-learning (ML) model to predict remission between six and 24 months in rheumatoid ar...

Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.

Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apo...

Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.

BACKGROUND: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Dee...

Artificial Intelligence-Enabled Prediction of Reversible Versus Irreversible Chemotherapy-Induced Myocardial Injury: Toward Precision Cardio-Oncology.

Cancer therapy-related cardiac dysfunction remains a major cause of morbidity among cancer survivors and may interrupt life-saving oncologic therapy o...

Deep Learning-based Segmentation for Assessment of Kidney Tumour Ablation Therapy in CT Images.

Kidney tumor ablation is a minimally invasive treatment for Renal Cell Carcinoma (RCC). Manual segmentation of the kidney ablation zone (KAZ) is time-...

Deep Learning Driven Evaluation of MR-guided Focused Ultrasound Ablation.

OBJECTIVE: Magnetic resonance-guided focused ultrasound (MRgFUS) thermal therapy is a promising incisionless procedure for breast cancer treatment. fo...

HCCaging: a liver physiological aging-related biomarker for hepatocellular carcinoma diagnosis based on transcriptome data.

Aging is a fundamental biological process that influences cancer development in a context-dependent manner; however, how aging-related programs manife...

Performance of an image-only deep learning breast cancer risk model with the addition of a polygenic risk score.

BACKGROUND: Mammograms contain imaging biomarkers that can predict future breast cancer risk using deep learning (DL) models. We evaluated whether add...