Cardiovascular

Novel risk calculator predicting long-term survival in pediatric versus adult patients diagnosed with spinal low-grade glioma: a nationwide analysis.

BACKGROUND CONTEXT: Spinal low-grade gliomas (SLGGs) are rare, slow-growing central nervous system tumors affecting both pediatric and adult populations. Due to their rarity, their prognosis and optimal treatment strategies remain poorly defined, necessitating further investigation into age-related differences in outcomes and risk factors. PURPOSE: This study aims to evaluate differences in treatm...

Exploring Radiologists' Use of AI Chatbots for Assistance in Image Interpretation: Patterns of Use and Trust Evaluation.

This study investigated radiologists' perceptions of AI-generated, patient-friendly radiology reports across three modalities: MRI, CT, and mammogram/ultrasound. The evaluation focused on report correctness, completeness, terminology complexity, and emotional impact. Seventy-nine radiologists from four major Saudi Arabian hospitals assessed AI-simplified versions of clinical radiology reports. Eac...

Liquid biopsy in breast cancer management: From circulating biomarkers to clinical breakthroughs.

Breast cancer continues to be a major global health concern, particularly for women, despite improvements in early detection and treatment strategies....

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognosticat...

Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.

INTRODUCTION: Strategic investment in new interventions is crucial for controlling and eliminating NTDs. However, selecting the optimal intervention c...

Tuning antibody stability and function by rational designs of framework mutations.

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches,...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i....

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management...

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is tra...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-...

Exploring graph-based models for predicting active compounds against triple-negative breast cancer.

Breast cancer is among the most dominant and rapidly rising cancers, both in India and around the world. Triple-negative breast cancer (TNBC) is one o...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models ...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse large B-cell lymphoma (DLBCL). Programmed cell death ...

MTMedFormer: multi-task vision transformer for medical imaging with federated learning.

Deep learning has revolutionized medical imaging, improving tasks like image segmentation, detection, and classification, often surpassing human accur...

Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.

Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can significantly reduce mortality rates. Mammograms a...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within ...

Evaluating cell-free DNA integrity index as a non-invasive biomarker for neoadjuvant chemotherapy in colorectal cancer patients.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is gaining attention as a treatment for advanced colorectal cancer owing to its potential to improve surgic...

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interventions, medical images are acquired to provide gui...

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

pyDOSEIA is a Python package designed for meteorological data processing and radiological impact assessment in diverse scenarios, including nuclear an...