Cardiovascular

Deep learning inference of cell type-specific gene expression from breast tumor histopathology.

Cell type-specific gene expression from single-cell RNA sequencing (RNA-seq) is valuable for breast cancer precision oncology but available cohorts are still limited due to its high cost. Deconvolution methods infer cell type-specific expression from bulk RNA-seq at a lower cost, yet expenses and processing time of bulk RNA-seq are also non-negligible and limit their application too. To address th...

X2Shape: CT-free 3D multi-organ reconstruction with biplanar X-rays.

Reconstructing three-dimensional (3D) anatomy from routine X-ray imaging remains a long-standing challenge, promising high accessibility and minimal radiation exposure compared to computed tomography (CT). We propose X2Shape, a deep learning framework that enables direct 3D multi-organ reconstruction from orthogonal biplanar X-rays, without requiring CT priors. X2Shape combines a geometry-aware vo...

Dynamic changes in serum HER2-peptide-specific autoantibodies predict response to neoadjuvant therapy in HER2-positive breast cancer.

Update on pleural mesothelioma.

PURPOSE OF REVIEW: Pleural mesothelioma remains a universally lethal cancer with a rising global burden. This underscores the need for pulmonologists ...

DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design.

Realizing the therapeutic potential of antibodies requires simultaneously optimizing multiple properties, such as antigen-binding specificity, viscosi...

Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides.

BACKGROUND: The assessment of estrogen/progesterone receptors (ER/PR) and human epidermal growth factor receptor 2 (HER2) is essential for managing br...

Integrative multi-omics analysis identifies PAICS as a key regulator of epithelial-mesenchymal transition in breast cancer metastasis.

BACKGROUND: Breast cancer metastasis remains a major clinical challenge due to its complex molecular mechanisms, highlighting the need to identify key...

Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.

BACKGROUND: Prognostic information is essential for decision-making in breast cancer management. In recent years, trials and clinical practice have em...

AI-based classification of pediatric sagittal skeletal patterns using multi-view facial photographs.

OBJECTIVE: The increasing global demand to assess pediatric skeletal malocclusions poses a growing challenge, as current screening methods are depende...

Breast area affects the performance of a commercial artificial intelligence algorithm assessment of negative digital breast tomosynthesis exams.

OBJECTIVE: To understand whether cancer-neutral image attributes (breast area and number of slices) impact an AI algorithm assessment of negative digi...

Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?

PURPOSE: In this prospective cross-over study, the precision of manual correction of the clinical target volume (CTV) during online-adaptive radiother...

Machine Learning to Predict Implant-Based Breast Reconstruction Failure: A Bootstrap-Validated Elastic Net Model.

BACKGROUND: Implant-based reconstruction failure remains a significant complication following breast reconstruction, with substantial implications for...

Objective quantification of chemotherapy-induced madarosis: a pilot study of an automated computer vision pipeline for eyebrow density assessment.

Chemotherapy-induced madarosis significantly impacts patient quality of life, yet current assessment methods rely heavily on subjective grading, limit...

Integrated genomic profiling identifies predictive biomarkers for neoadjuvant therapy response in Chinese breast cancer patients.

Neoadjuvant therapy (NAT) has emerged as a standard treatment strategy for locally advanced breast cancer (BC), yet robust biomarkers for response pre...

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Digital twin technology has emerged as a transformative innovation in healthcare, offering virtual replicas of physical entities at patient-level, equ...

Bone Magnetic Resonance Imaging: From Conventional Methods to AI-Driven Solutions.

With global population aging and increased life expectancy, bone diseases affect a substantial proportion of individuals worldwide, imposing a signifi...

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genom...

High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance.

Uveal melanoma (UM) presents a formidable clinical challenge due to its marked resistance to radiotherapy. In this study, an integrative strategy comb...

Clinical relevance of the characterisation and quantification of CD8+ T-cell infiltration in high-grade serous ovarian carcinoma using machine learning-based image analysis of dual immunostaining.

INTRODUCTION: High-grade serous carcinoma (HGSC) is the most common and aggressive subtype of ovarian cancer. Although tumour stage and complete cytor...

Deep learning on genome-wide association studies to predict the patient-specific risk of radiation-induced erectile dysfunction.

OBJECTIVE: Radiation-induced erectile dysfunction (RIED) is a frequent, unpredictable complication following radiotherapy for prostate cancer. We hypo...