In breast cancer research, diverse data types and formats, such as radiological images,
clinical records, histological data, and expression analysis, are employed. Given the intricate …

Simple Summary Graph Neural Networks are emerging as a powerful tool for structured data
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …

The burgeoning accumulation of large-scale biomedical data in oncology, alongside
significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) …

Introduction Survival analysis based on cancer registry data is of paramount importance for
monitoring the effectiveness of health care. As new methods arise, the compendium of …

Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in
planning appropriate treatment, reducing cancer-related medical expenses, and significantly …

Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD)
emerges as a distinctive neurological condition characterized by multifaceted challenges …

Utilizing information from multiple sources is a preferred and more precise method for
medical experts to confirm a diagnosis. Each source provides critical information about the …

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a
significant challenge for the scientific and clinical community despite decades of advanced …

Esophageal fistula (EF) is a critical and life-threatening complication following radiotherapy
treatment for esophageal cancer (EC). Albeit tabular clinical data contains other clinically …

The tumor microenvironment is widely recognized for its central role in driving cancer
progression and influencing prognostic outcomes. Despite extensive research efforts …