The application of radiogenomics in oncology has great prospects in precision medicine.
Radiogenomics combines large volumes of radiomic features from medical digital images …

Artificial Intelligence (AI) has transformed how we live and how we think, and it will change
how we practice medicine. With multimodal big data, we can develop large medical models …

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is
an aggressive variant associated with angiogenesis and immunosuppressive tumor …

Background Accurate predictions of distant metastasis (DM) in locally advanced rectal
cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in …

Objectives To develop and validate a deep learning imaging signature (DLIS) for risk
stratification in patients with multiforme (GBM), and to investigate the biological pathways …

The immune system is an interacting network of plentiful molecules that could better
characterize the relationship between immunity and cancer. This study aims to investigate …

Objectives To develop a dynamic nomogram containing radiomics signature and clinical
features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and …

Objectives To investigate whether radiomic features extracted from dynamic susceptibility
contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular …

Survival prediction of patients affected by brain tumors provides essential information to
guide surgical planning, adjuvant treatment selection, and patient counseling. Current …

Background Prognostic evaluation is important for personalized treatment in children with
medulloblastoma (MB). Limited data are available for risk stratification using a radiomics …