The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

Background Radiomics-based biomarkers enable the prognostication of resected non–small
cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA …

Simple Summary Part I is an overview aimed to investigate some technical principles and
the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) …

Background Adequate safe margin in tongue cancer radical surgery is one of the most
important prognostic factors. However, the role of peritumoral tissues in predicting lymph …

Objectives To develop a fusion model based on clinicopathological factors and MRI
radiomics features for the prediction of recurrence risk in patients with endometrial cancer …

Purpose Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor
prognosis. Radiomic features have emerged as promising predictors of the tumor …

Objectives To evaluate the value of deep learning (DL) combining multimodal radiomics and
clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from …

Purpose This study aims to evaluate the ability of peritumoral, intratumoral, or combined
computed tomography (CT) radiomic features to predict chemotherapy response in non …

Purpose This study aimed to identify patients with pathological complete response (pCR)
and make better clinical decisions by constructing a preoperative predictive model based on …