Kidney tumors are a significant health concern. Early detection and accurate segmentation
of kidney tumors are crucial for timely and effective treatment, which can improve patient …

Objective The goal of this study was to evaluate the efficacy of machine learning (ML)
techniques in predicting survival for chordoma patients in comparison with the standard Cox …

Background The machine learning models with dose factors and the deep learning models
with dose distribution matrix have been used to building lung toxics models for radiotherapy …

This study compared the diagnostic efficiency of benign and malignant breast nodules using
ultrasonographic characteristics coupled with several machine-learning models, including …

Background The purpose of this study is to develop and validate the potential value of the
deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass …

Objectives This study aims to investigate the potential of radiomics with multiple parameters
from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in …

The methods of deep learning and traditional radiomics feature extraction were preliminarily
discussed, and a multimodal data prediction model for breast cancer clinical stage was …

Objective This study aimed to develop a prediction model for esophageal fistula (EF) in
esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT) …

Abstract Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered
innumerable health complications, imposing great challenges in managing other respiratory …

Purpose To develop and evaluate the performance of radiomics-based computed
tomography (CT) combined with machine learning algorithms in detecting occult vertebral …