Objective: To establish a radiomics signature based on CT images of non-small cell lung
cancer (NSCLC) to predict the expression of molecular marker P63. Methods: A total of 245 …

Purpose. In this study, we aimed to develop and validate a noninvasive method based on
radiomics to evaluate the expression of Ki67 and prognosis of patients with non‐small‐cell …

Objective To establish a radiomics signature and a nomogram model based on enhanced
CT images to predict the Ki-67 index of lung cancer. Methods From January 2014 to …

Background The role of TP53 mutations in the diagnosis and treatment of lung cancer has
attracted increasing attention from experts worldwide. This study aimed to explore the …

To explore the ability of using chest CT images to predict the immunotherapy response in
non-small-cell lung cancer (NSCLC) patients, a radiomics model was constructed to …

Purpose To explore the feasibility and performance of machine learning-based radiomics
classifier to predict the cell proliferation (Ki-67) in non-small cell lung cancer (NSCLC) …

Background Energy spectrum CT is an effective method to evaluate the biological behavior
of lung cancer. Radiomics is a non-invasive technology to obtain histological information …

Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a
classification and prediction model of lung cancer based on Support Vector Machine (SVM) …

PURPOSE: To predict programmed death-ligand 1 (PD-L1) expression of tumor cells in non-
small cell lung cancer (NSCLC) patients by using a radiomics study based on CT images …

Background This study aimed at predicting the survival status on non-small cell lung cancer
patients with the phenotypic radiomics features obtained from the CT images. Methods A …