1. CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy
- Author
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Haifeng Wei, Liu Zhou, Jiayi Zhang, Xin Gao, Xingyu Zhao, Wei Xia, Fengchang Yang, Junming Jian, Rui Zhang, Shuanghu Yuan, and Jinxue Feng
- Subjects
medicine.medical_specialty ,business.industry ,medicine.medical_treatment ,Retrospective cohort study ,General Medicine ,medicine.disease ,Tumor response ,Targeted therapy ,Radiomics ,medicine ,Radiology, Nuclear Medicine and imaging ,Radiology ,Non small cell ,First line chemotherapy ,Lung cancer ,business ,Neuroradiology - Abstract
The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient’s CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value
- Published
- 2021