Abstract

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Title:: Prediction of radiation pneumonia in lung cancer patients by CT-based radiomics signatures and clinical physical dosimetric features

Author(s):: CHEN Wentao1; 2; SUN Lei1; TAN Aibin3; TANG Shiqiang2; CHEN Fen2; XIAO Jianbiao2; WANG Zhifang2; ZHEN Xin1; 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2. Radiotherapy Center, Chenzhou No.1 Peoples Hospital, Chenzhou 423000, China 3. Outpatient Department, North Hospital of Chenzhou No.1 Peoples Hospital, Chenzhou 423000, China

Keywords:: Keywords: lung cancer radiation pneumonia feature extraction radiomics classifier

Abstract:: Abstract: Objective To combine CT-based radiomics signatures with clinical physical dosimetric features for predicting radiation pneumonitis in lung cancer patients. Methods The clinical physical dosimetric features, CT images and follow-up data of 83 patients with lung cancer who underwent radiotherapy from January 2013 to January 2017 were retrospectively collected. A total of 152 features, including 107 radiomics signatures extracted from the CT images and 45 clinical physical dosimetric features, were collected for each case. Based on 22 feature extraction methods and 8 classifiers, 176 identification models were constructed to analyze the accuracy of 152 features in predicting radiation pneumonia and to evaluate the ability to screen dominant features. Results The highest AUC in the identification model for predicting radiation pneumonitis by clinical physical dosimetric parameter combined with radiomics signatures was 0.90. The top 5 dominant features included shape_Maximum2DDiameterColumn, shape_Maximum3DDiameter, V20, glcm_Imc1 and V45. Discussion The ideal identification model and superior prediction features can be screened from identification models constructed by the combination of different classifiers and feature selection algorithms based on clinical physical dosimetric features and radiomics signatures.