Identifying optimal input using multilevel radiomics and nested cross-validation for predicting pulmonary function in lung cancer patients treated with radiotherapy

Sang Ho Lee; Peijin Han; Russell K. Hales; K. Ranh Voong; Todd R. McNutt; Junghoon Lee

doi:10.1117/12.2513083

13 March 2019 Identifying optimal input using multilevel radiomics and nested cross-validation for predicting pulmonary function in lung cancer patients treated with radiotherapy

Sang Ho Lee, Peijin Han, Russell K. Hales, K. Ranh Voong, Todd R. McNutt, Junghoon Lee

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501C (2019) https://doi.org/10.1117/12.2513083
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Radiomics is a promising approach to identify patients at high risk of having pulmonary dysfunction caused by radiotherapy. This study aims to identify optimal radiomic input features for predicting pulmonary function. Forced expiratory volume in first second (FEV1) and forced vital capacity (FVC) were measured for 257 patients between 3 months prior to and 1 week after the first radiotherapy. FEV1/FVC ratio dichotomized at 70% was used as a target variable. Each patient had a radiotherapy planning CT and associated contours of gross tumor volume and left/right lungs. A total of 2,658 radiomic features were extracted and categorized into five levels: shape (S), first- (L1), second- (L2) and higher-order (L3) local texture, and global texture (G) features, as well as four multilevel groups: S+L1, S+L1+L2, S+L1+L2+L3, and S+L1+L2+L3+G. Nested cross-validation (NCV) was used to identify optimal input features. Cross-validated glmnet models optimized with unilevel or multilevel features were used to assess predictive performance on outer CV test sets. In unilevel analysis, the highest test AUC of 0.743±0.067 was obtained from NCV models optimized with L1 features. The best performance was achieved from NCV models optimized with S+L1+L2 features with AUC of 0.752±0.063. Paired Wilcoxon signed rank test results showed that AUC values of NCV models optimized with S, L2, L3, G or S+L1+L2+L3 features were statistically significantly different from those optimized with S+L1+L2 features (P<0.05). The multilevel analysis strategy will help to handle and optimize radiomic input features.

Conference Presentation

Citation Download Citation

Sang Ho Lee, Peijin Han, Russell K. Hales, K. Ranh Voong, Todd R. McNutt, and Junghoon Lee "Identifying optimal input using multilevel radiomics and nested cross-validation for predicting pulmonary function in lung cancer patients treated with radiotherapy", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501C (13 March 2019); https://doi.org/10.1117/12.2513083

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available