A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer.

Abstract

To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.

• Authors: Jing Gong, Xiao Bao, Ting Wang, Jiyu Liu, Weijun Peng, Jingyun Shi, Fengying Wu, Yajia Gu
• Journal: Oncoimmunology (Oncoimmunology) Vol. 11 Issue 1 Pg. 2028962 ( 2022) ISSN: 2162-402X [Electronic] United States
• PMID: 35096486 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Topics

• Adenocarcinoma
• Carcinoma, Non-Small-Cell Lung (diagnostic imaging, therapy)
• Carcinoma, Squamous Cell (diagnostic imaging, therapy)
• Follow-Up Studies
• Humans
• Immunotherapy
• Lung Neoplasms (diagnostic imaging, therapy)
• Retrospective Studies
• Tomography, X-Ray Computed (methods)