Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Runsheng Chang; Shouliang Qi; Yifan Zuo; Yong Yue; Xiaoye Zhang; Yubao Guan; Wei Qian

doi:10.3389/fonc.2022.915835

Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Front Oncol. 2022 Aug 8:12:915835. doi: 10.3389/fonc.2022.915835. eCollection 2022.

Authors

Runsheng Chang¹, Shouliang Qi^{1

2}, Yifan Zuo¹, Yong Yue³, Xiaoye Zhang⁴, Yubao Guan⁵, Wei Qian¹

Affiliations

¹ College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
² Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
³ Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
⁴ Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China.
⁵ Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Abstract

Purpose: This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).

Methods: After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.

Results: The radiomic model using features from the peritumoral region of 0-3 mm outperformed that using features from 3-6, 6-9, 9-12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0-3 and 3-6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).

Conclusions: CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Keywords: Computed Tomography (CT); area under curve; chemotherapy response; non-small cell lung cancer; peritumoral features; radiomics.