Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features

AiHui Feng; Ying Huang; Ya Zeng; Yan Shao; Hao Wang; Hua Chen; HengLe Gu; YanHua Duan; ZhenJiong Shen; ZhiYong Xu

doi:10.1016/j.cllc.2024.01.006

Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features

Clin Lung Cancer. 2024 Feb 1:S1525-7304(24)00008-1. doi: 10.1016/j.cllc.2024.01.006. Online ahead of print.

Authors

AiHui Feng¹, Ying Huang¹, Ya Zeng², Yan Shao², Hao Wang², Hua Chen², HengLe Gu², YanHua Duan¹, ZhenJiong Shen², ZhiYong Xu³

Affiliations

¹ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China; Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai, China.
² Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: xzyong12vip@sina.com.

PMID: 38402120
DOI: 10.1016/j.cllc.2024.01.006

Abstract

Introduction: Patients with early non-small-cell lung cancer (NSCLC) have a relatively long survival time after stereotactic body radiation therapy (SBRT). Predicting radiation-induced pneumonia (RP) has important clinical and social implications for improving the quality of life of such patients. This study developed an RP prediction model by using 3-dimensional (3D) dosiomic features. The model can be used to guide radiation therapy to reduce toxicity.

Methods: Radiomic features were extracted from pre-treatment CT, dose-volume histogram (DVH) parameters and dosiomic features were extracted from the 3D dose distribution of 140 lung cancer patients. Four predictive models: (1) CT; (2) CT + DVH; (3) CT + Rtdose; and (4) Hybrid, CT + DVH + Rtdose, were trained to predict symptomatic RP by extremely randomized trees. Accuracy, sensitivity, specificity, and area under the receiver operator characteristic curve were evaluated.

Result: Results showed that the fraction regimen was correlated with symptomatic RP (P < .001). The proposed model achieved promising prediction results. The performance metrics for CT, CT + DVH, CT + Rtdose, and Hybrid were as follows: accuracy: 0.786, 0.821, 0.821, and 0.857; sensitivity: 0.625, 1, 0.875, and 1; specificity: 0.8, 0.565, 0.5, and 0.875; and area under the receiver operator characteristic curve: 0.791, 0.809, 0.907, and 0.920, respectively.

Conclusion: Dosiomic features can improve the performance of the predictive model for symptomatic RP compared with that obtained with the CT + DVH model. The model proposed in this study can help radiation oncologists individually predict the incidence rate of RP.

Keywords: Lung cancer; Machine learning; SBRT; Symptomatic RP.