Lung Subregion Partitioning by Incremental Dose Intervals Improves Omics-Based Prediction for Acute Radiation Pneumonitis in Non-Small-Cell Lung Cancer Patients

Bing Li; Xiaoli Zheng; Jiang Zhang; Saikit Lam; Wei Guo; Yunhan Wang; Sunan Cui; Xinzhi Teng; Yuanpeng Zhang; Zongrui Ma; Ta Zhou; Zhaoyang Lou; Lingguang Meng; Hong Ge; Jing Cai

doi:10.3390/cancers14194889

Lung Subregion Partitioning by Incremental Dose Intervals Improves Omics-Based Prediction for Acute Radiation Pneumonitis in Non-Small-Cell Lung Cancer Patients

Cancers (Basel). 2022 Oct 6;14(19):4889. doi: 10.3390/cancers14194889.

Authors

Bing Li^{1

2}, Xiaoli Zheng², Jiang Zhang¹, Saikit Lam¹, Wei Guo², Yunhan Wang², Sunan Cui³, Xinzhi Teng¹, Yuanpeng Zhang¹, Zongrui Ma¹, Ta Zhou¹, Zhaoyang Lou², Lingguang Meng², Hong Ge², Jing Cai¹

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
² Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
³ Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, Stanford, CA 94305, USA.

Abstract

Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson’s correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features.

Keywords: dosiomics; multi-omics; radiation pneumonitis; radiomics; radiotherapy.

Grants and funding

This research was partly supported by research grants from the Innovation and Technology Fund (ITS/080/19), Mainland-Hong Kong Joint Funding Scheme (MHKJFS) (MHP/005/20), and Shenzhen-Hong Kong-Macau S&T Program (Category C) (SGDX20201103095002019), the National Natural Science Foundation of China (No. 81773230), the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research (SBGJ202103038 and SBGJ202102056), Henan Province Key R&D and Promotion Project (Science and Technology Research) (222102310015).