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Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis.

ABSTRACT: To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction from the whole lung (whole-lung radiomics analysis) and imaging- and dosimetric-based segmentation (multi-region radiomics analysis) were performed. Patients with RP grade ≥ 2 or < 2 were classified. Predictors were selected with least absolute shrinkage and selection operator logistic regression and the model was built with neural network classifiers. A total of 49,383 radiomics features per patient image were extracted from the radiotherapy planning computed tomography. We identified 4 features and 13 radiomics features in the whole-lung and multi-region radiomics analysis for classification, respectively. The accuracy and area under the curve (AUC) without the synthetic minority over-sampling technique (SMOTE) were 60.8%, and 0.62 for whole-lung and 80.1%, and 0.84 for multi-region radiomics analysis. These were improved 1.7% for whole-lung and 2.1% for multi-region radiomics analysis with the SMOTE. The developed multi-region radiomics analysis can help predict grade ≥ 2 RP. The radiomics features in the median- and high-dose regions, and the local intensity roughness and variation were important factors in predicting grade ≥ 2 RP.

SUBMITTER: Kawahara D

PROVIDER: S-EPMC8355298 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeAdjuvant durvalumab has become a standard treatment after chemoradiation therapy for patients with locally advanced non-small cell lung cancer (LA-NSCLC). Accordingly, predicting radiation pneumonitis (RP) requiring steroidal treatment (steroid-RP) is of utmost importance because steroidal administration is reported to weaken the effectiveness of immunotherapy. However, grade 2 RP was used as an index of RP in previous studies, but it is an ambiguous definition because it includes not only steroid-RP but also a mild cough requiring only a cough medicine. Therefore, in this study, steroid-RP was used for evaluating RP, and the purpose of this study was to investigate predictive factors of steroid-RP, including original simple interstitial lung abnormality scores (ILASs).Methods and materialsA total of 145 patients with LA-NSCLC who received definitive radiation therapy (DRT) in our institution from January 2014 to May 2017 were identified. Original ILASs, performance status, age, respiratory function, Brinkman index, concurrent administration of chemotherapy, and dose-volume histogram metrics of the lung were analyzed to evaluate their association with steroid-RP. Additionally, 3 diagnostic radiologists evaluated the patients' pre-DRT chest computed tomography images and determined the simple ILASs. ILASs were rated as follows: 0: none; 1: abnormality without honeycombing (ground-glass attenuation, fine reticular opacity, and microcysts); and 2: honeycombing.ResultsThe median follow-up period was 729 days. Thirty-one patients (21.4%) experienced steroid-RP. In the univariate analysis, lung V5/V10/VS5, Brinkman index, and ILASs were significant predictive factors of steroid-RP. Additionally, multivariate analysis including Brinkman index ≥840, lung V5 ≥37%, and an ILAS ≥1 revealed that only an ILAS (P = .001) was an independent predictive factor of steroid-RP.ConclusionsThe original simple ILAS was an easy-to-use tool and a significant predictive factor of steroid-RP in DRT in patients with LA-NSCLC.

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Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis.

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