| |
著者: Kawahara D, Imano N, Nishioka R, Ogawa K, Kimura T, Nakashima T, Iwamoto H, Fujitaka K, Hattori N, Nagata Y.
雑誌名: Sci Rep. 2021 Aug 10;11(1):16232. doi: 10.1038/s41598-021-95643-x. Epub 2021 Aug 10.
Abstract/Text
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.
© 2021. The Author(s).
PMID 34376721 Sci Rep. 2021 Aug 10;11(1):16232. doi: 10.1038/s41598-021-95643-x. Epub 2021 Aug 10.
|
