Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy

Hirose, Taka aki; Arimura, Hidetaka; Ninomiya, Kenta; Yoshitake, Tsuyoshi; Fukunaga, Junichi; Shioyama, Yoshiyuki

doi:10.1038/s41598-020-77552-7

Cited by 34 publications

(39 citation statements)

References 32 publications

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“…We believe that our data reflect the population of esophageal cancer patients since patient characteristic (age, sex and tumor stage) were similar to other study of esophageal cancer [ 27 , 29 , 37 ]. The incident of RP grade ≥ 2 in esophageal cancer was report by systematic review about 6.6% which similar to our study (5%), although information for grade 1 RP were lacking.…”

Section: Discussionsupporting

confidence: 88%

“…The minority class in the training set was randomly oversampled with replacement to equalize the two classes. Multivariate logistic regression with L1 norm regularization (LASSO) was used [ 25 – 27 ]. LASSO was performed to prevent overfitting and can also be used as embedded feature selection in the logistic regression model by shrinking the coefficient of unimportant features to zero.…”

Section: Methodsmentioning

confidence: 99%

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Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

et al. 2021

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Objective The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). Materials and methods DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs). Result The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2. Conclusion Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.

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Section: Discussionsupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

et al. 2021

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“…Through quantitative assessment of FDG PET/CT imaging before and after radiation therapy in stage III NSCLC patients, a pilot study suggested that global lung parenchymal glycolysis and lung parenchymal SUVmean may serve as potentially useful biomarkers to lung inflammation after thoracic radiation therapy [ 154 ]. Moreover, based on pretreatment planning CT in patients after SBRT delivery, the radiomic predictive model of lung volume irradiated with more than 5 Gy (LV5) was considered the best for RP estimation than the DVH model [ 155 ]. By the combination use of cone-beam CT radiomics features (NGTDM25: Contras and others) and two pretreatment CT radiomics features (SHAPE: Mass and SHAPE: Orientation), the prediction specificity of lung toxicity was further improved from 80.77% to 84.62% after SBRT in stage I NSCLC patients [ 156 ].…”

Section: Potential Biomarkers For Monitoring Radiation-induced Lung Injurymentioning

confidence: 99%

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Liu

Shao

2021

Biomedicines

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Radiation-induced lung injury (RILI) is one of the main dose-limiting side effects in patients with thoracic cancer during radiotherapy. No reliable predictors or accurate risk models are currently available in clinical practice. Severe radiation pneumonitis (RP) or pulmonary fibrosis (PF) will reduce the quality of life, even when the anti-tumor treatment is effective for patients. Thus, precise prediction and early diagnosis of lung toxicity are critical to overcome this longstanding problem. This review summarizes the primary mechanisms and preclinical animal models of RILI reported in recent decades, and analyzes the most promising biomarkers for the early detection of lung complications. In general, ideal integrated models considering individual genetic susceptibility, clinical background parameters, and biological variations are encouraged to be built up, and more prospective investigations are still required to disclose the molecular mechanisms of RILI as well as to discover valuable intervention strategies.

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“…The potential of planning computed tomography (pCT) image signatures in the prediction of RP status after SABR has been explored in previous radiomics studies [ 12 , 13 ]. Hirose et al utilized histogram and texture features extracted from pCT and wavelet decomposition (WD) images, which have been widely used in radiomics studies, to construct the image signature and prediction model using the least absolute shrinkage and selection operator logistic regression (LASSO-LR) [ 13 ]. Moran et al attempted to develop a LR model for the classification of the RP status based on the image features extracted from the pCT as well as the post-SABR diagnostic CT images [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis

et al. 2022

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Objectives We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer. Methods A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models. Results The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively. Conclusion This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.

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Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy

Cited by 34 publications

References 32 publications

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis

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