Development and validation of a lung biological equivalent dose-based multiregional radiomic model for predicting symptomatic radiation pneumonitis after SBRT in lung cancer patients

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BackgroundThis study aimed to develop and validate a multiregional radiomic-based composite model to predict symptomatic radiation pneumonitis (SRP) in non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).Materials and methods189 patients from two institutions were allocated into training, internal validation and external testing cohorts. The associations between the SRP and clinic-dosimetric factors were analyzed using univariate and multivariate regression. Radiomics features were extracted from seven discrete and three composite regions of interest (ROIs), including anatomical, physical dosimetry, and biologically equivalent dose (BED) dimensions. Correlation filters and Lasso regularization were applied for feature selection and five machine learning algorithms were utilized to construct radiomic models. Multiregional radiomic models integrating features from various regions were developed and undergone performance test in comparison with single-region models. Ultimately, three models—a radiomic model, a dosimetric model, and a combined model—were developed and evaluated using receiver operating characteristic (ROC) curve, model calibration, and decision curve analysis.ResultsVBED70 (α/β = 3) of the nontarget lung volume was identified as an independent dosimetric risk factor. The multiregional radiomic models eclipsed their single-regional counterparts, notably with the incorporation of BED-based dimensions, achieving an area under the curve (AUC) of 0.816 [95% CI: 0.694–0.938]. The best predictive model for SRP was the combined model, which integrated the multiregional radiomic features with dosimetric parameters [AUC=0.828, 95% CI: 0.701–0.956]. The calibration and decision curves indicated good predictive accuracy and clinical benefit, respectively.ConclusionsThe combined model improves SRP prediction across various SBRT fractionation schemes, which warrants further validation and optimization using larger-scale retrospective data and in prospective trials.