Multi-Omics Classifier of Tumor Recurrence vs. Radiation-Induced Lung Fibrosis in NSCLC Patients Treated with SBRT

Abstract

<h3>Purpose/Objective(s)</h3> Differentiation between radiation-induced lung fibrosis (RILF) and tumor local recurrence (LR) remains challenging in SBRT treated NSCLC patients. We aimed to integrate spatially and time-resolved 4D radiomics with dosiomics biomarkers to develop a novel robust multi-omics classifier of LR vs RILF. <h3>Materials/Methods</h3> 210 NSCLC patients (101/48% T1-2N0M0, 109/52% T1-3N1/xM0/x) treated with SBRT (median dose 60 Gy/8 fractions) between 2009 and 2019 were identified. Image-based quantitative features were extracted from the SBRT planning and follow-up (FU) CTs at the time point of diagnosis of LR/RILF or a matched time point for CTs without LR/RILF (radiomics) and from dose distributions (dosiomics). The region of interest was the planning target volume plus a 10mm margin (PTV_+10mm). Time-dependent alterations of the radiomics features in FU CTs were integrated (delta-radiomics). To identify relevant features associated with RILF or LR, resampling (iterations = 1000) of feature selection methods were applied. An ensemble classifier comprising random forests, neural networks, and logistic regression was then trained. The area under the ROC curve (AUC) with a 70%/30% training/testing split and a 5-fold cross-validated AUC on the entire cohort were calculated for performance assessment at CT FU. <h3>Results</h3> Out of the 210, 36 patients (17%) were reported with LR and 44 (21%) with RILF. The median FU time was 19 months (range, 5-87). 20 LR and 23 RILF FU CTs, both with median time after SBRT of 15 months, were identified. 53 patients with matching clinical characteristics without LR/RILF (none), at matched FU time points (median time after SBRT: 17 months), were additionally selected, resulting in a cohort of 96 patients (20/21% LR, 23/24% RILF, 53/55% none). Discrimination of RILF versus LC using 4D radiomics features was achieved with a testing and 5-fold AUC of 0.82 [95%CI 0.79 0.86] and 0.85 [0.83 0.88]. The addition of a dosiomics feature improved performance to 0.85 [0.82 0.87] and 0.88 [0.85 0.91]. The PTV_+10mm-based classifier includes 4 textural features (2 delta-radiomics, 1 radiomics from the FU CT and 1 dosiomics). A non-significant correlation was found between the significant features and tumor volume before RT (Spearman's r <0.1, p<0.05). <h3>Conclusion</h3> Our data indicate that integrative omics by combining radiotherapy volume and dose constraints with spatially and time-resolved radiomics may provide a novel mean for better discrimination of tumor recurrence vs RILF after SBRT.