Multi-organ multi-omics prediction of adaptive radiotherapy eligibility in patients with nasopharyngeal carcinoma

Abstract

Intensity-modulated radiotherapy (IMRT) is a standard-of-care for advanced nasopharyngeal carcinoma (NPC) patients. The success of treatment relies on an assumption that patient anatomy remains throughout the entire IMRT course. In response to treatment perturbations, however, tumors and surrounding healthy organs may exhibit significant morphometric volume and/or geometric alterations, which may jointly alter patient anatomy and jeopardize treatment efficacy. Adaptive Radiotherapy (ART) can compensate for these patient-specific variations. Nevertheless, most of existing ART triggers require close monitoring throughout the IMRT course, and are deficient in capturing inter-patient disparity in intrinsic biologic tissue response. Therefore, effective pre-treatment prediction of ART eligibility is greatly demanding. In this study, various machine learning techniques was applied to investigate capability of a variety of prediction models, developed by using different types of "- omics" features extracted from various organ structures, for pre-treatment prediction of ART demand in NPC patients, with an ultimate objective to facilitate ART clinical implementation in the future. First, 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic features extracted from neck nodal lesions of Computed Tomography (CT) images, clinical data, and combined types of features were used for developing R, C, and RC models, respectively, for predicting ill-fitted thermoplastic mask (IfTM)-triggered ART event. Results showed that the R model performed significantly better than the C model in the external QMH testing cohort (p<0.0001), while demonstrating no significant difference compared to the RC model (p=0.5773). Second, pre-treatment contrast-enhanced T1-weighted (CET1-w), T2-w magnetic resonance (MR) images of seventy NPC patients from QEH were processed for extraction of radiomic features from Gross-Tumor-Volume of primary NPC tumor, for developing CET1-w, T2-w, and joint T1-T2 models. Results indicated promising predictability of MR-based tumoral radiomics, with AUCs ranging from 0.895–0.984 in the training set and 0.750–0.930 in the testing set. Third, pre-treatment CECT and MR images, radiotherapy dose and contour data of one-hundred and thirty-five NPC patients treated at QEH were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from eight organ structures. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed. Results demonstrated that the R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC 0.918 (95%CI: 0.903-0.933) in hold-out test set, respectively. Intriguingly, Radiomic features accounted for the majority of the final selected features (64-94%) in all the studied multi-omics models. In conclusion, a series of studies in this thesis demonstrated that CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients undergoing RT, showing higher predictability over clinical predictors. MRI-based tumoral radiomics was shown promising in pre-treatment identification of ART eligibility in NPC patients. Multi-organ multi-omics analyses revealed that the Radiomic model played a dominant role for ART eligibility in NPC patients. The overall findings may provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the future.