Abstract

Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong’s test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest “corrected” AUC of 0.784 (BCa 95%CI: 0.673–0.859) and 0.723 (BCa 95%CI: 0.534–0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong’s test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.

Highlights

  • Radiotherapy (RT) is a cornerstone modality for nasopharyngeal cancer (NPC) patients [1,2], among which the involvement of neck lymph nodes (LNs) is of high prevalence [3]

  • We aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting ill-fitted thermoplastic mask (TM) (IfTMs)-triggered adaptive radiotherapy (ART) events in nasopharyngeal carcinoma (NPC) patients via a multi-center setting

  • (p = 0.163 and p = 0.215, respectively) and Queen Mary Hospital (QMH) (p = 0.576, p = 0.443, respectively) cohort; pre-treatment BMI was found to be statistically significant in the QMH cohort (p = 0.014), but not in the Queen Elizabeth Hospital (QEH) cohort (p = 0.600)

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Summary

Introduction

Radiotherapy (RT) is a cornerstone modality for nasopharyngeal cancer (NPC) patients [1,2], among which the involvement of neck lymph nodes (LNs) is of high prevalence [3]. Immobilization device that provides full coverage of the head and bi-lateral shoulders is deployed for each NPC patient to ensure reproducible patient positioning between RT fractions in order to maintain treatment efficacy [6]. In cases of ill-fitted TMs (IfTMs), an ad hoc adaptive radiotherapy (ART) may be triggered to ensure accuracy and safe radiation delivery and to maintain treatment efficacy [14,15,16,17]. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for the sake of improving medical resource allocation and achieving greater procedural efficiency in oncologic care delivery

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