Pre-treatment radiomic features predict individual lymph node failure for head and neck cancer patients

Background and purpose: To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinoma patients. Materials and methods: Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis. Results: There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69–0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively. Conclusion: A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes.