This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color-coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave-one-out cross-validation for nonrecurrence and recurrence groups. Fifty-one patients were included, with a median follow up of 38 months (range 7-64 months). Recurrence was observed in 17 patients. The best results were obtained using a k-nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN-model-predicted 3-year recurrence-free survival was 81% and 40% in the predicted no-recurrence and predicted-recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS-radiomics can predict the recurrence group with an accuracy of 75% in patients with node-positive HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
Keywords: head-neck squamous cell carcinoma; machine learning; quantitative ultrasound; radiomics; radiotherapy; recurrence; texture analysis.
© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.