Quantitative ultrasound radiomics in predicting recurrence for patients with node ‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy

Quantitative ultrasound radiomics obtained before starting treatment was used to predict the outcomes in patients with head ‐neck squamous cell carcinoma treated with radical radiotherapy. A KNN classifier was able to predict the recurrence with an accuracy of 75%. AbstractThis 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. Co lor‐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 institut ional 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‐on e 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...
Source: Cancer Medicine - Category: Cancer & Oncology Authors: Tags: ORIGINAL RESEARCH Source Type: research