Details

Autor(en) / Beteiligte

• Tran, William T
• Suraweera, Harini
• Quiaoit, Karina
• DiCenzo, Daniel
• Fatima, Kashuf
• Jang, Deok
• Bhardwaj, Divya
• Kolios, Christopher
• Karam, Irene
• Poon, Ian
• Sannachi, Lakshmanan
• Gangeh, Mehrdad
• Sadeghi-Naini, Ali
• Dasgupta, Archya
• Czarnota, Gregory J

Titel

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

Ist Teil von

• Future science OA, 2020-10, Vol.6 (9), p.FSO624

Ort / Verlag

England: Future Science Ltd

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment. Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).