Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node‐positive head and neck cancer

• Published in: Head & neck Vol. 45; no. 5; pp. 1184 - 1193
• Main Authors: Franzese, Ciro, Lillo, Sara, Cozzi, Luca, Teriaca, Maria Ausilia, Badalamenti, Marco, Di Cristina, Luciana, Vernier, Veronica, Stefanini, Sara, Dei, Damiano, Pergolizzi, Stefano, De Virgilio, Armando, Mercante, Giuseppe, Spriano, Giuseppe, Mancosu, Pietro, Tomatis, Stefano, Scorsetti, Marta
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2023; Wiley Subscription Services, Inc
• Subjects: Head & neck cancer; head and neck cancer; Head and Neck Neoplasms - pathology; Humans; Lymph nodes; Lymph Nodes - pathology; Metastases; Prediction models; predictive factor; predictive model; Radiation therapy; Radiomics; radiotherapy; Retrospective Studies; ROC Curve; Survival; radiotherapy; radiomics; predictive model; head and neck cancer; predictive factor
• ISSN: 1043-3074; 1097-0347
• DOI: 10.1002/hed.27332

Background Prediction of survival and radiation therapy response is challenging in head and neck cancer with metastatic lymph nodes (LNs). Here we developed novel radiomics‐ and clinical‐based predictive models. Methods Volumes of interest of LNs were employed for radiomic features extraction. Radiomic and clinical features were investigated for their predictive value relatively to locoregional failure (LRF), progression‐free survival (PFS), and overall survival (OS) and used to build multivariate models. Results Hundred and six subjects were suitable for final analysis. Univariate analysis identified two radiomic features significantly predictive for LRF, and five radiomic features plus two clinical features significantly predictive for both PFS and OS. The area under the curve of receiver operating characteristic curve combining clinical and radiomic predictors for PFS and OS resulted 0.71 (95%CI: 0.60–0.83) and 0.77 (95%CI: 0.64–0.89). Conclusions Radiomic and clinical features resulted to be independent predictive factors, but external independent validation is mandatory to support these findings.