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.

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

Franzese, Ciro;De Virgilio, Armando;Mercante, Giuseppe;Spriano, Giuseppe;Scorsetti, Marta
2023-01-01

Abstract

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.
2023
head and neck cancer
predictive factor
predictive model
radiomics
radiotherapy
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/77624
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact