Prognostic radiomic signature for head and neck cancer: Development and validation on a multi-centric MRI dataset

Marco Bologna, Valentina Corino, Stefano Cavalieri, Giuseppina Calareso, Silvia Eleonora Gazzani, Tito Poli, Marco Ravanelli, Davide Mattavelli, Pim de Graaf, Irene Nauta, Kathrin Scheckenbach, Lisa Licitra, Luca Mainardi

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

BACKGROUND AND PURPOSE: Prognosis in locally advanced head and neck cancer (HNC) is currently based on TNM staging system and tumor subsite. However, quantitative imaging features (i.e., radiomic features) from magnetic resonance imaging (MRI) may provide additional prognostic info. The aim of this work is to develop and validate an MRI-based prognostic radiomic signature for locally advanced HNC.

MATERIALS AND METHODS: Radiomic features were extracted from T1- and T2-weighted MRI (T1w and T2w) using the segmentation of the primary tumor as mask. In total 1072 features (536 per image type) were extracted for each tumor. A retrospective multi-centric dataset (n = 285) was used for features selection and model training. The selected features were used to fit a Cox proportional hazard regression model for overall survival (OS) that outputs the radiomic signature. The signature was then validated on a prospective multi-centric dataset (n = 234). Prognostic performance for OS and disease-free survival (DFS) was evaluated using C-index. Additional prognostic value of the radiomic signature was explored.

RESULTS: The radiomic signature had C-index = 0.64 for OS and C-index = 0.60 for DFS in the validation set. The addition of the radiomic signature to other clinical features (TNM staging and tumor subsite) increased prognostic ability for both OS (HPV- C-index 0.63 to 0.65; HPV+ C-index 0.75 to 0.80) and DFS (HPV- C-index 0.58 to 0.61; HPV+ C-index 0.64 to 0.65).

CONCLUSION: An MRI-based prognostic radiomic signature was developed and prospectively validated. Such signature can successfully integrate clinical factors in both HPV+ and HPV- tumors.

Original languageEnglish
Article number109638
JournalRadiotherapy and oncology
Volume183
Early online date31 Mar 2023
DOIs
Publication statusPublished - 1 Jun 2023

Keywords

  • Head and neck cancer
  • MRI
  • Machine learning
  • Radiomics
  • Survival analysis

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