Ying Yuan1, Jiliang Ren1, Yiqian Shi1, Xiaofeng Tao2. 1. Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: cjr.taoxiaofeng@vip.163.com.
Abstract
PURPOSE: To develop magnetic resonance imaging (MRI)-based radiomic signature and nomogram for preoperatively predicting prognosis in head and neck squamous cell carcinoma (HNSCC) patients. METHOD: This retrospective study consisted of a training cohort (n = 85) and a validation cohort (n = 85) of patients with HNSCC. LASSO Cox regression model was used to select the most useful prognostic features with their coefficients, upon which a radiomic signature was generated. The receiver operator characteristics (ROC) analysis and association of the radiomic signature with overall survival (OS) of patients was assessed in both cohorts. A nomogram incorporating the radiomic signature and independent clinical predictors was then constructed. The incremental prognostic value of the radiomic signature was evaluated. RESULTS: The radiomic signature, consisted of 7 selected features from MR images, was significantly associated with OS of patients with HNSCC (P < 0.0001 for training cohort, P = 0.0013 for validation cohort). The radiomic signature and TNM stage were proved to be independently associated with OS of HNSCC patients, which therefore were incorporated to generate the radiomic nomogram. In the training cohort, the nomogram showed a better prognostic capability than TNM stage only (P = 0.005), which was confirmed in the validation cohort (P = 0.01). Furthermore, the calibration curves of the nomogram demonstrated good agreement with actual observation. CONCLUSIONS: MRI-based radiomic signature is an independent prognostic factor for HNSCC patients. Nomogram based on radiomic signature and TNM stage shows promising in non-invasively and preoperatively predicting prognosis of HNSCC patient in clinical practice.
PURPOSE: To develop magnetic resonance imaging (MRI)-based radiomic signature and nomogram for preoperatively predicting prognosis in head and neck squamous cell carcinoma (HNSCC) patients. METHOD: This retrospective study consisted of a training cohort (n = 85) and a validation cohort (n = 85) of patients with HNSCC. LASSO Cox regression model was used to select the most useful prognostic features with their coefficients, upon which a radiomic signature was generated. The receiver operator characteristics (ROC) analysis and association of the radiomic signature with overall survival (OS) of patients was assessed in both cohorts. A nomogram incorporating the radiomic signature and independent clinical predictors was then constructed. The incremental prognostic value of the radiomic signature was evaluated. RESULTS: The radiomic signature, consisted of 7 selected features from MR images, was significantly associated with OS of patients with HNSCC (P < 0.0001 for training cohort, P = 0.0013 for validation cohort). The radiomic signature and TNM stage were proved to be independently associated with OS of HNSCC patients, which therefore were incorporated to generate the radiomic nomogram. In the training cohort, the nomogram showed a better prognostic capability than TNM stage only (P = 0.005), which was confirmed in the validation cohort (P = 0.01). Furthermore, the calibration curves of the nomogram demonstrated good agreement with actual observation. CONCLUSIONS: MRI-based radiomic signature is an independent prognostic factor for HNSCC patients. Nomogram based on radiomic signature and TNM stage shows promising in non-invasively and preoperatively predicting prognosis of HNSCC patient in clinical practice.
Authors: Andrea Alberti; Luigi Lorini; Marco Ravanelli; Francesco Perri; Marie Vinches; Paolo Rondi; Chiara Romani; Paolo Bossi Journal: Vaccines (Basel) Date: 2022-06-01
Authors: Steven W Mes; Floris H P van Velden; Boris Peltenburg; Carel F W Peeters; Dennis E Te Beest; Mark A van de Wiel; Joost Mekke; Doriene C Mulder; Roland M Martens; Jonas A Castelijns; Frank A Pameijer; Remco de Bree; Ronald Boellaard; C René Leemans; Ruud H Brakenhoff; Pim de Graaf Journal: Eur Radiol Date: 2020-06-04 Impact factor: 5.315