A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0. (October 2020)

• Record Type: Journal Article
• Authors: Zhong, Lian-Zhen; Fang, Xue-Liang; Dong, Di; Peng, Hao; Fang, Meng-Jie; Huang, Cheng-Long; He, Bing-Xi; Lin, Li; Ma, Jun; Tang, Ling-Long; Tian, Jie
• Is Part Of: Radiotherapy and oncology. Volume 151(2020)
• Page Start: 1
• Page End: 9
• Publication Date: 2020-10
• Subjects: Nasopharyngeal cancer -- Induction chemotherapy -- MRI-based treatment planning -- Deep learning -- Survival analysis; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.06.050
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: DL-based radiomic signatures were significantly correlated with prognosis of NPC. DL-based radiomic signatures were complementary to clinical prognostic factors. The radiomic nomogram improved prediction of DFS, OS, DMFS and LRFS of NPC. The radiomic nomogram may assist in pretreatment risk stratification. Abstract: Purpose: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT). Methods: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan–Meier estimator. Results: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695–0.731, all p < 0.001) and test (C-index: 0.706–0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001). Conclusions: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC.