Lian-Zhen Zhong1, Xue-Liang Fang2, Di Dong1, Hao Peng3, Meng-Jie Fang1, Cheng-Long Huang2, Bing-Xi He4, Li Lin2, Jun Ma5, Ling-Long Tang6, Jie Tian7. 1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, PR China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China. 2. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, PR China. 3. Center for Translational Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China. 4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, PR China. 5. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, PR China. Electronic address: majun2@mail.sysu.edu.cn. 6. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, PR China. Electronic address: tangll@mail.sysu.edu.cn. 7. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, PR China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, PR China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, PR China. Electronic address: jie.tian@ia.ac.cn.
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.
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.
Authors: Wai Tong Ng; Barton But; Horace C W Choi; Remco de Bree; Anne W M Lee; Victor H F Lee; Fernando López; Antti A Mäkitie; Juan P Rodrigo; Nabil F Saba; Raymond K Y Tsang; Alfio Ferlito Journal: Cancer Manag Res Date: 2022-01-26 Impact factor: 3.989