A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study
Lianzhen Zhong,
Di Dong,
Xueliang Fang,
Fan Zhang,
Ning Zhang,
Liwen Zhang,
Mengjie Fang,
Wei Jiang,
Shaobo Liang,
Cong Li,
Yujia Liu,
Xun Zhao,
Runnan Cao,
Hong Shan,
Zhenhua Hu,
Jun Ma,
Linglong Tang,
Jie Tian
Affiliations
Lianzhen Zhong
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Di Dong
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Xueliang Fang
Department of radiation oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong Province 510060, PR China
Fan Zhang
Department of head and neck oncology, The cancer centre of the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province 519000, PR China; Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province 519000, PR China
Ning Zhang
Department of radiation oncology, First People's Hospital of Foshan Affiliated to Sun Yat-sen University, Foshan, Guangdong Province 528000, PR China
Liwen Zhang
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Mengjie Fang
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Wei Jiang
Department of radiation Oncology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Province 541000, PR China
Shaobo Liang
Department of radiation oncology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province 510000, PR China
Cong Li
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Yujia Liu
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Xun Zhao
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Runnan Cao
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China
Hong Shan
Department of head and neck oncology, The cancer centre of the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province 519000, PR China; Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province 519000, PR China
Zhenhua Hu
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China; Corresponding authors at.
Jun Ma
Department of radiation oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong Province 510060, PR China; Corresponding authors at.
Linglong Tang
Department of radiation oncology, Sun Yat-sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong Province 510060, PR China; Corresponding authors at.
Jie Tian
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, PR China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, PR China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, PR China; Corresponding authors at.
Background: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC. Methods: A total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator. Findings: The nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (C-index range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p<0.001 and 0.24, p=0.02); while the trend was opposite in the CCRT-preferred group (HR: 6.24, p<0.001 and 12.08, p<0.001). Similar results for treatment decision using the nomogram were obtained in different subgroups stratified by clinical factors and MR acquisition parameters. Interpretation: Our nomogram could predict the prognosis of T3N1M0 NPC patients with different treatment regimens and accordingly recommend an optimal treatment regimen, which may serve as a potential tool for promoting personalized treatment of NPC. Funding: National Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Strategic Priority Research Program of CAS, Project of High-Level Talents Team Introduction in Zhuhai City, Beijing Natural Science Foundation, Beijing Nova Program, Youth Innovation Promotion Association CAS.
