En-Hong Zhuo1, Wei-Jing Zhang2, Hao-Jiang Li2, Guo-Yi Zhang3, Bing-Zhong Jing2, Jian Zhou2, Chun-Yan Cui2, Ming-Yuan Chen2, Ying Sun2, Li-Zhi Liu4, Hong-Min Cai5,6. 1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, People's Republic of 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, No.651 Dongfeng east road, Yuexiu District, Guangzhou, Guangdong province, People's Republic of China. 3. Department of Radiation Oncology, Cancer Center, First People's Hospital of Foshan, Foshan, 528000, Guangdong Province, People's Republic of China. 4. 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, No.651 Dongfeng east road, Yuexiu District, Guangzhou, Guangdong province, People's Republic of China. liulizh@sysucc.org.com. 5. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, People's Republic of China. hmcai@scut.edu.cn. 6. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, South China University of Technology, Guangzhou, People's Republic of China. hmcai@scut.edu.cn.
Abstract
OBJECTIVES: To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI. METHODS: A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test. RESULTS: The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p < .004 and C-index = 0.814, p < .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort. CONCLUSIONS: Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes. KEY POINTS: • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.
OBJECTIVES: To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI. METHODS: A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test. RESULTS: The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p < .004 and C-index = 0.814, p < .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort. CONCLUSIONS: Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes. KEY POINTS: • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.
Entities:
Keywords:
Magnetic resonance imaging; Nasopharynx; Radiomics; Survival analysis
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
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