Bingzhong Jing1, Yishu Deng1, Tao Zhang2, Dan Hou2, Bin Li1, Mengyun Qiang3, Kuiyuan Liu3, Liangru Ke4, Taihe Li5, Ying Sun6, Xing Lv7, Chaofeng Li8. 1. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. 2. Guangzhou Deepaint intelligence Tenchnology Co.Ltd., Guangzhou 510060, China. 3. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. 4. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Radiology, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. 5. Shenzhen Annet Information System Co.LTD., Guangzhou 510060, China. 6. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Radiotherapy, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. 7. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. Electronic address: lvxing@sysucc.org.cn. 8. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Information, Sun Yat-Sen University Cancer Centre, Guangzhou 510060, China. Electronic address: lichaofeng@sysucc.org.cn.
Abstract
BACKGROUND: Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients. OBJECTIVE: To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients. METHODS: In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient. RESULT: A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610). CONCLUSIONS: The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.
BACKGROUND: Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients. OBJECTIVE: To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients. METHODS: In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient. RESULT: A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610). CONCLUSIONS: The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.
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