Yuming Jiang1,2, Cheng Jin2, Heng Yu2, Jia Wu2, Chuanli Chen3, Qingyu Yuan3, Weicai Huang1, Yanfeng Hu1, Yikai Xu3, Zhiwei Zhou4, George A Fisher5, Guoxin Li1, Ruijiang Li2. 1. Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. 2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California. 3. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China. 4. Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. 5. Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, California.
Abstract
OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images. BACKGROUND: Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer. METHODS: We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. RESULTS: The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P< 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease). CONCLUSIONS: The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.
OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images. BACKGROUND: Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer. METHODS: We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. RESULTS: The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P< 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease). CONCLUSIONS: The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.
Authors: Jorge E Jimenez; Dong Dai; Guofan Xu; Ruiyang Zhao; Tengfei Li; Tinsu Pan; Linghua Wang; Yingyan Lin; Zhangyang Wang; David Jaffray; John D Hazle; Homer A Macapinlac; Jia Wu; Yang Lu Journal: Clin Nucl Med Date: 2022-03-01 Impact factor: 7.794