Author:
Liu Zonglin,Wang Yueming,Shen Fu,Zhang Zhiyuan,Gong Jing,Fu Caixia,Shen Changqing,Li Rong,Jing Guodong,Cai Sanjun,Zhang Zhen,Sun Yiqun,Tong Tong
Abstract
Abstract
Background
Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient.
Aims
This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment.
Methods
A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM.
Findings
The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80–0.93] vs. 0.71 [95% CI: 0.59–0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76–0.90] vs. 0.68 [95% CI: 0.56–0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976–29.853], p < 0.001; HR = 6.427 [95% CI: 2.265–13.036], p = 0.002).
Conclusion
Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Publisher
Springer Science and Business Media LLC
Subject
Biochemistry (medical),Health Policy,Drug Discovery
Cited by
3 articles.
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