Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine

Zonglin Liu; Yueming Wang; Fu Shen; Zhiyuan Zhang; Jing Gong; Caixia Fu; Changqing Shen; Rong Li; Guodong Jing; Sanjun Cai; Zhen Zhang; Yiqun Sun; Tong Tong

doi:10.1007/s13167-022-00303-3

Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine

EPMA J. 2022 Nov 12;13(4):633-647. doi: 10.1007/s13167-022-00303-3. eCollection 2022 Dec.

Authors

Zonglin Liu^#^{1

2}, Yueming Wang^#³, Fu Shen^#⁴, Zhiyuan Zhang^{2

5}, Jing Gong^{1

2}, Caixia Fu^{1

6}, Changqing Shen¹, Rong Li¹, Guodong Jing⁴, Sanjun Cai^{2

7}, Zhen Zhang^{2

5}, Yiqun Sun^{1

2}, Tong Tong^{1

2}

Affiliations

¹ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Anatomy and Physiology, Shanghai JiaoTong University School of Medicine, Shanghai, China.
⁴ Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China.
⁵ Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
⁶ MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China.
⁷ Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.

^# Contributed equally.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00303-3.

Keywords: Diffusion-weighted imaging; Machine learning; Predictive preventive personalized medicine; Readout-segmented echo-planar imaging; Rectal cancer; Recurrence or metastasis.