Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT

Journal of Translational Medicine

Lisheng Wang¹  Ziyi Yang²  Junyan Xu²  Shaoli Song²  Lilang Lv³  Xiaomao Guo³  Bowen Xin⁴  Yichao Hao⁴  Xiuying Wang⁴ 

[1] Department of Automation, Shanghai Jiao Tong University, 200240, Shanghai, China;Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, No.270 Dong’an Road, Xuhui district, 200032, Shanghai, China;Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;Center for Biomedical Imaging, Fudan University, Shanghai, China;Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China;Department of Radiotherapy, Fudan University Shanghai Cancer Center, No.270 Dong’an Road, Xuhui district, 200032, Shanghai, China;Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China;School of Computer Science, The University of Sydney, Sydney, NSW, Australia;

关键词: Stage III Colorectal cancer;    F-FDG PET/CT;    Radiomics;    Prognosis;    Prediction;   

DOI  :  10.1186/s12967-022-03262-5

来源: Springer

【 摘 要 】

BackgroundTo develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.MethodsA total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.ResultsRadiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.ConclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer.Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.

【 授权许可】

CC BY