Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients

Cancer Imaging

Research Article

Debing Shi¹  Yue Li²  Menglei Li²  Tong Tong²  Hui Zhu²  Xun Yao³  Xiaozhu Lin⁴  Zhiyuan Wu⁴  Haoyan Chen⁵  Yanru Ma⁵  Muni Hu⁵ 

[1] Department of Colorectal Surgery, Department of Oncology, Fudan University Shanghai Cancer Center; Shanghai Medical College, Fudan University, 270 DongAn Road, 200032, Shanghai, China;Department of Diagnostic Radiology, Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, 270 DongAn Road, 200032, Shanghai, China;Department of Radiology, Peking University People’s Hospital, 11 Xizhimen South St, 100044, Beijing, China;Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin Er Rd, 200025, Shanghai, China;State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China;

关键词: Radiomics;    Computed tomography;    Prognosis;    Stage II colorectal cancer;    Adjuvant chemotherapy;   

DOI  :  10.1186/s40644-023-00588-1

 received in 2023-02-21, accepted in 2023-07-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOur study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC).MethodsA total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR).ResultsA radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17).ConclusionThe radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients.Trial registrationRetrospectively registered.

【 授权许可】

CC BY   
© International Cancer Imaging Society (ICIS) 2023

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