Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study

被引:23
作者
Li, Zhi [1 ]
Zhang, Jing [2 ]
Zhong, Qi [3 ]
Feng, Zhan [1 ]
Shi, Yushu [1 ]
Xu, Ligong [1 ]
Zhang, Rui [1 ]
Yu, Fang [4 ]
Lv, Biao [5 ]
Yang, Tian [6 ]
Huang, Chencui [7 ]
Cui, Feng [3 ]
Chen, Feng [1 ]
机构
[1] Zhejiang Univ, Affiliated Hosp 1, Dept Radiol, Sch Med, Hangzhou, Zhejiang, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Radiol, Ruijin Hosp, Sch Med, Shanghai, Peoples R China
[3] Hangzhou Hosp Tradit Chinese Med, Dept Radiol, Hangzhou, Zhejiang, Peoples R China
[4] Zhejiang Univ, Affiliated Hosp 1, Dept Pathol, Sch Med, Hangzhou, Zhejiang, Peoples R China
[5] 903 Hosp Joint Logist Support Force PLA, Dept Radiol, Hangzhou, Zhejiang, Peoples R China
[6] Shulan Hangzhou Hosp, Dept Radiol, Hangzhou, Zhejiang, Peoples R China
[7] Beijing Deepwise & League PHD Technol Co Ltd, Dept Res Collaborat, R&D Ctr, Beijing, Peoples R China
关键词
MRI; Rectal cancer; Microsatellite instability; AUC; TUMOR HETEROGENEITY; COLORECTAL CANCERS; CHEMORADIOTHERAPY;
D O I
10.1007/s00330-022-09160-0
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients. Methods A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model. Results In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively. Conclusion The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients.
引用
收藏
页码:1835 / 1843
页数:9
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