Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?

被引:8
作者
Yuan, Yuan [1 ]
Lu, Haidi [1 ]
Ma, Xiaolu [1 ]
Chen, Fangying [1 ]
Zhang, Shaoting [1 ]
Xia, Yuwei [2 ]
Wang, Minjie [1 ]
Shao, Chengwei [1 ]
Lu, Jianping [1 ]
Shen, Fu [1 ]
机构
[1] Changhai Hosp, Dept Radiol, 168 Changhai Rd, Shanghai 200433, Peoples R China
[2] Huiying Med Technol Co Ltd, B2,Dongsheng Sci & Technol Pk, Beijing, Peoples R China
关键词
Rectal cancer; Radiomics; Magnetic resonance imaging; Machine learning; PREDICTION; GUIDELINES; DISTANCE; BRIDGE;
D O I
10.1007/s00261-022-03477-6
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
Purpose To determine whether rectal filling with ultrasound gel is clinically more beneficial in preoperative T staging of patients with rectal cancer (RC) using radiomics model based on magnetic resonance imaging (MRI). Methods A total of 94 RC patients were assigned to cohort 1 (leave-one-out cross-validation [LOO-CV] set) and 230 RC patients were assigned to cohort 2 (test set). Patients were grouped according to different pathological T stages. The radiomics features were extracted through high-resolution T2-weighted imaging for all volume of interests in the two cohorts. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. Model 1 (without rectal filling) and model 2 (with rectal filling) were constructed. LOO-CV was adopted for radiomics model building in cohort 1. Thereafter, the cohort 2 was used to test and verify the effectiveness of the two models. Results Totally, 204 patients were enrolled, including 60 cases in cohort 1 and 144 cases in cohort 2. Finally, seven optimal features with LASSO were selected to build model 1 and nine optimal features were used for model 2. The ROC curves showed an AUC of 0.806 and 0.946 for model 1 and model 2 in cohort 1, respectively, and an AUC of 0.783 and 0.920 for model 1 and model 2 in cohort 2, respectively (p = 0.021). Conclusion The radiomics model with rectal filling showed an advantage for differentiating T1 + 2 from T3 and had less inaccurate categories in the test cohort, suggesting that this model may be useful for T-stage evaluation.
引用
收藏
页码:1741 / 1749
页数:9
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