Yuan Yuan, Lu Haidi, Ma Xiaolu, Chen Fangying, Zhang Shaoting, Xia Yuwei, Wang Minjie, Shao Chengwei, Lu Jianping, Shen Fu
Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China.
Huiying Medical Technology Co., Ltd, B2, Dongsheng Science and Technology Park, HaiDian District, Beijing, China.
Abdom Radiol (NY). 2022 May;47(5):1741-1749. doi: 10.1007/s00261-022-03477-6. Epub 2022 Mar 10.
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).
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.
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).
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.
使用基于磁共振成像(MRI)的放射组学模型,确定在直肠癌(RC)患者术前T分期中,直肠内填充超声凝胶在临床上是否更有益。
将94例RC患者分配到队列1(留一法交叉验证[LOO-CV]组),230例RC患者分配到队列2(测试组)。患者根据不同的病理T分期进行分组。通过高分辨率T2加权成像提取两个队列中所有感兴趣体积的放射组学特征。使用最小绝对收缩和选择算子(LASSO)算法选择最佳特征。构建模型1(不进行直肠填充)和模型2(进行直肠填充)。在队列1中采用LOO-CV进行放射组学模型构建。此后,使用队列2测试并验证这两个模型的有效性。
共纳入204例患者,其中队列1有60例,队列2有144例。最终,选择7个LASSO最佳特征构建模型1,9个最佳特征用于模型2。ROC曲线显示,队列1中模型1和模型2的AUC分别为0.806和0.946,队列2中模型1和模型2的AUC分别为0.783和0.920(p = 0.021)。
直肠填充的放射组学模型在区分T1+2和T3方面具有优势,且在测试队列中的错误分类较少,表明该模型可能有助于T分期评估。