Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer

Frontiers in Oncology

Siyun Liu¹  Bin Song¹  Chencui Huang²  Min Wu²  Yumei Jin³  Mou Li⁴  Yali Zhao⁵  Shengmei Liu⁵ 

[1] League of PHD Technology Co., Ltd, Beijing, China;;D Center, Beijing Deepwise &Department of MRI, Qujing First People’s Hospital, Qujing, China;Department of Radiology, West China Hospital of Sichuan University, Chengdu, China;;Department of Research Collaboration, R&

关键词: tumor deposits;    rectal cancer;    radiomics;    computed tomography;    preoperative prediction;   

DOI  :  10.3389/fonc.2021.710248

来源: DOAJ

【 摘 要 】

ObjectiveTo develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC).MethodsThis retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).ResultsOne hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P < 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039).ConclusionsThe combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.

【 授权许可】

Unknown