Multiparameter magnetic resonance imaging-based radiomics model for the preoperative prediction of lymphovascular invasion in rectal cancer
Xie Yuying1, Cui Yanfen2, Yang Xiaotang2, Quan Shuai3, Peng Kun4
1Department of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China; 2Department of Radiology, Affiliated Cancer Hospital of Shanxi Medical University, Shanxi Province Tumor Hospital, Taiyuan 030000, China; 3GE Healthcare China, Shanghai 200000, China; 4Department of MR, the Sixth Hospital of Shanxi Medical University, Taiyuan 030008, China
Abstract：Objective This study aims to investigate the application value of a fusion model based on multiparameter magnetic resonance imaging (MRI) for the preoperative prediction of lymphovascular invasion (LVI) in rectal cancer. Methods Retropective cohort study was conduted. The clinicopathological data and multi-parameter MRI data of 224 patients with rectal cancer who underwent radical resection for rectal cancer in Shanxi Province Tumor Hospital from January 2016 to December 2019 were analyzed, including 129 males and 95 females, aged 28 to 83 (61.3±9.7) years old. The patients were randomly divided into two groups, namely, the training group (n=157) and the validation group (n=67), According to a ratio of 7∶3. ITK-SNAP image segmentation was used to manually delineate the ROI of tumor slice by slice on the images of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and enhanced T1-weighted imaging (cT1WI) sequences to obtain the volume of interest. The delineation information of DWI was copied onto the apparent diffusion coefficient (ADC) map. A three-step dimensionality reduction method based on the maximum relevance minimum redundancy, least absolute shrinkage and selection operator regression, and multiple logistic regression was used for feature selection and radiomics signature building. Independent predictors of clinicopathologic features and MRI features were screened by multivariate logistic regression analysis. A radiomic model based on single and combined sequences of T2WI, ADC, and cT1WI and fusion models with clinicopathological features were constructed, and the corresponding nomogram was made. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve were used to evaluate the efficacy and clinical benefit of the model. Results Postoperative pathological examination confirmed LVI in 70 patients and negative in 154 patients. No significant differences in clinicopathologic features and MRI findings were observed between the training and validation groups (all P values > 0.05). Six key features related to LVI of rectal cancer were obtained after three-step screening (all P values < 0.05). Carcinoembryonic antigen (CEA) was an independent predictor of colorectal cancer (odds ratio[95% confidence interval] 2.071 [1.038~4.131], P = 0.039). The AUC of the radiomics model based on single and combined sequences of T2WI, ADC, and cT1WI were 0.765, 0.772, 0.776, and 0.878 in the training group and 0.741, 0.739, 0.764, and 0.846 in the validation group,respectively. The AUC of the fusion model training group and validation group constructed by CEA were 0.899 and 0.876, respectively, which showed the best prediction efficiency. The calibration curve showed that the fusion model had a good calibration performance. The decision curve of the verification group showed that the fusion model had the maximum net benefit when the threshold probability ranged from 0.10 to 0.20 and from 0.35 to 0.90. Conclusion The fusion model constructed based on the radiomic features of multi-parameter MRI and CEA has high diagnostic efficacy in predicting LVI of rectal cancer before surgery. The visual nomogram of this model can be used as an effective tool for predicting LVI before surgery.
谢玉莹, 崔艳芬, 杨晓棠, 全帅, 彭琨. 基于多参数MRI的影像组学融合模型对直肠癌脉管侵袭的术前预测价值[J]. 中华解剖与临床杂志, 2024, 29(2): 97-104.
Xie Yuying, Cui Yanfen, Yang Xiaotang, Quan Shuai, Peng Kun. Multiparameter magnetic resonance imaging-based radiomics model for the preoperative prediction of lymphovascular invasion in rectal cancer. Chinese Journal of Anatomy and Clinics, 2024, 29(2): 97-104.
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2024, Vol. 29 
