A radiomic nomogram based on T2WI for predicting synchronous liver metastasis of rectal cancer
10.3760/cma.j.issn.1005-1201.2019.03.009
- VernacularTitle:基于T2WI图像的影像组学列线图预测直肠癌同步肝转移的价值
- Author:
Zhenyu SHU
1
;
Songhua FANG
;
Yuan SHAO
;
Dewang MAO
;
Rui CHAI
;
Yuanjun CHEN
;
Xiangyang GONG
Author Information
1. 浙江省人民医院杭州医学院附属人民医院放射科
- Keywords:
Rectal neoplasms;
Radiomics;
Synchronous liver metastases;
Magnetic resonance images;
Nomogram
- From:
Chinese Journal of Radiology
2019;53(3):205-211
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the clinical feasibility of predicting synchronous liver metastases based on MRI radiomics nomogram based on T2WI in rectal cancer. Methods The imaging and clinical data of 261 patients with primary rectal cancer admitted to Zhejiang People′s Hospital from April 2012 to May 2018 were retrospectively analyzed. 101 patients were accompanied by synchronous liver metastasis All cases were divided into training group (n=182) and verification group (n=79). T2WI image of each patient was selected to extract texture features by AK analysis software of GE company. A radiomics signature was constructed after reduction of dimension in training group by the least absolute shrinkage and selection operator (LASSO). Univariate logistic regression was used to select for independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models and nomogram. ROC was used to assess the accuracy of the nomogram in the training group and to verify them by the validation group. Finally, the clinical efficacy of each patient′s synchronized liver metastasis risk factor was calculated based on the nomogram. Results A total of 328 texture features were extracted from the T2WI. Seven most valuable features were selected after reducing the dimension by LASSO algorithm, including 3 co-occurrence matrices (GLCM) and 4 run-length matrices(RLM). Tumor staging and radiomic signatures were included in the Multifactor logistic regression to build the prediction model and nomogram. The accuracy of predicting SRLM was 0.862 and 0.844 in the training and the verification group, respectively. To evaluate the accuracy of the nomogram, radiomics signature and the tumor staging in all cases were 0.857, 0.832 and 0.663, respectively. There was no significant difference in the number of SRLM cases between the high risk group and the low risk group based on nomogram (P>0.05). Conclusion The radiomics nomogram based on T2WI can be used as a quantitative tool to predict synchronous liver metastases of rectal cancer.
