T2WI MR-Based Radiomics Nomogram for Predicting Deep Stromal Invasion of Early-Stage Cervical Cancer
10.3969/j.issn.1005-5185.2024.09.012
- VernacularTitle:基于T2WI影像组学列线图预测早期宫颈癌深度间质浸润
- Author:
Huizhen SONG
1
;
Yu WANG
;
Maoyuan LI
;
Xue LI
;
Taoming DU
Author Information
1. 成都市第七人民医院(成都医学院附属肿瘤医院)放射科,四川成都 610000
- Keywords:
Uterine cervical neoplasms;
Magnetic resonance imaging;
Radiomics;
Nomogram;
Forecasting;
Pathology,surgical
- From:
Chinese Journal of Medical Imaging
2024;32(9):928-933
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To investigate the value of T2WI MR-based radiomics nomogram for predicting deep stromal invasion of cervical cancer preoperatively.Materials and Methods Retrospective analysis of 164 consecutive patients with early-stage cervical cancer with postoperative pathological findings and preoperative MR images admitted to two medical centers in the Affiliated Hospital of Southwest Medical University(first center)and the Huaihe Hospital of Henan University(second center)from May 2018 to August 2022.The data in the first center(n=1 14)and the second center(n=50)were divided into the training and validation cohorts,respectively.To segment T2WI images in the 3D Slicer software and to extract image features in the python software.The radiomic features were selected in the training cohort.Based on the selected features,support vector machine prediction model was constructed.Univariate Logistic regression was used to select clinicopathological risk factors,then,multi-variate Logistic regression combined with radiomics score was used to construct radiomics nomogram,diagnostic performance of the radiomics model,clinical prediction model and radiomics nomogram model were assessed by receiver operating characteristic analysis.The predictive efficacy of the different models were compared.Results The 12 radiomics features were selected out.The FIGO staging and radiomics score were included in the multifactor Logistic regression to build the radiomics nomogram for predicting deep stromal invasion.The results showed that the predictive performance for the radiomics nomogram model was better than the clinical prediction model(in validation cohort:area under the curve was 0.845 vs.0.717;Z=2.728,P=0.006).Conclusion Radiomics nomogram based on T2WI is of high value for predicting deep stromal invasion of cervical cancer preoperatively.
