Multi-parametric MRI radiomics-based nomogram model for predicting the lymphovascular space invasion of endometrial endometrioid adenocarcinoma
10.3969/j.issn.1672-8467.2024.03.003
- VernacularTitle:多参数MRI组学列线图模型预测子宫内膜样腺癌淋巴血管间隙侵犯
- Author:
Xiao-Liang MA
1
;
Min-Hua SHEN
;
Feng-Hua MA
;
Guo-Fu ZHANG
;
Jian-Jun ZHOU
;
Meng-Su ZENG
;
Jin-Wei QIANG
Author Information
1. 复旦大学附属中山医院放射科 上海 200032;复旦大学附属金山医院放射科 上海 200540
- Keywords:
radiomics;
magnetic resonance imaging;
endometrial cancer;
lymphovascular space invasion;
nomogram
- From:
Fudan University Journal of Medical Sciences
2024;51(3):306-314,322
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the feasibility and value of a multi-parametric MRI radiomics-based nomogram model for pretreatment predicting the lymphovascular space invasion(LVSI)of endometrial endometrioid adenocarcinoma(EEA).Methods Preoperative MRI and baseline clinical characteristics of 205 EEA patients were prospectively collected from Oct 2020 to Jan 2022 in the Obstetrics and Gynecology Hospital,Fudan University,and randomly divided into training set(n=123)and validation set(n=82)in a 6∶4 ratio.The whole-tumor region of interest was manually drawn on T2-weighted imaging,diffusion-weighted imaging(apparent diffusion coefficient),and dynamic contrast-enhanced MRI,respectively,for radiomics features extraction.In the training set,univariate and multivariate Logistic regression analysis were used to select independent clinical predictors of LVSI(+)and construct the clinical model.The least absolute shrinkage and selection operator(LASSO)regression and multivariate Logistic regression analysis were used to select optimal radiomics features to form a radiomics signature.A combined nomogram model was established by integrating clinical independent predictors and the radiomics signature,and validated in the validation set.The predicting performance and clinical net benefit were evaluated by using the area under the receiver operating characteristic curve(AUC)and clinical decision curve analysis,respectively.Results Of the 205 EEA cases,144 cases were LVSI(-)and 61 cases were LVSI(+).Menopausal status,CA125,and CA199 were independent clinical predictors for the LVSI(+),and contributing to a clinical model with AUCs of 0.714(training)and 0.731(validation).From 8 240 extracted radiomics features,five were selected to construct a MRI radiomics signature after de-redundancy and LASSO dimensionality reduction,yielding AUCs of 0.860(training)and 0.759(validation).The combined nomogram model showed AUCs of 0.887(training)and 0.807(validation),outperforming others and achieving maximum clinical benefit in a large range of threshold probability in both training and validation sets.Conclusion The multi-parametric MRI-based nomogram model has the potential for pretreatment predicting the LVSI status of EEA,providing valuable information for clinical management decision-making and improving patient's clinical benefits.
