基于多参数磁共振成像的机器学习影像组学模型预测脑膜瘤中孕激素受体表达的开发与验证：一项多中心研究

Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

作者信息

Lin Guihan, Chen Weiyue, Chen Yongjun, Shi Changsheng, Cao Qianqian, Jing Yang, Hu Weiming, Zhao Ting, Chen Pengjun, Yan Zhihan, Chen Minjiang, Lu Chenying, Xia Shuiwei, Ji Jiansong

机构信息

Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.

Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.

出版信息

Acad Radiol. 2025 Apr;32(4):2182-2196. doi: 10.1016/j.acra.2024.11.019. Epub 2024 Nov 29.

DOI:10.1016/j.acra.2024.11.019

PMID:39613583

Abstract

RATIONALE AND OBJECTIVES

This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier analysis.

RESULTS

Among the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The recurrence-free survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes.

CONCLUSION

The XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients.

摘要

原理与目的

本研究旨在开发并验证一种基于机器学习的预测模型，用于术前利用多参数磁共振成像（MRI）预测脑膜瘤患者的孕激素受体（PR）表达。

材料与方法

本研究回顾性纳入了来自三个医疗中心的739例经病理证实的脑膜瘤患者，将他们分为四个队列：训练组（n = 294）、内部测试组（n = 126）、外部测试1组（n = 217）和外部测试2组（n = 102）。从T2加权和对比增强T1加权MRI图像中提取影像组学特征，随后进行特征选择。通过将最佳影像组学模型的影像组学评分（rad-scores）与临床预测指标相结合，开发了一种基于机器学习的联合模型。采用Shapley加法解释（SHAP）方法直观展示预测过程。使用Kaplan-Meier分析评估模型的预后价值。

结果

在739例患者中，299例（40.5%）经病理证实PR表达为阴性。从多参数MRI中选取了12个影像组学特征来构建影像组学模型。肿瘤位置和强化方式被确定为关键临床预测指标，并与rad-scores相结合，利用极端梯度提升（XGBoost）算法创建联合模型。联合模型显示出强大的准确性和稳健性，在训练组、内部测试组、外部测试1组和外部测试2组中的曲线下面积值分别为0.907、0.827、0.846和0.807。无复发生存分析表明，联合模型能够根据复发结果有效对患者进行分类。