基于磁共振成像的多区域影像组学用于术前预测脑膜瘤中Ki-67的表达：一项双中心研究

MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study.

作者信息

Luo Ming, Lin Guihan, Chen Duoning, Chen Weiyue, Xia Shuiwei, Hui Junguo, Chen Pengjun, Chen Minjiang, Ye Wangyang, Ji Jiansong

机构信息

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

Department of Neurosurgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.

出版信息

Front Neurol. 2025 Jul 24;16:1554539. doi: 10.3389/fneur.2025.1554539. eCollection 2025.

DOI:10.3389/fneur.2025.1554539

PMID:40777860

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12328361/

Abstract

BACKGROUND

High expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to develop a predictive model for the Ki-67 index in meningioma based on preoperative magnetic resonance imaging (MRI).

METHODS

This study included 196 patients from one center (internal cohort) and 92 patients from another center (external validation cohort). Meningioma had to have been pathologically confirmed for inclusion. The Ki-67 index was classified as high (Ki-67 ≥ 5%) and low (Ki-67 < 5%). The internal cohort was randomly assigned to training and validation sets at a 7:3 ratio. Radiomics features were selected from contrast-enhanced T1-weighted MRI using the least-absolute shrinkage and selection operator and random forest methods. Then, we constructed a predictive model based on the identified semantic and radiomics features, aiming to distinguish high and low Ki-67 expression. The model's performance was evaluated through internal cross-validation and validated in the external cohort.

RESULTS

Among the clinical features, peritumoral edema ( = 0.001) and heterogeneous enhancement ( = 0.001) were independent predictors of the Ki-67 index in meningiomas. The radiomics model using a combined 8 mm volume of interest demonstrated optimal performance in the training (area under the receiver operating characteristic curve [AUC] = 0.883) and validation (AUC = 0.811) sets. A nomogram integrating clinical and radiomic features was constructed, achieving an AUC of 0.904 and enhancing the model's predictive accuracy for high Ki-67 expression.

CONCLUSION

This study developed clinical-radiomic models to non-invasively predict Ki-67 expression in meningioma and provided a novel preoperative strategy for assessing tumor proliferation.

摘要

背景

脑膜瘤中Ki-67的高表达与更高的组织学分级及更差的预后显著相关。对脑膜瘤中Ki-67表达水平进行非侵入性动态评估具有重要临床意义且迫切需要。本研究旨在基于术前磁共振成像（MRI）建立脑膜瘤Ki-67指数的预测模型。

方法

本研究纳入了来自一个中心的196例患者（内部队列）和来自另一个中心的92例患者（外部验证队列）。脑膜瘤必须经病理证实方可纳入。Ki-67指数分为高（Ki-67≥5%）和低（Ki-67<5%）。内部队列以7:3的比例随机分为训练集和验证集。使用最小绝对收缩和选择算子及随机森林方法从增强T1加权MRI中选择放射组学特征。然后，我们基于识别出的语义和放射组学特征构建了一个预测模型，旨在区分Ki-67的高表达和低表达。通过内部交叉验证评估模型性能，并在外部队列中进行验证。

结果

在临床特征中，瘤周水肿（=0.001）和不均匀强化（=0.001）是脑膜瘤Ki-67指数的独立预测因素。使用8mm感兴趣体积组合的放射组学模型在训练集（受试者操作特征曲线下面积[AUC]=0.883）和验证集（AUC=0.811）中表现最佳。构建了一个整合临床和放射组学特征的列线图，AUC为0.904，提高了模型对高Ki-67表达的预测准确性。