Abstract

Background

Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma.

Methods

Preoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier–based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019.

Results

Volume of peritumoral edema was an independent predictor of brain invasion (P < 0.001). The top 6 interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97; 95% CI: 0.95–0.98) and validation sets (AUC 0.91; 95% CI: 0.84–0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76; 95% CI: 0.66–0.86).

Conclusion

An imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.

中文翻译：

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广泛的瘤周水肿和脑-肿瘤界面 MRI 特征能够预测脑膜瘤的脑浸润：开发和验证

摘要

背景

脑膜瘤侵入脑部是 2016 年世界卫生组织分类中肿瘤异型性的独立标准，但尚未显示任何影像参数足以预测它。本研究的目的是开发和验证基于 MRI 的脑-肿瘤界面放射组学模型，以预测脑膜瘤对脑的侵袭。

方法

术前 T2 加权和对比增强 T1 加权成像数据来自 2012 年至 2017 年间 454 名患者（88 名脑侵犯患者）。从 1 cm 厚的肿瘤-脑界面中获得的 3222 个影像组学特征中进行特征选择区域使用最小绝对收缩和选择算子。瘤周水肿体积、年龄、性别和选定的放射组学特征用于构建基于随机森林分类器的诊断模型。使用 2018 年至 2019 年间 150 名患者（29 名脑浸润患者）的独立队列中的受试者工作特征曲线下面积 (AUC) 评估性能。

结果

瘤周水肿体积是脑浸润的独立预测因子（P < 0.001）。选择前 6 个界面放射组学特征加上肿瘤周围水肿的体积用于模型构建。组合模型在训练 (AUC 0.97; 95% CI: 0.95-0.98) 和验证集 (AUC 0.91; 95% CI: 0.84-0.98) 中表现出最高的脑侵袭预测性能，并提高了诊断性能。仅肿瘤周围水肿（AUC 0.76；95% CI：0.66–0.86）。

结论

结合界面放射组学和瘤周水肿的基于成像的模型可以帮助预测脑膜瘤对大脑的侵袭，并提高已知临床和成像参数的诊断性能。