Abstract

BACKGROUND

Although World Health Organization (WHO) grade I meningiomas are considered “benign” tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy.

OBJECTIVE

In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas.

METHODS

A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76).

RESULTS

An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors.

CONCLUSION

Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.

中文翻译：

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使用多参数磁共振成像放射特征分析的机器学习预测世界卫生组织 I 级脑膜瘤中的 Ki-67

摘要

背景

尽管世界卫生组织 (WHO) I 级脑膜瘤被认为是“良性”肿瘤，但 Ki-67 升高是影响肿瘤行为和临床结果的关键因素之一。术前识别 Ki-67 的能力将赋予指导手术策略的能力。

客观的

在这项研究中，我们开发了一种机器学习 (ML) 算法，使用放射学特征分析来预测 WHO I 级脑膜瘤中的 Ki-67。

方法

对 306 名接受 WHO I 级脑膜瘤手术切除的患者进行了回顾性分析。术前磁共振成像用于执行放射组学特征提取，然后使用最小绝对收缩和选择算子通过发现队列（n = 230）上的嵌套交叉验证包裹支持向量机进行 ML 建模，以基于 Ki-67 对肿瘤进行分层<5% 和 ≥5%。最终模型在复制队列（n = 76）上进行了独立测试。

结果

在发现队列中，受试者工作曲线下面积 (AUC) 为 0.84（95% CI：0.78-0.90），敏感性为 84.1%，特异性为 73.3%。当将该模型应用于复制队列时，实现了类似的高性能，AUC 为 0.83（95% CI：0.73-0.94），敏感性和特异性分别为 82.6% 和 85.5%。该模型在应用于颅底和非颅底肿瘤时表现出相似的功效。

结论

我们提出的放射组学特征分析可用于基于 Ki-67 对 WHO I 级脑膜瘤进行分层，具有出色的准确性，并可应用于颅底和非颅底肿瘤，并获得类似的性能。