Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas
Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas
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DOI：
10.1093/neuros/nyab307
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发表时间：
2021-08-30
期刊：
影响因子：
4.8
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Khanna, Omaditya;Kazerooni, Anahita Fathi;Davatzikos, Christos
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的升高是影响肿瘤行为和临床预后的一个重要因素。目的：在这项研究中，我们开发了一种机器学习(ML)算法，利用放射学特征分析来预测WHO I级脑膜瘤的Ki-67。方法：对306例接受WHO I级脑膜瘤手术的患者进行回顾性分析。术前磁共振成像进行放射学特征提取，然后用最小绝对收缩和支持向量机包裹的选择算子对发现队列(n=230)进行嵌套交叉验证的最大似然建模，根据Ki-675%和>；=5%对肿瘤进行分层。结果：发现队列的受试者工作曲线下面积为0.84(95%CI：0.78~0.90)，灵敏度为84.1%，特异度为73.3%。当该模型应用于复制队列时，获得了类似的高性能，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%。该模型在颅底和非颅底肿瘤中的应用效果相似。结论：我们提出的放射学特征分析方法可用于WHO基于Ki-67的I级脑膜瘤的分级，具有很高的准确性，并可应用于颅底和非颅底肿瘤，具有类似的表现。
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.