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高级别胶质瘤手术中的定制术中MRI策略:基于机器学习的放射组学模型凸显了选择性益处。

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

作者信息

Aichholzer Martin, Rauch Philip, Kastler Lucia, Pichler Josef, Aufschnaiter-Hiessböck Kathrin, Ruiz-Navarro Francisco, Aspalter Stefan, Hartl Saskia, Schimetta Wolfgang, Böhm Petra, Manakov Ilja, Thomae Wolfgang, Gmeiner Matthias, Gruber Andreas, Stefanits Harald

机构信息

Department of Neurosurgery, Kepler University Hospital, Johannes Kepler University, Linz , Austria.

Institute of Neuro-Oncology, Kepler University Hospital, Linz , Austria.

出版信息

Oper Neurosurg (Hagerstown). 2024 Jun 1;26(6):645-654. doi: 10.1227/ons.0000000000001023. Epub 2023 Dec 22.

Abstract

BACKGROUND AND OBJECTIVES

In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy of iMRI and 5-aminolevulinic acid (5-ALA), questioning the continued justification of iMRI because of its associated costs and extended surgical duration. Nonetheless, drawing from our clinical observations, we postulated that a subset of intricate HGGs may continue to benefit from the adjunctive application of iMRI.

METHODS

In a prospective study of 73 patients with HGG, 5-ALA was the primary technique for tumor delineation, complemented by iMRI to detect residual contrast-enhanced regions. Suboptimal 5-ALA efficacy was defined when (1) iMRI detected contrast-enhanced remnants despite 5-ALA's indication of a gross total resection or (2) surgeons observed residual fluorescence, contrary to iMRI findings. Radiomic features from preoperative MRIs were extracted using a U2-Net deep learning algorithm. Binary logistic regression was then used to predict compromised 5-ALA performance.

RESULTS

Resections guided solely by 5-ALA achieved an average removal of 93.14% of contrast-enhancing tumors. This efficacy increased to 97% with iMRI integration, albeit not statistically significant. Notably, for tumors with suboptimal 5-ALA performance, iMRI's inclusion significantly improved resection outcomes ( P -value: .00013). The developed deep learning-based model accurately pinpointed these scenarios, and when enriched with radiomic parameters, showcased high predictive accuracy, as indicated by a Nagelkerke R 2 of 0.565 and a receiver operating characteristic of 0.901.

CONCLUSION

Our machine learning-driven radiomics approach predicts scenarios where 5-ALA alone may be suboptimal in HGG surgery compared with its combined use with iMRI. Although 5-ALA typically yields favorable results, our analyses reveal that HGGs characterized by significant volume, complex morphology, and left-sided location compromise the effectiveness of resections relying exclusively on 5-ALA. For these intricate cases, we advocate for the continued relevance of iMRI.

摘要

背景与目的

在高级别胶质瘤(HGG)手术中,术中磁共振成像(iMRI)传统上一直是实现肿瘤最大程度切除并改善患者预后的金标准。然而,最近的一级证据对比了iMRI与5-氨基酮戊酸(5-ALA)的疗效,由于其相关成本和手术时间延长,对iMRI的持续合理性提出了质疑。尽管如此,基于我们的临床观察,我们推测一部分复杂的HGG可能继续受益于iMRI的辅助应用。

方法

在一项对73例HGG患者的前瞻性研究中,5-ALA是肿瘤勾勒的主要技术,辅以iMRI检测残留的强化区域。当出现以下情况时定义为5-ALA疗效欠佳:(1)尽管5-ALA提示肿瘤已全切,但iMRI检测到强化残留;(2)外科医生观察到残留荧光,与iMRI结果相反。使用U2-Net深度学习算法从术前MRI中提取影像组学特征。然后使用二元逻辑回归预测5-ALA性能受损情况。

结果

仅由5-ALA引导的切除平均切除了93.14%的强化肿瘤。结合iMRI后,这一疗效提高到97%,尽管无统计学意义。值得注意的是,对于5-ALA性能欠佳的肿瘤,加入iMRI显著改善了切除效果(P值:0.00013)。所开发的基于深度学习的模型准确地确定了这些情况,当加入影像组学参数时,显示出高预测准确性,Nagelkerke R²为0.565,受试者工作特征曲线下面积为0.901。

结论

我们的机器学习驱动影像组学方法可预测在HGG手术中,与iMRI联合使用相比,单独使用5-ALA可能效果欠佳的情况。尽管5-ALA通常能产生良好结果,但我们的分析表明,体积大、形态复杂且位于左侧的HGG会影响仅依赖5-ALA的切除效果。对于这些复杂病例,我们主张iMRI仍具有相关性。

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