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
Department of Neurosurgery, Kepler University Hospital, Johannes Kepler University, Linz , Austria.
Institute of Neuro-Oncology, Kepler University Hospital, Linz , Austria.
Oper Neurosurg (Hagerstown). 2024 Jun 1;26(6):645-654. doi: 10.1227/ons.0000000000001023. Epub 2023 Dec 22.
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.
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.
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.
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.
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。
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。
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。
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。