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

被引:2
|
作者
Aichholzer, Martin [1 ]
Rauch, Philip [1 ,5 ,6 ,7 ]
Kastler, Lucia [1 ]
Pichler, Josef [2 ]
Aufschnaiter-Hiessboeck, Kathrin [1 ]
Ruiz-Navarro, Francisco [1 ]
Aspalter, Stefan [1 ]
Hartl, Saskia [1 ]
Schimetta, Wolfgang [3 ]
Boehm, Petra [1 ]
Manakov, Ilja [4 ]
Thomae, Wolfgang [1 ]
Gmeiner, Matthias [1 ]
Gruber, Andreas [1 ]
Stefanits, Harald [1 ]
机构
[1] Kepler Univ Hosp, Johannes Kepler Univ, Dept Neurosurg, Linz, Austria
[2] Kepler Univ Hosp, Inst Neurooncol, Linz, Austria
[3] Johannes Kepler Univ Linz, Inst Stat, Linz, Austria
[4] ImFusion GmbH, Munich, Germany
[5] Kepler Univ Hosp, Dept Neurosurg, Wagner Jauregg Weg 15, A-4020 Linz, Austria
[6] Johannes Kepler Univ Linz, Wagner Jauregg Weg 15, A-4020 Linz, Austria
[7] Alteberger Str 69, A-4040 Linz, Austria
关键词
Glioma; Intraoperative MRI; Machine learning; Neural network; Personalized medicine; Radiomics; 5-ALA; 5-AMINOLEVULINIC ACID; FLUORESCENCE;
D O I
10.1227/ons.0000000000001023
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:645 / 654
页数:10
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