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
相关论文
共 49 条
  • [41] A DEEP LEARNING-BASED METHOD FOR RAPID, PATIENT-SPECIFIC ASSAY OF MACROPHAGE INFILTRATION IN HIGH-GRADE GLIOMA USING LABEL-FREE STIMULATED RAMAN HISTOLOGY.
    Alber, Daniel
    Lock, Emily
    Sangwon, Karl
    Smith, Andrew
    Movahed-Ezazi, Misha
    Oermann, Eric
    Hollon, Todd
    Orringer, Daniel
    NEURO-ONCOLOGY, 2023, 25
  • [42] Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice
    Zhou, Jie
    JOURNAL OF TRANSLATIONAL MEDICINE, 2025, 23 (01)
  • [43] Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation
    Wu, Ruixin
    Chen, Sihao
    He, Yi
    Li, Ya
    Mu, Song
    Jin, Aishun
    FRONTIERS IN ONCOLOGY, 2025, 15
  • [44] A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery
    Zheng, Zhinan
    Huang, Yabin
    Zhao, Yingyin
    Shi, Jiankun
    Zhang, Shimin
    Zhao, Yang
    AMERICAN JOURNAL OF SURGERY, 2024, 237
  • [45] Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI
    Wang, Li
    Zhou, Li
    Liu, Shengdan
    Zheng, Yurong
    Liu, Qianhan
    Yu, Minglin
    Lu, Xiaofei
    Lei, Wei
    Chen, Guangxiang
    PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, 2024, 133
  • [46] Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques
    Guo, Wei
    She, Dejun
    Xing, Zhen
    Lin, Xiang
    Wang, Feng
    Song, Yang
    Cao, Dairong
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [47] T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas
    Tang, W. -t.
    Su, C. -q.
    Lin, J.
    Xia, Z. -w.
    Lu, S. S.
    Hong, X. -n.
    CLINICAL RADIOLOGY, 2024, 79 (05) : e750 - e758
  • [48] Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score
    Laios, Alexandros
    De Oliveira Silva, Raissa Vanessa
    Dantas De Freitas, Daniel Lucas
    Tan, Yong Sheng
    Saalmink, Gwendolyn
    Zubayraeva, Albina
    Johnson, Racheal
    Kaufmann, Angelika
    Otify, Mohammed
    Hutson, Richard
    Thangavelu, Amudha
    Broadhead, Tim
    Nugent, David
    Theophilou, Georgios
    Gomes de Lima, Kassio Michell
    De Jong, Diederick
    JOURNAL OF CLINICAL MEDICINE, 2022, 11 (01)
  • [49] Machine learning-based identification of determinants for rehabilitation success and future healthcare use prevention in patients with high-grade, chronic, nonspecific low back pain: an individual data 7-year follow-up analysis on 154,167 individuals
    Niederer, Daniel
    Schiller, Joerg
    Groneberg, David A.
    Behringer, Michael
    Wolfarth, Bernd
    Gabrys, Lars
    PAIN, 2024, 165 (04) : 772 - 784