Supervised machine learning algorithms demonstrate proliferation index correlates with long-term recurrence after complete resection of WHO grade I meningioma

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  • 1 Department of Neurological Surgery, University of California, San Francisco;
  • | 2 School of Medicine, University of California, San Francisco;
  • | 3 Department of Radiation Oncology, University of California, San Francisco;
  • | 4 Department of Hospital Medicine, University of California, San Francisco, California;
  • | 5 Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and
  • | 6 Division of Neurosurgery, Miami Neuroscience Institute, Miami, Florida
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OBJECTIVE

Meningiomas are the most common primary intracranial tumor, and resection is a mainstay of treatment. It is unclear what duration of imaging follow-up is reasonable for WHO grade I meningiomas undergoing complete resection. This study examined recurrence rates, timing of recurrence, and risk factors for recurrence in patients undergoing a complete resection (as defined by both postoperative MRI and intraoperative impression) of WHO grade I meningiomas.

METHODS

The authors conducted a retrospective, single-center study examining recurrence risk for adult patients with a single intracranial meningioma that underwent complete resection. Uni- and multivariate nominal logistic regression and Cox proportional hazards analyses were performed to identify variables associated with recurrence and time to recurrence. Two supervised machine learning algorithms were then implemented to confirm factors within the cohort that were associated with recurrence.

RESULTS

The cohort consisted of 823 patients who met inclusion criteria, and 56 patients (6.8%) had recurrence on imaging follow-up. The median age of the cohort was 56 years, and 77.4% of patients were female. The median duration of head imaging follow-up for the entire cohort was 2.7 years, but for the subgroup of patients who had a recurrence, the median follow-up was 10.1 years. Estimated 1-, 5-, 10-, and 15-year recurrence-free survival rates were 99.8% (95% confidence interval [CI] 98.8%–99.9%), 91.0% (95% CI 87.7%–93.6%), 83.6% (95% CI 78.6%–87.6%), and 77.3% (95% CI 69.7%–83.4%), respectively, for the entire cohort. On multivariate analysis, MIB-1 index (odds ratio [OR] per 1% increase: 1.34, 95% CI 1.13–1.58, p = 0.0003) and follow-up duration (OR per year: 1.12, 95% CI 1.03–1.21, p = 0.012) were both associated with recurrence. Gradient-boosted decision tree and random forest analyses both identified MIB-1 index as the main factor associated with recurrence, aside from length of imaging follow-up. For tumors with an MIB-1 index < 8, recurrences were documented up to 8 years after surgery. For tumors with an MIB-1 index ≥ 8, recurrences were documented up to 12 years following surgery.

CONCLUSIONS

Long-term imaging follow-up is important even after a complete resection of a meningioma. Higher MIB-1 labeling index is associated with greater risk of recurrence. Imaging screening for at least 8 years in patients with an MIB-1 index < 8 and at least 12 years for those with an MIB-1 index ≥ 8 may be needed to detect long-term recurrences.

ABBREVIATIONS

CI = confidence interval; GBDT = gradient-boosted decision tree; GTR = gross-total resection; HR = hazard ratio; OR = odds ratio; SRS = stereotactic radiosurgery.

Supplementary Materials

    • Supplemental Table 1 and Fig. 1 (PDF 684 KB)

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  • 1

    Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro Oncol. 2019;21(suppl 5):v1v100.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Withrow DR, Devesa SS, Deapen D, et al. Nonmalignant meningioma and vestibular schwannoma incidence trends in the United States, 2004-2017. Cancer. 127(19):3579-3590.

  • 3

    Agarwal V, McCutcheon BA, Hughes JD, et al. Trends in management of intracranial meningiomas: analysis of 49,921 cases from modern cohort. World Neurosurg. 2017;106:145151.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Champeaux C, Houston D, Dunn L, Resche-Rigon M. Intracranial WHO grade I meningioma: a competing risk analysis of progression and disease-specific survival. Acta Neurochir (Wien). 2019;161(12):25412549.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5

    Gallagher MJ, Jenkinson MD, Brodbelt AR, Mills SJ, Chavredakis E. WHO grade 1 meningioma recurrence: are location and Simpson grade still relevant? Clin Neurol Neurosurg. 2016;141:117121.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Haddad AF, Young JS, Kanungo I, et al. WHO grade I meningioma recurrence: identifying high risk patients using histopathological features and the MIB-1 index. Front Oncol. 2020;10:1522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7

    Rogers L, Barani I, Chamberlain M, et al. Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review. J Neurosurg. 2015;122(1):423.

  • 8

    Pettersson-Segerlind J, Orrego A, Lönn S, Mathiesen T. Long-term 25-year follow-up of surgically treated parasagittal meningiomas. World Neurosurg. 2011;76(6):564571.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Slot KM, Verbaan D, Bosscher L, Sanchez E, Vandertop WP, Peerdeman SM. Agreement between extent of meningioma resection based on surgical Simpson grade and based on postoperative magnetic resonance imaging findings. World Neurosurg. 2018;111:e856e862.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Spille DC, Hess K, Bormann E, et al. Risk of tumor recurrence in intracranial meningiomas: comparative analyses of the predictive value of the postoperative tumor volume and the Simpson classification. J Neurosurg. 2020;134(6):17641771.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Choudhury A, Magill ST, Eaton CD, et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat Genet. 2022;54(5):649659.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Formeister EJ, Baum R, Knott PD, et al. Machine learning for predicting complications in head and neck microvascular free tissue transfer. Laryngoscope. 2020;130(12):E843E849.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Fernandez-Lozano C, Hervella P, Mato-Abad V, et al. Random forest-based prediction of stroke outcome. Sci Rep. 2021;11(1):10071.

  • 14

    Liu N, Song SY, Jiang JB, Wang TJ, Yan CX. The prognostic role of Ki-67/MIB-1 in meningioma: a systematic review with meta-analysis. Medicine (Baltimore). 2020;99(9):e18644.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Li J, Liang R, Song C, Xiang Y, Liu Y. Prognostic value of Ki-67/MIB-1 expression in meningioma patients: a meta-analysis. Crit Rev Eukaryot Gene Expr. 2019;29(2):141150.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    de Carvalho GTC, da Silva-Martins WC, de Magalhães KCSF, et al. Recurrence/regrowth in Grade I meningioma: how to predict? Front Oncol. 2020;10:1144.

  • 17

    Nassiri F, Liu J, Patil V, et al. A clinically applicable integrative molecular classification of meningiomas. Nature. 2021;597(7874):119125.

  • 18

    Marciscano AE, Stemmer-Rachamimov AO, Niemierko A, et al. Benign meningiomas (WHO Grade I) with atypical histological features: correlation of histopathological features with clinical outcomes. J Neurosurg. 2016;124(1):106114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Oya S, Kawai K, Nakatomi H, Saito N. Significance of Simpson grading system in modern meningioma surgery: integration of the grade with MIB-1 labeling index as a key to predict the recurrence of WHO Grade I meningiomas. J Neurosurg. 2012;117(1):121128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    Booth TC, Thompson G, Bulbeck H, et al. A position statement on the utility of interval imaging in standard of care brain tumour management: defining the evidence gap and opportunities for future research. Front Oncol. 2021;11:620070.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Islim AI, Mohan M, Moon RDC, et al. Treatment outcomes of incidental intracranial meningiomas: results from the IMPACT cohort. World Neurosurg. 2020;138:e725e735.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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