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

Minh P. NguyenDepartment of Neurological Surgery, University of California, San Francisco;
School of Medicine, University of California, San Francisco;

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Ramin A. MorshedDepartment of Neurological Surgery, University of California, San Francisco;

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Cecilia L. Dalle OreDepartment of Neurological Surgery, University of California, San Francisco;

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Daniel D. CumminsDepartment of Neurological Surgery, University of California, San Francisco;
School of Medicine, University of California, San Francisco;

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Satvir SaggiDepartment of Neurological Surgery, University of California, San Francisco;
School of Medicine, University of California, San Francisco;

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William C. ChenDepartment of Radiation Oncology, University of California, San Francisco;

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Abrar ChoudhurySchool of Medicine, University of California, San Francisco;

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Akshay RaviDepartment of Hospital Medicine, University of California, San Francisco, California;

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David R. RaleighDepartment of Radiation Oncology, University of California, San Francisco;

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Stephen T. MagillDepartment of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and

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Michael W. McDermottDivision of Neurosurgery, Miami Neuroscience Institute, Miami, Florida

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Philip V. TheodosopoulosDepartment of Neurological Surgery, University of California, San Francisco;

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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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Figure from Ramos et al. (pp 95–103).

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