Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas

Abstract

Background: Although World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy.

Objective: In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas.

Methods: A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76).

Results: An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors.

Conclusion: Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.

Keywords: Artificial intelligence; Machine learning; Meningioma; Radiomics.

FIGURE 1.

A schematic of the process workflow used to develop a machine learning model to predict Ki-67 proliferative index in WHO grade I meningioma.

FIGURE 2.

Spatial atlases in the axial, sagittal, and coronal planes illustrating the distribution of 235 nonskull base (top panel) and 71 skull base (bottom panel) meningiomas used to train a machine learning model.

FIGURE 3.

Distribution of tumor and perilesional edema volumes reveal a wide range of overlap between Ki-67 <5% and ≥5% in WHO grade I meningiomas.

FIGURE 4.

Receiver operating characteristic curves of a machine learning model trained using the entire dataset of n = 306 patients who underwent resection of WHO grade I meningioma, dichotomized based on Ki-67 <5% and ≥5%. A, n = 230 discovery cohort (AUC: 0.84, 95% CI 0.78-0.90) and B, n = 76 validation cohort (AUC: 0.83, 95% CI 0.73-0.94). The model was applied to C, skull base (AUC: 0.86, 95% CI 0.79-0.98) and D, nonskull base tumors (AUC: 0.83, 95% CI 0.76-0.89), with similar efficacies achieved.

FIGURE 5.

Illustrative cases showcasing the radiomic phenotypes of 2 patients with sphenoid wing meningioma: patient 1 with Ki-67 of 17% and patient 2 with Ki-67 of 2.8%. A, T1 + C image shows similar hyperintensity albeit ADC maps with lower ADC values in the tumor with high Ki-67 compared to the tumor with lower Ki-67. B, Normalized histograms of T1-subtraction (T1 + C minus T1 intensity) and ADC map between patients, showing an association of higher T1-subtraction intensity (ie, increased permeability), and lower ADC values (ie, restricted diffusion or increased cellular density) within the tumorous region of with higher Ki-67.