MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study

TL;DR

This study uses MRI scans to predict Ki-67 levels in meningiomas, offering a non-invasive way to assess tumor aggressiveness before surgery.

Contribution

A novel clinical-radiomic model combining MRI features and clinical data to predict Ki-67 expression in meningiomas.

Findings

Peritumoral edema and heterogeneous enhancement were independent predictors of high Ki-67 expression.

The radiomics model achieved an AUC of 0.883 in training and 0.811 in validation.

A nomogram integrating clinical and radiomic features achieved an AUC of 0.904 for high Ki-67 prediction.

Abstract

High expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to develop a predictive model for the Ki-67 index in meningioma based on preoperative magnetic resonance imaging (MRI).

This study included 196 patients from one center (internal cohort) and 92 patients from another center (external validation cohort). Meningioma had to have been pathologically confirmed for inclusion. The Ki-67 index was classified as high (Ki-67 ≥ 5%) and low (Ki-67 < 5%). The internal cohort was randomly assigned to training and validation sets at a 7:3 ratio. Radiomics features were selected from contrast-enhanced T1-weighted MRI using the least-absolute shrinkage and selection operator and random forest methods. Then, we constructed a predictive model based on the identified semantic and radiomics features, aiming to distinguish high and low Ki-67 expression. The model’s performance was evaluated through internal cross-validation and validated in the external cohort.

Among the clinical features, peritumoral edema (p = 0.001) and heterogeneous enhancement (p = 0.001) were independent predictors of the Ki-67 index in meningiomas. The radiomics model using a combined 8 mm volume of interest demonstrated optimal performance in the training (area under the receiver operating characteristic curve [AUC] = 0.883) and validation (AUC = 0.811) sets. A nomogram integrating clinical and radiomic features was constructed, achieving an AUC of 0.904 and enhancing the model’s predictive accuracy for high Ki-67 expression.

This study developed clinical-radiomic models to non-invasively predict Ki-67 expression in meningioma and provided a novel preoperative strategy for assessing tumor proliferation.

Figures