• Single contrast-enhanced MRI enables meningioma growth risk prediction. • Deep network features and SVM obtain F1-scores up to 0.97 on 192 single-tumor cases. • High accuracy for small tumors; medium-sized tumors remain challenging to classify. • Performance varies by vendor, field strength, and location; 3 T performs better. • Non-invasive AI-driven single-scan assessment complements MRI monitoring pathway. Neurosurgical strategies for monitoring meningiomas and evaluating their growth risk largely rely on serial imaging or invasive sampling, practices that place considerable burdens on both patients and clinical resources. In this study, we present a novel framework for predicting meningioma growth risk using only a single contrast-enhanced MRI scan. Our approach compares a custom-trained fully convolutional neural network encoder and PyRadiomics features at both tumor- and whole-image scale, capturing tumor-specific and peritumoral image features, evaluated using conventional machine learning classifiers. The study cohort included 192 patients with single meningiomas, categorized as growing, stable, or shrinking, based on volumetric assessments by expert neurosurgeons. Classifiers trained on encoder-derived features achieved the highest F1-scores of 0.97 ± 0.01, demonstrating strong predictive performance particularly when edema content was included. Ensemble learning on encoder- and PyRadiomics-extracted features did not improve accuracy compared to the individual approaches. Prediction performance varied across scanner vendor, field strength, tumor location, and volume quintiles, with 3 T scanners yielding superior results, notably higher accuracy for smaller tumors under 1.73 cm 3 , and comparatively reduced performance in foramen magnum and intraventricular regions. This represents an important advance with clear clinical relevance, as smaller tumors are more difficult to classify regarding future growth. Our findings establish the feasibility of predicting meningioma growth risk from a single MRI scan, offering a non-invasive approach for early risk stratification and personalized surveillance strategies. By reducing reliance on serial imaging, the approach has the potential to support informed clinical decisions while improving resource allocation and ensuring timely intervention.
Sadeghzadeh et al. (Sun,) studied this question.