Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery

2026-04-27

AI Summary

This study addresses the preoperative prediction of volumetric response in patients with skull base meningiomas following CyberKnife stereotactic radiosurgery. To achieve this, the authors integrate preoperative MRI-based radiomic features with clinical variables and develop six machine learning models, employing nested cross-validation to mitigate challenges posed by small sample sizes and high-dimensional data. Innovatively, tumor volume response is adopted as the primary efficacy endpoint instead of conventional progression-free survival. Among the evaluated models, TabPFN demonstrates superior performance with an AUC of 0.81 and consistently robust classification metrics, underscoring the method’s predictive validity and clinical potential even under data-limited conditions.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular structures make treatment selection challenging. Stereotactic radiosurgery with CyberKnife represents an effective therapeutic option when surgical resection is not feasible; however, not all patients benefit equally from this treatment. Early identification of patients likely to respond to radiosurgery remains an open clinical problem. In this study, we propose a radiomics- and clinical feature-driven framework for predicting volumetric response in skull-base meningiomas treated with CyberKnife. Unlike most existing approaches that focus on progression-free survival or recurrence, our method targets volumetric response as an indicator of treatment efficacy. Pre-treatment MRI images from 104 patients were processed to extract radiomic features, which were combined with clinical variables and analyzed using six models. To ensure methodological rigor, the entire modeling process was implemented within a nested cross-validation scheme. Among the evaluated models, TabPFN achieved the best overall performance, with an AUC of 0.81 and consistently favorable classification metrics. These results suggest that advanced machine learning architectures, when combined with robust validation strategies, can effectively capture patterns associated with treatment response even in small-sample, high-dimensional settings.

Problem

• skull-base meningioma
• volumetric response
• CyberKnife radiosurgery
• treatment prediction
• radiosurgery response

Innovation

• radiomics
• volumetric response
• CyberKnife radiosurgery
• TabPFN
• nested cross-validation