A Machine Learning Framework to Predict IQ Decline Post-treatment in Pediatric Brain Cancer with Consideration of Computational Efficiency

Existing models for predicting intelligence quotient (IQ) for pediatric brain cancer patients after intensity-modulated radiation therapy (RT) are derived from statistical linear and nonlinear analysis. While those models are computationally efficient, they use oversimplified representations of a patient's overall brain features. They neglect other important and complex brain structure information, such as image segmentations of healthy tissue and the target to be treated.\\ Therefore, we develop a machine learning (ML) approach to predict post-RT IQ based on 57 pediatric cases. Our data-model pipeline allows us to train over 600 billion parameters. Such high-density processing pipeline requires a novel parallel computing framework for the training and tuning tasks. Our framework can tractably handle these computational requirements by utilizing 1) an extensive grid search fitting-training scheme on individual and ensemble ML models, 2) automated network morphism that optimizes neural network structure efficiently, and 3) hardware configurations for RAM, CPU and GPU environments. This framework is adaptable to most of the ML-RT applications and particularly useful in both high- and low-dimensional datasets shown in many clinical applications. In predicting high or low post-RT IQ deficit, our framework outperforms baseline models, achieving a test accuracy of 75\%, a mean training accuracy of 83.3\% (±13), recall $\geq75\%$, and precision-recall harmonic mean of 0.75 for pre-Treatment-Planning dataset. We also demonstrate that complex models like ensemble stack model and deep neural nets can be practical under certain conditions.

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