Using Machine Learning and Radiomics to Predict the Growth Rate of Vestibular Schwannomas
Hesheng Wang, Douglas Kondziolka, J. Thomas Roland, John Golfinos, Conor Grady
Introduction: Clinicians are not able to predict the growth-rate of a vestibular schwannoma (VS) by reviewing a standard MRI. Recently, the field of radiomics has enabled high-dimensional, quantitative datasets to be created from imaging obtained during routine clinical care.
Objective: This study investigates whether machine learning techniques can yield accurate predictions of volumetric growth-rate from radiomic data from MRIs of treatment-naïve VSs.
Methods: 212 patients diagnosed with unilateral VS between 2012 and 2018 underwent measurement of tumor volume on all pre-treatment MRIs. Two cohorts were formed from the 30 patients with the lowest (-20% to 10%) and the 40 patients with the highest (55% - 165%) annualized growth-rates, respectively. Pyradiomics was used to calculate histogram, shape, and texture parameters from ADC, CISS, T2 weighted, and post-contrast T1 weighted sequences, resulting in a total of 311 radiomic parameters per volume of interest. Two models predicting cohort membership, a random forest classifier (RFC) and a gradient boosted trees (XGBoost) algorithm, were then trained on a dataset containing the radiomic profiles of 25 patients from the low growth-rate and 35 from the high growth-rate cohort. The models were then tested against the radiomic profiles of the 10 patients withheld from the training group.
Results: Following tuning of hyperparameters, both models were able to predict individual tumor membership in the low-growth-rate or high growth-rate cohorts with 100% accuracy. Area under the receiver operating curve (ROC) curve (AUC) was 1.0 for both models.
Conclusion: Supervised machine learning techniques can predict growth-rate in VS based on radiomic parameters. External validation is warranted.