• 026974

    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.

We use cookies to improve the performance of our site, to analyze the traffic to our site, and to personalize your experience of the site. You can control cookies through your browser settings. Please find more information on the cookies used on our site here. Privacy Policy