Doxorubicin Efficacy Prediction for Glioblastomas using Deep Learning and Differential Equations

Abstract

This paper presents a novel approach for predicting the efficacy of Doxorubicin treatment for glioblastoma. Glioblastomas' rapid growth places them among the most aggressive cancers, killing thousands of Americans every year. The rapid progression of glioblastoma coupled with the high cost of cranial imaging makes clinical decision-making uniquely challenging. Doxorubicin is a commonly used chemotherapy drug to treat glioblastomas. However, predicting the treatment's efficacy remains challenging and time-consuming. Inaccurate predictions can lead to ineffective treatments, severe side effects, and even death. To address this issue, a framework was developed that amalgamates deep learning and differential equations to accurately predict tumor volume growth over time. Specifically, a 2D U-net convolutional neural network (CNN) was employed to segment MRI brain tumor regions and obtain initial volumes. The Gompertz differential equation was then utilized to model the predicted tumor volume growth over time, achieving a mean absolute percent error of 4.98 %. The Gompertz model was modified to incorporate the cytotoxic effect of Doxorubicin treatment. The methodology predicted the final tumor volume of the tumor after being treated with Doxorubicin over multiple 21-day cycles, enabling us to predict the efficacy of treatment and identify patients who may benefit most from this therapy. A user-friendly web application was developed to allow users to input NIFTI files of MRI scans and receive as output a time-course prediction of tumor volume with and without chemotherapy treatment. This approach provides a prediction of Doxorubicin treatment efficacy and can improve patient outcomes and treatment plans.