Interpretable deep-learning model for distinguishing tumor pseudoprogression from true progression using MRI imaging of glioblastoma patients

Glioblastoma multiforme (GBM) are extremely invasive cancers. As a response to radiation treatment, in many cases, a new or a progressing lesion can be observed in imaging studies which resolves without additional treatment. This phenomenon is referred to as pseudoprogression (PsP). In contrast to PsP, a True Progression (TP) represents an enlarging lesion that requires a change in the treatment. Distinguishing between PsP and TP is thus central to treatment choice and clinical management. In this paper we present a 3D convolutional neural network (CNN) trained on 3D MRI images from 114 GBM patients to distinguish between Psp and TP. The model performs with an AUCROC of 0.74, Peak geometric mean of specificity and sensitivity: 0.69, Brier Score: 0.22, Scaled Brier Score: 0.04. Our findings suggest further investigation of deep learning models trained on larger imaging datasets to build more robust and generalizable models for distinguishing between PsP and True Progression.