NEIM-02 DEVELOPMENT OF A DEEP LEARNING MODEL FOR DISCRIMINATING TRUE PROGRESSION FROM PSEUDOPROGRESSION IN GLIOBLASTOMA PATIENTS

Abstract INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. Despite multimodal treatment, its median overall survival ranges between 16 and 20 months. The standard treatment regimen consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. Despite temozolomide’s effectiveness, it may cause the clinical challenge of treatment-related progression also known as pseudoprogression(PsP). Usually, PSP resolves or stabilizes without further treatment, whereas a true progression (TP) requires more aggressive management. Identifying PSP from TP will affect the patient’s treatment plan. Conventional magnetic resonance imaging (MRI) reading techniques cannot distinguish these entities. This study investigated the feasibility of using deep learning to distinguish PsP from TP. METHOD We included GBM patients who met our inclusion criteria. We evaluated all cases to see if they had a new enhancing lesion within the original radiation field or an increase in the size of an existing lesion. The challenging MRIs were collected. Clinical notes regarding tumor and recurrence location, clinical history, and medication were collected. We labeled the ones who stayed stable or improved in the imaging and clinical situation as PSP and those with further imaging and clinical deterioration as TP. We coregistered Contrast-enhanced-T1 MRIs with T2-weighted images for each patient. We performed five-fold cross-validation to generalize the performance. We trained A 3-D Densenet121 model to establish the prediction. We selected the best models with the highest accuracy. RESULT After reviewing 1000 patients, we included 124 patients whose imaging showed suspicious progression and their medicational histories were completely retrievable; 63 PsP, and 61 TP. We developed a deep learning model based on the whole dataset. The 5-fold cross-validation revealed that the mean area under the curve (AUC) was 0.81. CONCLUSION We report the development of a deep learning model that diagnoses PsP from TP in patients who received temozolomide. Further refinement and external validation are required prior to widespread adoption in clinical practice.