CTNI-67. AI BASED PREDICTION OF CLINICAL TTFIELDS RESPONSE IN GLIOBLASTOMA PATIENTS (RETROSPECTIVE ANALYSIS)

Abstract

Abstract RESEARCH QUESTION Accurate and individualized prediction of response to therapies is central to precision medicine and a major goal in neuro-oncology. An extension of overall survival (OS) and progression-free survival (PFS) was observed in patients treated with tumor treating fields (TTFields) plus maintenance temozolomide compared to those treated with maintenance temozolomide alone and TTFields have become an integral part of the treatment of glioblastoma. To identify patients who benefit most from TTFields, we aim to implement a state-of-the-art machine learning approach to create a model capable of identifying responding patients early in treatment. METHODOLOGY In this retrospective analysis, patients with IDH-wildtype glioblastoma treated with TTFields, in addition to RT and temozolomide, as first-line therapy are included. Clinical data and MRI raw data (in DICOM format) are collected. The preprocessing of the MRI data includes reorientation, bias field correction, registration, segmentation, and intensity normalization. This is followed by the extraction of a Radiomics signature. The latter, along with the clinical data, is trained using various machine learning models. The model validation occurs on an independent test cohort. Data collection from various neuro-oncological centers across Europe is ongoing. RESULTS So far, data from 134 TTFields patients and 131 control patients have been collected. Initial results, providing detailed insights into the model architecture and performance, will be presented at the time of the SNO meeting. CONCLUSION This study has the potential to optimize the treatment of glioblastoma patients by using machine learning to allow the early identification of patients that respond to first line therapy.