Integrated use of tumor AI volumetrics and advanced MRI to assess early response to Temferon in a phase 2 GBM trial: A novel paradigm.

Abstract

e14041 Background: mRANO criteria form the basis for assessing treatment response in GBM clinical trials. mRANO measurements show variability due to limited sampling of the tumor morphology and reader estimations. Volumetrics may overcome limitations of linear measurements and improve our ability to detect changes reliably. Advanced MRI shed light on the mechanism of action and provide biomarkers, whose changes may precede any morphological alterations in the tumor. Methods: Six consecutive patients (3M, 3F, mean age 53.5 yrs) from an ongoing Phase-2 study on the effect of Temferon on recurrent GBM, were included and 3 consecutive MRI follow-up time points from each patient were entered in this interim analysis. The mRANO response status was determined by a central reader. Integrated volumetrics, two perfusion modalities (DSC/DCE) and diffusion analysis were performed on a proprietary platform after co-registering data in a common space to eliminate user bias and variability. Volumetrics and perfusion/diffusion biomarkers were estimated using state-of-the-art Artificial Intelligence (AI)-empowered algorithms. Statistical significance was set at p < 0.001. Results: At the time of the interim analysis, 3 patients experienced stable disease (SD) / partial response (pR) and 3 patients had progressive disease (PD). The tumor volume had significant correlation (r = 0.89) with the SPDP but the SPDP showed monotonously biased overestimation of the tumor burden in the Bland-Altman analysis with mean difference = 886 (95%CI: 64-1708). Changes assessed by SPDP in the tumor burden during the treatment were underestimated for low volume tumor and underestimated for sizeable tumors compared with the volumetrics. The optimal threshold to predict tumor progression was 26% volume increase (sensitivity/specificity: 67/93%, AUC = 0.85), whereas there was no statistically significant threshold for SPDP. The perfusion fractional tumor volumes (FTV) with low and intermediately elevated rCBV showed strong correlation with the SPDP and enhancement volumetrics (r = 0.92-0.98). ROC analysis showed that 14% increase in the high perfusion tumor could significantly predict progression (sensitivity/specificity: 79/71%, AUC = 0.71). The responders showed significant increase in mean DWI values (11±0.3%) vs decrease (-4±0.05%) in non-responders. The mean rCBV and Vp values in responders showed decrease (-3±0.45% and -33±0.1%) and were increased in non-responders (25±0.1% and 51±0.66%). Conclusions: We demonstrated significant bias in estimating treatment response by means of mRANO compared to tumor volumetrics. Distinct patterns of changes in tumor perfusion, diffusion and FTV correlated highly with treatment response and enhancing tumor burden. Volumetrics and perfusion/diffusion composite biomarkers predicted accurately the response status in our patients. Clinical trial information: NCT03866109.