Diagnosing GBM Progression with and Without AI-Measured Longitudinal Volume Measurements: A Retrospective Observational Study

Glioblastomas (GBM) grow rapidly and infiltrate the cerebral parenchyma, leading to significant neurological morbidity and mortality. Early detection of tumor growth promotes early therapeutic interventions that delay neurological progression and possibly prolong survival times. This retrospective observational study evaluates the ability of AI-assisted volumetric analysis to correctly detect tumor progression in longitudinal studies of newly diagnosed GBM, compared to the standard clinical method of visual inspection by radiologists and neuro-oncologists. Fifteen of 56 patients met the inclusion and exclusion criteria. The dates of tumor progression were gathered from clinical reports. Longitudinal tumor volumes were calculated from automated segmentations by the MRIMath T1c AI followed by physician review using the MRIMath Smart manual contouring system. Growth by significant shifts in tumor volumes was detected by using the statistical method of the online change-of-point method. Our results demonstrate that automatic AI segmentation followed by human review detects tumor progression earlier than clinical notes in 4/15 patients at a median of 105 days. Furthermore, the longitudinal AI-measured volumes validated two cases of pseudoprogression as evidenced by subsequent volume stability. This study emphasizes the enhanced diagnostic accuracy achieved by incorporating volumetric data analysis into clinical decision-making. This can be accomplished by integrating efficient AI-powered segmentation and a streamlined human review system into the clinical workflow.