Speaker
Description
Accurate clinical assessment of a patient's response to treatment is a critical task in the era of precision medicine. Glioblastoma (GBM), a primary brain tumour with dismal median survival times of 12-16 months, has a highly heterogeneous, invasive profile, causing tumour dynamics and therapeutic response to vary widely from patient to patient. Currently, two sets of standard assessment criteria are used, RANO and iRANO (for standard radiation and chemotherapy versus immunotherapy, respectively), for evaluating changes in tumour size following treatment to determine whether a patient responded to that treatment. In addition, when complex imaging transformations, such as pseudo-progression, occur in response to therapy these metrics require long follow-up times of 3-6 months. Therefore, it has been difficult for physicians to use existing metrics to define optimal treatment schedules, including when to maintain or switch therapies.
Using the Proliferation-Invasion (PI) model, the unique kinetics of individual patients’ tumours can be simulated using derived rates of cell proliferation (ρ) and invasion (D) from serial magnetic resonance imaging (MRI). These models serve as untreated virtual controls of tumour growth, enabling comparisons against post-treatment imaging to generate a patient-specific “Days Gained” (DG) response metric. DG values were evaluated across a variety of primary and secondary therapies for thresholds discriminating long and short-term survivors.
Significant DG thresholds were found using follow-up imaging 1-2 months post therapy in patients receiving cytotoxic, radiation, and immunotherapeutic treatments. High DG thresholds were associated with better survival in standard radiation and chemotherapy treatments. In contrast, low DG values were discriminative of longer survival for immunotherapy due to the well known pseudo-progression effect of image-detectable inflammation response. The DG metric therefore demonstrated utility as a single, patient-specific method for judging response across multiple therapies with different response profiles that currently require distinct evaluation criteria (RANO and iRANO respectively). In addition, Days Gained was assessable in a similar or shorter time window than the existing metrics. Taken together, these data support the utility of the model-derived response metric, Days Gained, in a diversity of clinical settings to quantitatively connect response with patient survival.
