NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING

Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU.

METHODS

In a prospective clinical study, weekly mpMRIs (post-contrast T1, T2 FLAIR, and diffusion) for patients with HGG receiving CRT were used to describe tumor extent and cellularity. This data collected from baseline (pre-CRT) till week 3 (mid-CRT) was used to calibrate a model family to forecast HGG response for each individual patient at week 6 (end CRT) and at 3-month FU.

RESULTS

Error between the forecasted and observed responses was assessed globally using percent error in tumor volume, and at the local level by Pearson correlation coefficient (PCC). In an initial cohort of 11 patients, our MM framework predictions had a percent error in tumor volume of less than 8.6% and at week 6 RT and less than 20% at 3 months FU. The PCCs were 0.84 at week 6 RT and 0.72 at 3 months FU.

CONCLUSIONS

Temporal consistency across this early evaluation of the model predictions show promise of image-driven MM for HGG response forecasting to guide timely personalized assessment and adjustment of treatment.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Digoxin and cimetidine: investigation of the potential for a drug interaction

The potential for a pharmacokinetic interaction between digoxin and cimetidine was investigated in a series of studies. In a single-dose cross-over study in healthy volunteer subjects cimetidine increased the area under the plasma digoxin concentration curve and the peak plasma digoxin concentration. In a repeated-dose study in healthy volunteer subjects taking digoxin 0.25 mg daily, co-administration of cimetidine resulted in an average increase in plasma digoxin concentration of 0.15 ng/ml. In a repeated-dose study in healthy volunteer subjects taking digoxin 0.5 mg daily, co-administration of cimetidine resulted in an average increase in plasma digoxin concentration of 0.19 ng/ml. In a repeated-dose study in patients receiving long-term digoxin therapy for atrial fibrillation co-administration of cimetidine had no significant effect on plasma digoxin concentrations. We have shown that co-administration of cimetidine and digoxin in volunteer subjects causes a statistically significant but small increase in plasma digoxin concentration but no such increase was found in patients. We conclude that it is doubtful that this interaction is of any clinical significance.

Assessment of a new calendar pack--the 'C-Pak'

Seventy-eight elderly patients in hospital were studied for up to four weeks to assess drug compliance. Forty patients received medication from individualized calendar packs ('C-Pak') and 38 received medication from standard bottles. There was no difference in compliance between the two groups, the percentage error for each group being 26%. This result suggests that C-Pak is unlikely to improve drug compliance in unselected elderly patients.