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Eva Wembacher-Schroeder, Nicole Kerstein, Evan D. Bander, Neeta Pandit-Taskar, Rowena Thomson, and Mark M. Souweidane

OBJECTIVE

With increasing use of convection-enhanced delivery (CED) of drugs, the need for software that can predict infusion distribution has grown. In the context of a phase I clinical trial for pediatric diffuse intrinsic pontine glioma (DIPG), CED was used to administer an anti-B7H3 radiolabeled monoclonal antibody, iodine-124–labeled omburtamab. In this study, the authors retrospectively evaluated a software algorithm (iPlan Flow) for the estimation of infusate distribution based on the planned catheter trajectory, infusion parameters, and patient-specific MRI. The actual infusate distribution, as determined on MRI and PET imaging, was compared to the distribution estimated by the software algorithm. Similarity metrics were used to quantify the agreement between predicted and actual distributions.

METHODS

Ten pediatric patients treated at the same dose level in the NCT01502917 trial conducted at Memorial Sloan Kettering Cancer Center were considered for this retrospective analysis. T2-weighted MRI in combination with PET imaging was used to determine the distribution of infusate in this study. The software algorithm was applied for the generation of estimated fluid distribution maps. Similarity measures included object volumes, intersection volume, union volume, Dice coefficient, volume difference, and the center and average surface distances. Acceptable similarity was defined as a simulated distribution volume (Vd Sim) object that had a Dice coefficient higher than or equal to 0.7, a false-negative rate (FNR) lower than 50%, and a positive predictive value (PPV) higher than 50% compared to the actual Vd (Vd PET).

RESULTS

Data for 10 patients with a mean infusion volume of 4.29 ml (range 3.84–4.48 ml) were available for software evaluation. The mean Vd Sim found to be covered by the actual PET distribution (PPV) was 77% ± 8%. The mean percentage of PET volume found to be outside the simulated volume (FNR) was 34% ± 10%. The mean Dice coefficient was 0.7 ± 0.05. In 8 out of 10 patients, the simulation algorithm fulfilled the combined acceptance criteria for similarity.

CONCLUSIONS

iPlan Flow software can be useful to support planning of trajectories that produce intraparenchymal convection. The simulation algorithm is able to model the likely infusate distribution for a CED treatment in DIPG patients. The combination of trajectory planning guidelines and infusion simulation in the software can be used prospectively to optimize personalized CED treatment.