Image-based Fluid Dynamics Research Articles

Abstract 222: Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics

Abstract Introduction: Standard-of-care neoadjuvant therapeutic regimens are based on clinical trials designed to identify treatment protocols at the population level, which cannot capture the unique characteristics of individual patients. Moreover, it is impossible for clinical trials to experimentally evaluate all the possible treatment combinations and dosing strategies. Mechanism-based, mathematical modeling can potentially address this critical barrier to achieving truly personalized treatment. In this contribution, we present preliminary results using a fluid dynamics model personalized with a patient's imaging data to optimize a drug injection protocol in breast cancer. Methods: Nine breast cancer patients are included in this study. MRI data are acquired, processed, and used to estimate hemodynamics and imported to our drug delivery model. By varying the dosing schedule, the model predicts varying dynamics of drug distribution throughout the breast. We define an objective function, MSTD, balancing the treatment efficacy and toxicity, and use it to optimize the injection protocol for each patient. The injection schedules tested include: (a) single injection on day 1 of each 3-week cycle; double injections of half the total dose each at (b) day 1 and 2, (c) day 1 and 3, (d) day 1 and 5, and (e) day 1 and 8 within each 3-week cycle. The doses tested vary from 0 to 100 μg/ml of maximal plasma concentration. Improvement ratio of the optimal protocol to a standard protocol, 10 μg/ml via schedule (a), is measured. Results: The optimal protocol and improvement are reported for each patient in Table 1. Conclusion: Our modeling system can efficiently solve the personalization of drug injection protocol as an optimal control problem. Different patients have different optimal treatment protocols, each of which is predicted to outperform the standard protocol. Table 1.Optimal injection protocol for individual patientsPatientOptimal scheduleOptimal dose (μg/ml)Improvement ratio1(e)8.891.492(e)6.071.823(e)5.882.124(e)7.362.415(e)5.193.266(e)5.203.377(e)5.703.598(e)4.103.079(e)4.593.49NCI U01CA142565, U01CA174706, R01CA218700, R01CA172801, U24CA226110, P30CA014599. CPRIT RR160005. Citation Format: Chengyue Wu, David A. Hormuth, Federico Pineda, Gregory S. Karczmar, Thomas E. Yankeelov. Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 222.

Image-based fluid dynamics analysis of left ventricle outflow tract pressure gradient after deployment transcatheter mitral valve.

The goal of this study was to develop an image-based model to computational investigate blood flow and pressure gradients resulting from left ventricular (LV) wall motion after the implantation of a mitral valve (MV) prosthesis. Two image-based 3D models were reconstructed from multi-slice computed tomography images obtained from patients undergoing transcatheter MV replacement. Navier-Stokes equations were then used to compute the fluid motion. Outflow tract obstruction of the models with MV prosthesis were identified by calculating the difference between LV systolic and aortic pressures. It was found that computed outflow track obstruction compared well with actual obstruction data obtained from two patients. Our study indicates computational modeling can be a valuable tool to investigate the optimal placement of prosthetic valves guided by individualized anatomical data.

212 絶滅腕足類の化石を用いた殻まわりのイメージベース流体解析(OS3-1:生物遊泳・飛翔とバイオミメティクス(1),OS3:生物遊泳・飛翔とバイオミメティクス)

212 絶滅腕足類の化石を用いた殻まわりのイメージベース流体解析(OS3-1:生物遊泳・飛翔とバイオミメティクス(1),OS3:生物遊泳・飛翔とバイオミメティクス)