Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma.

BACKGROUND: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM). METHODS: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient (CCC) was used to quantify the agreement between the predicted and measured SPECT signals. The model was then used to simulate RNL distributions from a range of catheter placements, resulting in a ratio of the cumulative RNL dose outside versus inside the tumor, the "off-target ratio" (OTR). Optimal catheter placement) was identified by minimizing OTR. RESULTS: Fifteen patients with rGBM from a Phase I/II clinical trial (NCT01906385) were recruited to the study. Our model, with either individual-calibrated or population-assigned parameters, achieved high accuracy (CCC > 0.80) for predicting RNL distributions up to 24 h after delivery. The optimal catheter placements identified using this model achieved a median (range) of 34.56 % (14.70 %-61.12 %) reduction on OTR at the 24 h post-delivery in comparison to the original placements. CONCLUSIONS: Our image-guided model achieved high accuracy for predicting patient-specific RNL distributions and indicates value for optimizing catheter placement for CED of radiolabeled liposomes.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes.

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models. Methods: We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters. Results: Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively. Conclusion: We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment. Statement of Significance: This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.

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.

Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI.

PURPOSE: To develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI). METHODS: The DP4 phantom was designed for single-use and imaged concurrently with a human subject so that the phantom data can be utilized as the reference to detect errors in qDCE-MRI measurement of human tissues. The change of contrast-agent concentration in the phantom was measured using liquid chromatography-mass spectrometry. The repeatability of the contrast enhancement curve (CEC) was assessed with five phantoms in a single MRI scanner. Five healthy human subjects were recruited to evaluate the reproducibility of qDCE-MRI measurements. Each subject was imaged concurrently with the DP4 phantom at two institutes using three 3T MRI scanners from three different vendors. Pharmacokinetic (PK) parameters in the regions of liver, spleen, pancreas, and paravertebral muscle were calculated based on the Tofts model (TM), extended Tofts model (ETM), and shutter speed model (SSM). The reproducibility of each PK parameter over three measurements was evaluated with the intraclass correlation coefficient (ICC) and compared before and after DP4-based error correction. RESULTS: The contrast-agent concentration in the DP4 phantom was linearly increased over 10 min (0.17 mM/min, measurement accuracy: 96%) after injecting gadoteridol (100 mM) at a constant rate (0.24 ml/s, 4 ml). The repeatability of the CEC within the phantom was 0.997 when assessed by the ICC. The reproducibility of the volume transfer constant, Ktrans , was the highest of the PK parameters regardless of the PK models. The ICCs of Ktrans in the TM, ETM, and SSM before DP4-based error correction were 0.34, 0.39, and 0.72, respectively, while those increased to 0.93, 0.98, and 0.86, respectively, after correction. CONCLUSIONS: The DP4 phantom is reliable, portable, and capable of significantly improving the reproducibility of qDCE-MRI measurements.

Enhancement of label-free biosensing of cardiac troponin I.

The detection of cardiac troponin I (cTnI) is clinically used to monitor myocardial infarctions (MI) and other heart diseases. The development of highly sensitive detection assays for cTnI is needed for the efficient diagnosis and monitoring of cTnI levels. Traditionally, enzyme-based immunoassays have been used for the detection of cTnI. However, the use of label-free sensing techniques have the advantage of potentially higher speed and lower cost for the assays. We previously reported a Photonic Crystal-Total Internal Reflection (PC-TIR) biosensor for label-free quantification of cTnI. To further improve on this, we present a comparative study between an antibody based PC-TIR sensor that relies on recombinant protein G (RPG) for the proper orientation of anti-cTnI antibodies, and an aptamer-based PC-TIR sensor for improved sensitivity and performance. Both assays relied on the use of polyethylene glycol (PEG) linkers to facilitate the modification of the sensor surfaces with biorecognition elements and to provide fluidity of the sensing surface. The aptamer-based PC-TIR sensor was successfully able to detect 0.1 ng/mL of cTnI. For the antibody-based PC-TIR sensor, the combination of the fluidity of the PEG and the increased number of active antibodies allowed for an improvement in assay sensitivity with a low detection limit of 0.01 ng/mL. The developed assays showed good performance and potential to be applied for the detection of cTnI levels in clinical samples upon further development.