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.

Abstract 2742: A biology-based, mathematical model to predict the response of recurrent glioblastoma to treatment with 186Re-labeled nanoliposomes

Introduction: 186Re-nanoliposomes (RNL) are a theranostic that emits a therapeutic payload of ionizing beta radiation, and a gamma photon to be measured with SPECT. The RNL is delivered via convection-enhanced delivery, resulting in a highly localized distribution around the glioma that produces up to a 30-fold increase in maximum tolerable dose. RNL provides a continuous source of low dose rate irradiation, until the particles are cleared biologically or decay. The goal of this study is to evaluate the accuracy of a patient calibrated reaction-diffusion equation for predicting the growth and response of recurrent glioblastoma multiforme (GBM) following treatment with RNL.

Methods: Multi-parametric images were collected from patients (n=10) receiving RNL treatment, consisting of pre-treatment and follow-up MRIs (Day 0, 28*, 56, 112*) and SPECT/CTs acquired at the middle and end of infusion, and 24-, 112-, and 192-hours post-infusion. For each time point, tumor segmentations and cell count maps are computed using contrast enhanced and diffusion weighted MRI, respectively. The spatio-temporal response to RNL is modeled using a biology-informed reaction-diffusion model describing tumor cell proliferation, invasion, and radiation induced death. Key model parameters related to the RNL activity are populated through a quantification of the SPECT time course. The remaining model parameters related to diffusivity, proliferation, and death rate are calibrated via the Levenberg-Marquardt algorithm for each individual patient, and then used to forecast growth. Calibrations are performed in two different scenarios, first to all imaging time points to assess the model capabilities (Scenario 1), and then without the last acquired MRI, which is set aside to evaluate prediction accuracy (Scenario 2). Error will be assessed at the global (Dice similarity coefficient and percent error in total cell number) and local (concordance correlation coefficient or CCC) levels for both scenarios. *Patients are scanned on day 28 or day 112 at a minimum, potentially both (n=5)

Results: Scenario 1 calibrations produced on average, Dice=0.92, CCC=0.69, and total cell percent error = 10.2%, validating usage of the current model formulation. Scenario 2 calibrations show high prediction success on a global scale, mean Dice=0.78, mean total cell percent error =23%, but resulted in poor local accuracy, mean CCC=0.21.

Discussion & conclusion: The mathematical model and processing framework can predict the spatiotemporal evolution of recurrent GBM after treatment with RNL. Ongoing efforts include validating the methodology on a larger cohort and further model selection to improve predictive capabilities.

Citation Format: Chase Christenson, Chengyue Wu, David A. Hormuth, Shiliang Huang, Andrew Brenner, Thomas E. Yankeelov. A biology-based, mathematical model to predict the response of recurrent glioblastoma to treatment with 186Re-labeled nanoliposomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2742.

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, K^trans , was the highest of the PK parameters regardless of the PK models. The ICCs of K^trans 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.

Minor physical anomalies in schizophrenia patients, bipolar patients, and their siblings

Minor physical anomalies (MPAs) are believed to reflect abnormalities in fetal neurodevelopment. Several studies have shown that schizophrenia patients have more MPAs than normal controls, but little is known about the meaning of this increased rate of MPAs. The current study first attempted to determine whether the increased MPAs are associated with schizophrenia in particular or with psychosis in general. Second, the study tested whether the patients' siblings also show an increased rate of MPAs by assessing MPAs in schizophrenia patients, bipolar manic patients, the siblings from each group of patients, and normal controls. The schizophrenia patients had significantly more MPAs than normal controls and bipolar patients. The rate of MPAs in bipolar patients did not differ from normal controls. This pattern suggests that MPAs have some degree of specificity to schizophrenia. Both sibling groups had fewer MPAs than the patients, and this difference was significant for the comparison between schizophrenia patients and their siblings. When viewed within a vulnerability-stress model, the results are consistent with the theory that MPAs may reflect early, largely extra-genetic, stressful events.

Comparison of oblique closing base wedge osteotomies of the first metatarsal: stripping versus nonstripping of the periosteum

The authors present a study comparing two methods of performing the oblique closing base wedge osteotomy of the first metatarsal for correction of hallux abducto valgus deformity. In one group, the periosteum was stripped from the metatarsal prior to performing the osteotomy, and in the other group, the osteotomy was performed through the periosteum. Sixty-five osteotomies are included in the study. This article discusses the closing base wedge osteotomy, fracture healing and the role of the periosteum, the blood supply to the first metatarsal, and the effects of periosteal stripping. Also included is a presentation of the surgical technique used, a statistical review of the procedures performed, a critical discussion summarizing the results, and a discussion of the postoperative complications encountered.

Frostbite

Frostbite can cause loss of life and limbs. Proper treatment and follow-up care are essential in reducing morbidity and mortality. Management of the frostbite patient consists of three stages. Initial care includes protection of the extremity from trauma or partial thawing. The second stage consists of rapid rewarming in a whirlpool bath containing hot water, with continued protection of the tissues. The third stage is long-term care of the affected body parts.