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This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity.With an individually optimized dose (range = 12.11-15.11 mg/mA clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis.Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response. Significance: Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.Significance:Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

Supplementary Data from MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Introduction:. Patients with locally advanced triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of the subsequent breast conservation surgery. A critical unmet need is the lack of a method to accurately predict how a patient with TNBC will respond to NAT before surgery. In this work, we applied a clinical-computational framework to predict response of TNBC early in the course of NAT, by integrating quantitative MRI with mechanism-based mathematical modeling. Methods:. Patients and Data. Multiparametric quantitative MRI was acquired in patients (n = 46) before, and after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C) regimen as part of the MD Anderson Cancer Center TNBC Moonshot Program. Within each imaging session, dynamic contrast-enhanced (DCE-), diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. Image processing. The processing pipeline consisted of three components. First, the images within each visit were registered to account for patient motion, and the parametric maps from the DCE and DWI images were computed. Second, inter-visit image registration was achieved by a non-rigid registration applied on breast, with a rigid penalty applied on the tumor region to preserve its size and shape. Third, post-processing was performed for preparation of modeling, including segmentation of the breast contour and tissues, and calculation of voxel-wise cellularity within tumors. Mathematical modeling. A predictive model was developed based on a reaction-diffusion equation (Eq. 1). The mobility of tumor cells is represented by diffusion coupled to mechanical properties of the tissue (Eq. 2), and the proliferation of the tumor is described with logistic growth. The injection and decay of administered therapies, inducing tumor cell death, is also represented in the model (Eq. 3). The variables and parameters used are listed in Table 1. Eq. 1: ∂N(x,t)/∂t = ∇⋅(D(x,t) ∇N(x,t)) + k(x) (1 - N(x,t)/θ)N(x,t) - (λ1(x,t) + λ2(x,t))N(x,t). Eq. 2: D(x,t) = D0 e-γσ(x,t). Eq. 3: λn(x,t) = αne-βn t C(x,t), n = 1, 2. For each patient, the domain and initial condition were generated from the pre-treatment images, and the images acquired during NAT were used for patient-specific calibration of parameters. The calibrated model was then used to predict the response to be observed at the end of NAT. We evaluated the model by comparing its predictions of tumor volume, longest axis, voxel-wise cellularity, and total tumor cellularity to the imaging measurements at the end of A/C. Results:. Our model predicted the tumor volume, total cellularity, and longest axis with a Pearson correlation coefficient (PCC) of 0.85, 0.80, and 0.60, respectively. The accuracy of voxel-wise cellularity achieved a PCC with the median (range) of 0.89 (0.77 - 0.93) between the prediction and the actual measurement. Moreover, we set criteria of 70% shrinkage of tumor volume to define response versus non-response cases, with which our model achieved a differentiation sensitivity/specificity of 0.90/0.73. Discussion:. Preliminary results of our study demonstrate the potential of the clinical-computational framework as a powerful tool for predicting response to NAT. Once validated, the method could also assist in optimizing treatment plans on a patient specific basis, or guiding patient selection in trials for novel NAT regimens. Table 1. Summary of the variables and parameters in the modelQuantitiesDefinition AssignmentDomainsΩbreast tissue domainGenerated from pre-treatment MRITEnd time point of NAT procedureDetermined from NAT schedulexCoordinate in breast tissueAssociated with spatial domain, ΩttimeAssociated with temporal domain, [0, T]VariablesN(x,t)Tumor cell numberInitialized from pre-treatment ADC, computed via Eq. 1D(x,t)Diffusive mobility of tumor cellsComputed via Eq. 2λn(x,t)Death rate induced by nth type of drugComputed via Eq. 3, n = 1 and 2 for A/Cσ(x,t)Von Mises stressComputed from gradient of N(x,t), based on Hormuth et al., 2018C(x,t)Spatiotemporal distribution of drugsAssigned based on NAT schedule and DCE imagesParametersk(x)Proliferation rate of tumor cellsLocally calibratedθTumor cells carry capacityGlobally calibratedαnEfficacy rate of nth type of drugGlobally calibratedβnDecay rate of of nth type of drugGlobally calibratedD0Diffusion coefficient of tumor cells in the absence of mechanical restrictionsGlobally calibratedγStress-tumor cell diffusion coupling constantAssigned based on Hormuth et al., 2018 Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania M. Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Stacy Moulder, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-08.

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2–3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction–diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor’s size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient’s tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index.The CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r TThe use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.

Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant systemic therapy (NAST). However, the response of TNBC to NAST is varied and a prognostic marker is still lacking. Since angiogenesis plays an important role in cancer progression, the quantification of changes in the tumor vasculature during treatment has the potential to provide useful information for characterizing the treatment response. We have recently developed a method to quantify changes in tumor-associated vasculature through a novel analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we investigated its ability to monitor the response of TNBC to NAST. Methods: Dynamic contrast-enhanced (DCE) MRI was acquired in TNBC patients (N = 50; 25 pathological complete response (pCR), 25 non-pCR) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), as part of the ARTEMIS trial (NCT02276433). Using DCE-MRI, we identified the tumor-associated vessels via a breast vasculature analysis. Specifically, the difference between pre- and post-contrast DCE-MRI images was enhanced by a histogram-based intensity transfer function. The 3D vasculature in the breast was then segmented by applying a Hessian filter. Given the vasculature segmented by the algorithms and the tumor masks segmented by radiologists, a lowest-cost tracking algorithm was used to automatically detect the vessels that are most likely to contact the tumor. These vessels are defined as tumor-associated vessels (TAV). The number of TAVs per patient was tabulated and the Wilcoxon rank sum test compared the TAV values before (V1), after 2 cycles (V2), and after 4 cycles of NAST (V3). Additionally, we compared the percent change of TAV from V1 to V3 between the pCR patients and non-pCR patients. Statistical significance was defined as P < 0.05. Results: A significant decrease in the number of TAVs was observed during the NAST. In particular, the number of TAVs has a median (interquartile range) of 15 (7 - 24), 7 (3 - 14), and 2 (0 - 8) at V1, V2, V3, respectively, which are (pair-wise) significantly different (P < 0.01). Moreover, pCR patients showed a significantly greater decrease in the number of TAVs as compared to non-pCR patients. The percent changes in the number of TAVs from V1 to V3 have a median (range) of -88.89% (-100.00% - -60.00%) and -66.67% (-87.85% - -40.88%) in the pCR and non-pCR patients, respectively (P < 0.05). Conclusion: These preliminary results demonstrate that tumor-associated vasculature may be a valuable imaging biomarker for monitoring the response of TNBC to NAST. Ongoing efforts include additional investigation of the TAVs beyond their number, as well as applying the analysis to more patients. Citation Format: Chengyue Wu, Casey E. Stowers, Zhan Xu, Ernesto A. B. F. Lima, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Quantification of tumor-associated vasculature as an imaging biomarker for monitoring the response of triple-negative breast cancer to neoadjuvant chemotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5569.

More than 50% of triple negative breast cancer (TNBC) patients do not respond well to the standard-of-care neoadjuvant therapy (NAT). Therefore, methods capable of predicting treatment response will be highly useful to optimize intervention and outcomes for TNBC patients. To address this problem, we aim to integrate quantitative magnetic resonance imaging (MRI) with biology-based mathematical modeling and deep learning to make patient-specific predictions of TNBC response to NAT using only pretreatment data. TNBC patients (n = 150) enrolled in the ARTEMIS trial (NCT02276443) received doxorubicin/cyclophosphamide (A/C) followed by paclitaxel. MRI exams were acquired for each patient at the following timepoints: (1) before initiation of NAT, (2) after two A/C cycles, (3) after four A/C cycles, and (4) at the conclusion of NAT. Using patient-specific MRI data from the first two exams, we calibrated a biology-based mathematical model to characterize migration, proliferation, and treatment-induced death of tumor cells. We then used this model as a digital twin to predict spatiotemporal tumor response. While effective, this approach requires the patient to have completed at least part of their NAT regime before we are able to predict therapeutic response. To relax this requirement, we have developed an approach that combines deep learning and biology-based mathematical modeling to predict the response of TNBC to NAT before treatment initiation. Specifically, we integrated a U-Net-based convolutional neural network with our mathematical model to regress between pre-treatment data and the model parameters obtained from a training set. Using parameters from learning a network with a subset of 68 patients, our mathematical model yielded concordance correlation coefficients between the predicted and measured patient-specific changes in tumor cellularity and volume at the third imaging point of 0.95 and 0.92, respectively. Spatially, we obtain the median difference between predicted and measured percent change in cellularity from visit one to visit three for each patient, giving a mean (95% confidence interval) of -6.51% (-7.13%, -5.90%) across all patients. These encouraging results may be further improved using methods such as expanding to a spatially-resolved proliferation rate, including genetic and/or histological data, and extending the deep learning framework to the end of the treatment course to predict pathological response. This approach allows us to obtain patient-specific predictions of response before NAT commences, thereby providing the opportunity to optimize interventions and patient outcomes. Citation Format: Casey E. Stowers, Chengyue Wu, Sidharth Kumar, Ernesto A.B.F. Lima, Xhan Zu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I. Tamir, Gaiane M. Rauch, Thomas E. Yankeelov. Developing MRI-based digital-twins via mathematical modeling and deep learning to predict the response of triple-negative breast cancer to neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 845.

Active surveillance (AS) is an established clinical strategy for the management of prostate cancer (PCa) exhibiting low to intermediate risk. In AS, treatment is delayed until progression to higher-risk disease is detected during the close monitoring of patients via longitudinal multiparametric magnetic resonance imaging (mpMRI) scans, biopsies, and Prostate Specific Antigen (PSA) tests. Thus, AS has been regarded as a promising strategy to address the current rates of overtreatment and undertreatment in PCa. However, current AS protocols rely on an observational paradigm, which may delay the detection of tumor progression, and the testing frequency is largely fixed according to population studies, which impedes the design of personalized monitoring plans. To address these issues, we propose to advance AS towards a predictive patient-specific paradigm by leveraging computational tumor forecasts obtained with a biomechanistic model informed by the imaging and clinical data collected during standard AS for each individual patient. Here, we present a preliminary study in a cohort of eight PCa patients who enrolled in AS and had three mpMRI scans over a period of 2.6 to 5.6 years. Our model describes PCa growth in terms of the dynamics of tumor cell density as a combination of tumor cell mobility and net proliferation, which are formulated as a diffusion process and logistic growth, respectively. The model is implemented in each patient’s prostate geometry, which is segmented on T2-weighted MRI data. Tumor cell density estimates are derived from Apparent Diffusion Coefficient (ADC) maps obtained from diffusion-weighted MRI data. To facilitate modeling, the longitudinal imaging datasets are non-rigidly co-registered for each patient. We initialize the model with the tumor cell density map obtained from the ADC map of the first mpMRI scan. Then, the model is parameterized by minimizing the model-data mismatch in tumor cell density at the date of a second mpMRI scan. Finally, we perform a tumor forecast up to the date of a third mpMRI scan, which we use to assess the model-data agreement of our predictions of PCa growth. We obtained a concordance correlation coefficient (CCC) for tumor volume and global tumor cell count of 0.87 and 0.95 during model calibration and of 0.91 and 0.87 at forecasting horizon, respectively. For each patient, the spatial fit of tumor cell density yielded a median Dice score and CCC of 0.77 and 0.50 at the second mpMRI date, respectively. Likewise, the tumor cell density predictions at the third scan date resulted in a median Dice coefficient and CCC of 0.74 and 0.51 across the patient cohort, respectively. Thus, while further model development and performance assessment over lager cohorts are required, these results suggest that our forecasting technology is a promising tool to predict PCa progression in AS and, hence, identify patients who require treatment early during the course of AS. Citation Format: Guillermo Lorenzo Gomez, Chengyue Wu, Joshua P. Yung, John F. Ward, Hector Gomez, Alessandro Reali, Thomas E. Yankeelov, Aradhana M. Venkatesan, Thomas J. Hughes. Patient-specific, organ-scale forecasting of prostate cancer growth in active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 850.

Background: This study evaluates the ability to predict the response of locally advanced breast cancers to neoadjuvant therapy (NAT) using patient-specific magnetic resonance imaging (MRI) data and a biophysical mathematical model. The 3D mathematical model consists of three parts: tumor cell proliferation, tumor spread (diffusion), and treatment. In particular, the tumor cells proliferate according to logistic growth, and the diffusion term is coupled to the mechanical properties of the surrounding fibroglandular and adipose tissues to inform individual tumor growth patterns (specific to each patient’s anatomy). The model’s treatment term accounts for tumor cell reduction according to approximate local drug delivery for each patient. Methods: Patients (N = 21) with intermediate to high grade invasive breast cancers with varying receptor status, who were eligible for NAT as a component of their clinical care, were recruited. Each patient was treated with standard-of-care consisting of one or two NAT regimens in sequence followed by surgical resection of any residual tumor. MRI data are acquired at four time points: 1) prior to initiation of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after scan 3. The MRI data is processed and evaluated using our semi-automated pipeline. Specifically, diffusion-weighted MRI data is utilized to characterize the cellularity throughout the tumor tissue, and dynamic contrast-enhanced (DCE-) MRI data is used to segment the breast tissue and analyze the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of drug concentrations. The model’s predictive ability is assessed using three different strategies. First, the model is calibrated using each patient’s first two scans to enable predictions of the total tumor cellularity, volume, and longest axis that are directly compared to the values measured from their third scan. Second, the model’s predictions for tumor response are compared to the corresponding response evaluation criteria in solid tumors (RECIST) results. Third, the model is re-calibrated using scans 3 and 4 and simulated to the time of surgery to compare the model’s predictions to each patient’s response status determined by surgical pathology. Results: Calibrating the model with MRI data for one cycle of therapy yields predictions strongly correlated with tumor response measured from each patient’s third scan, concordance correlation coefficients of 0.91, 0.90, and 0.86 for total cellularity, volume, and longest axis, respectively (p < 0.01, N = 18). The model’s predictions are significantly (p < 0.01) correlated with tumor response as designated by RECIST for the cohort. Specifically, the model predicts greater percent reduction in the longest axis for the RECIST designated responder group (i.e., complete response and partial response) compared to non-responders. At the time of surgery, the model predicts changes in total tumor cellularity from baseline that are significantly (p < 0.01) correlated with pathological response status—an area under the receiver operator characteristic curve of 0.92 and a sensitivity and specificity of 1.0 and 0.74, respectively. Discussion: These preliminary results suggest that this clinical-mathematical approach can be predictive of tumor response very early in the course of NAT on a patient-specific basis. Moreover, the study was performed in the community-care setting across a heterogenous group of patients, indicating the approach may be practical for wide-spread application. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE, R01CA240589, NCI-U24CA226110, CPRIT RR160093 Citation Format: Angela M Jarrett, David A. Hormuth, II, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Jeanne Kowalski, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Predicting breast cancer response to neoadjuvant therapies using a mathematical model individualized with patient-specific magnetic resonance imaging data: Preliminary Results [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-18.

Introduction. X-ray mammography is the standard-of-care screening protocol for breast cancer due to its low cost, widespread availability, and greater specificity. While magnetic resonance imaging (MRI) has lower specificity, it has superior tissue contrast, and dynamic contrast-enhanced (DCE) MRI has been shown to increase MRI specificity due to its ability to estimate tissue vascular properties. Because of the increased expense and scan time for MRI, there is an ongoing effort to develop abbreviated breast MRI scans for screening high-risk patients at a lower cost without compromising quantitative information. Here we investigate the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the quantitative tissue information computed from retrospectively abbreviated DCE-MRI data. Methods. Data acquisition. We evaluate the error in perfusion model parameters computed using DCE-MRI time courses sourced from three datasets acquired with very different temporal resolutions (dt). These datasets are the ACRIN 6883 multi-site breast trial (dt = 15 s), The University of Texas at Austin (UTA) neoadjuvant therapy study (dt = 7.3 s), and The University of Chicago (UC) ultra-fast breast DCE-MRI study (dt = 3.4 s). Ten representative patients are chosen from each of these datasets for a total of 30 DCE-MRI patient datasets. All 30 full-time courses (FTCs) are retrospectively truncated into a series of abbreviated-time courses (ATCs). An ATC containing the first n post-contrast injection time points of a DCE-MRI time course is referred to as “ATC n.” For the ACRIN dataset, n is the inclusive set of integers from 7 to 18; and, similarly, for the UC data, n is the inclusive set of integers from 12 to 23. For the UTA dataset, n is the inclusive set ranging from 13 through 53, incrementing by eight. Data Analysis. The groups of FTCs and ATCs are analyzed by one of three models, determined by the specifics of the acquisition details of each dataset. The standard Kety-Tofts (SKT) model is fit to the UTA time courses, the reference-region (RR) model is fit to the ACRIN time courses, and the Patlak model is fit to the UC time courses. The volume transfer constant (Ktrans) characterizes tissue enhancement in all three models; whereas, the extravascular/extracellular volume fraction (ve) is specific to the SKT and RR models, and the plasma volume fraction (vp) is specific to the Patlak model. Due to the absence of an arterial input function (AIF) for the ACRIN dataset, the RR model was most appropriate with the pectoral muscle serving as the reference region. The UTA dataset has a population AIF and is thus able to be modeled by the SKT. Lastly, the UC dataset does not capture the tissue washout necessary for ve estimation, so the Patlak model was chosen for analyzing tissue enhancement. Results and Conclusion. The longest ATCs of 4.5, 6.4, and 1.3 min. yielded average errors of 9.1%, 7%, and 3.6% in Ktrans for the ACRIN, UTA, and UC datasets, respectively; and the shortest ATCs of 2, 1.6, and 0.6 min. yielded higher average errors of 24.2%, 22.8%, and 65% in Ktrans, respectively. This is expected from simulations as even the most aggressive ATCs did not substantially exclude the tissue enhancement, characterized by Ktrans. Errors in ve were higher overall since shorter ATCs exclude much of the washout phase. As Ktrans has been shown to discriminate between malignant and benign lesions in full length DCE-MRI scans, it is promising that tolerable errors are observed in the abbreviated MRI setting. There is potential for implementing quantitative abbreviated DCE-MRI scans in the clinic for enhanced diagnostic specificity while freeing up scan time for additional imaging sequences without interfering with standard-of-care image acquisition. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Characterizing errors in perfusion model parameters derived from retrospectively abbreviated quantitative DCE-MRI data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-26.

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.

The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( ${P} =0.028$ ), and the ranges of tumor-associated blood pressure ( P = 0.016) and vascular extraction rate ( P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology’s utility for the quantitative characterization of breast cancer.

Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p < 0.01) for total cellularity, total volume, and longest axis, respectively. The model predicts that for the alternative dosing regimens assessed, individual patients could have achieved an additional 0-43% reduction in total cellularity compared to the therapeutic regimens patients actually received. This indicates that standard regimens may not be the most effective for every patient. Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-16-17.

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D-0D-3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D-0D-3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.

Convection-enhanced delivery (CED) of Rhenium-186 nanoliposomes (RNL) is a promising approach to provide precise delivery of large, localized doses of radiation with the goal of extending overall survival for patients with recurrent GBM. A central component of successful CED, is achieving optimal catheter placement for delivery of the therapy. While surgical planning software exists for this purpose, current approaches are designed for small molecules and therefore are not appropriate for larger particles like RNL. To address this concern, we have developed a mathematical model to predict the distribution of RNL via CED on a patient-specific basis. The model is defined on the 3D brain domain which consists of 1) pressure and flow fields generated by accounting for catheter infusion, flow through brain, and fluid loss into capillaries, and 2) the transport of RNL governed by an advection-diffusion equation. We utilize pre-operative MRI to assign patient-specific tissue geometry and properties (e.g., diffusivity, conductivity), and calibrate the model with SPECT measurements within 24 h post the RNL delivery. This model is implemented on one patient enrolled in NCT01906385. The accuracy of model calibration and prediction is evaluated by the Dice score and concordance correlation coefficient (CCC) between modeled and measured distributions of RNL. Our model calibration achieves Dice scores of 0.80, 0.81, 0.69 and CCC of 0.92, 0.93, 0.73 for RNL distributions at the mid-delivery, end of delivery, and 24 h after the delivery, respectively. Long-term model prediction achieves Dice scores of 0.69 and 0.52 at 144 h and 196 h after the delivery, respectively, and CCC of 0.57 and 0.31. Preliminary results demonstrate a proof-of-concept for a patient-specific model to predict the spatiotemporally-resolved distribution of nanoparticles. Ongoing efforts focus on improving our model by accounting backflow and angle of catheter placement, and applying to more patients. Funding: NIH R01CA235800, CPRIT RR160005.

Simple Summary The recruitment of new vasculature via angiogenesis is a critical component of tumor development, which fundamentally influences tumor growth and response to treatment. The characterization of tumor-induced angiogenesis via mathematical models could enable approaches to forecast tumor response and improve patient care. In this review, we discuss how time-resolved imaging data integrated with mathematical modeling can be used to systematically investigate angiogenesis from the cell to tissue scale and ultimately forecast response to therapy. Abstract Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

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.

Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of subsequent breast conservation surgery. There are currently no methods to accurately predict how a TNBC patient will respond to NAT before surgery. In this work, we applied a digital twin framework to address this unmet clinical need, by integrating quantitative magnetic resonance imaging (MRI) data with mechanism-based mathematical modeling. Methods: Multiparametric MRI was acquired in patients (N = 50) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), and again after 12 cycles of Paclitaxel as part of the ARTEMIS (NCT02276433) trial. Within each imaging session, dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. The images were processed by a pipeline consisting of motion correction, multiparametric image alignment, inter-visit image registration to align the tumor and surrounding breast tissue, tissue segmentation, and estimation of tumor cellularity from DWI. A mechanism-based mathematical model, a reaction-diffusion equation, is used to characterize the mobility of tumor cells via diffusion damped by mechanical tissue properties, tumor proliferation via logistic growth, and treatment-induced cell death via the delivery and decay of therapies. For each patient, pre-treatment images were used for model initialization. The model calibration and prediction were implemented with two strategies: 1) using images acquired during the A/C for calibration and predicting up to the end of A/C, and 2) using images acquired during and after the A/C for calibration and predicting up to the end of NAT. For strategy 1), we evaluated the model by comparing its predicted tumor volume and total tumor cellularity to the imaging measurements at the end of A/C. For strategy 2), we evaluated the model by comparing its predicted final response to the post-surgical pathological findings. Results: For strategy 1), our framework predicted the change of tumor volume and total tumor cellularity with Pearson correlation coefficients of 0.91 and 0.89, respectively. Regarding strategy 2), our framework achieved an area under the receiver operator characteristic curve of 0.88 for distinguishing pCR from non-pCR. As a comparison, imaging measurement of tumor volume at the end of A/C achieved an AUC of 0.79. Conclusion: Our approach successfully captures the patient-specific dynamics of TNBC response to NAT and provides an improved prediction of final response, which demonstrates the potential of a digital twin framework to be a powerful tool for predicting response to NAT. Once validated, the method will provide a unique opportunity for optimizing treatment plans on a patient-specific basis. Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin [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 2736.

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the “ground truth” data to which the output of a given processing method must be compared. One way to address this issue is via “digital phantoms”, which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SR(A), where the subscript indicates Protocol A) or signal-to-noise ratio (SNR(A)) decreases. Specifically, with an SNR(A) / SR(A) = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNR(A) / SR(A) = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PE(SER)). We find that PE(SER) significantly decreases the as temporal resolution (TR(B)) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PE(SER). For example, with an SNR(B) / SR(B) / TR(B) = 5 dB / 300 μm / 10 s, the median PE(SER) is 21.00 %, whereas an SNR(B) / SR(B) / TR(B) = 75 dB / 60 μm / 1 s, yields a median PE(SER) of 0.90 %. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

PURPOSE: This study assesses the rate of enhancement of breast fibroglandular tissue after administration of a magnetic resonance imaging (MRI) gadolinium-based contrast agent and determine its relationship with response to neoadjuvant therapy (NAT) in women with breast cancer. METHOD: Women with locally advanced breast cancer (N = 19) were imaged four times over the course of NAT. Dynamic contrast-enhanced (DCE) MRI was acquired after administration of a gadolinium-based contrast agent with a temporal resolution of 7.27 sec. The tumor, fibroglandular tissue, and adipose tissue were semi-automatically segmented using a manually drawn region of interest encompassing the tumor followed by fuzzy c-means clustering. The rate and relative intensity of signal enhancement were calculated for each voxel within the tumor and fibroglandular tissue. RESULTS: The rate of fibroglandular tissue enhancement after contrast agent injection declined by an average of 29% over the course of NAT. This decline was present in 16 of the 19 patients in the study. The rate of enhancement is significantly higher in women who achieve pathological complete response (pCR) after both 1 cycle (68% higher, p < 0.05) and after 3–5 cycles of NAT (58% higher; p < 0.05). The relative intensity of fibroglandular enhancement correlates with the rate of enhancement (R(2) = 0.64, p < 0.001) and is higher in women who achieve pCR after both 1 cycle and after 3–5 cycles of NAT (p < 0.05, both timepoints). CONCLUSION: The rate of fibroglandular tissue enhancement declines over the course of therapy, provides novel information not reflected by tumoral measures, and may predict pathological response early in the course of therapy, with smaller declines in enhancement in women who achieve favorable response.

We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.

Background Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. Purpose/hypothesis To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices. Study type Prospective. Subjects/phantom Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability. Field strength/sequence 3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping. Assessment Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast. Statistical tests Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility. Results ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively). Data conclusion Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment. Level of evidence 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

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.

186-Rhenium nanoliposomes (RNL) are an experimental theranostic being investigated for the treatment of recurrent Glioblastoma. While traditional external beam therapy exposures healthy tissue to radiation, RNL has the potential to deliver extremely large doses (> 2000 Gy) of localized radiation, minimally exposing surrounding tissue. RNL is delivered directly to the malignancy by convection-enhanced delivery (CED) via intracranial catheter. For this reason, accurate and precise delivery of RNL to the target region is an imperative. While models of CED for molecular agents exist, we know of no such models for CED of liposomal nanoparticles. To that end, we are developing a patient-specific advection-diffusion model of RNL delivery and distribution, informed by pre-delivery quantitative magnetic resonance imaging (MRI) parameters, and validated by intra-delivery single-photon emission computed tomography (SPECT). Apparent liposome diffusivity and interstitial hydraulic conductivity are spatially informed by diffusion weighted MRI, while the clearance of interstitial fluid is spatially informed by the T1 enhancement ratio after contrast agent delivery. The model output is compared to SPECT images at two time points, acquired mid-way through and immediately following the RNL infusion. At the time of submission, the model has been calibrated by patient-specific data to match the spatiotemporal distribution of RNL in four patients. After calibration, the concordance correlation coefficient between the model and SPECT measurements was 0.80 +/- 0.23 mid-way through the infusion volume, and 0.86 +/- 0.14 immediately post-infusion. The DICE coefficient between the modeled delivery volume and measured delivery volume was 0.86 +/- 0.10 mid-way through the infusion volume, and 0.81 +/- 0.14 immediately post-infusion (reported as mean +/- 95% confidence intervals). These results provide preliminary evidence that the model can capture the spatiotemporal distribution of RNL during and after delivery, and may enable physicians to better plan CED procedures for liposomal nanotherapeutics in the future.

The use of rCBV as a response metric in clinical trials has been hampered, in part, due to variations in the biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and post-processing methods. This study leveraged a dynamic susceptibility contrast magnetic resonance imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific imaging protocols (IPs, n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance imaging analysis (eg, using a digital reference object where the ground truth is known).

Repeatability and reproducibility of magnetization transfer magnetic resonance imaging of the breast, and the ability of this technique to assess the response of locally advanced breast cancer to neoadjuvant therapy (NAT), are determined. Reproducibility scans at 3 different 3 T scanners, including 2 scanners in community imaging centers, found a 16.3% difference (n = 3) in magnetization transfer ratio (MTR) in healthy breast fibroglandular tissue. Repeatability scans (n = 10) found a difference of &sim, 8.1% in the MTR measurement of fibroglandular tissue between the 2 measurements. Thus, MTR is repeatable and reproducible in the breast and can be integrated into community imaging clinics. Serial magnetization transfer magnetic resonance imaging performed at longitudinal time points during NAT indicated no significant change in average tumoral MTR during treatment. However, histogram analysis indicated an increase in the dispersion of MTR values of the tumor during NAT, as quantified by higher standard deviation (P = .005), higher full width at half maximum (P = .02), and lower kurtosis (P = .02). Patients' stratification into those with pathological complete response (pCR, n = 6) at the conclusion of NAT and those with residual disease (n = 9) showed wider distribution of tumor MTR values in patients who achieved pCR after 2&ndash, 4 cycles of NAT, as quantified by higher standard deviation (P = .02), higher full width at half maximum (P = .03), and lower kurtosis (P = .03). Thus, MTR can be used as an imaging metric to assess response to breast NAT.

Convection-enhanced delivery of rhenium-186 (186Re)-nanoliposomes is a promising approach to provide precise delivery of large localized doses of radiation for patients with recurrent glioblastoma multiforme. Current approaches for treatment planning utilizing convection-enhanced delivery are designed for small molecule drugs and not for larger particles such as 186Re-nanoliposomes. To enable the treatment planning for 186Re-nanoliposomes delivery, we have developed a computational fluid dynamics approach to predict the distribution of nanoliposomes for individual patients. In this work, we construct, calibrate, and validate a family of computational fluid dynamics models to predict the spatio-temporal distribution of 186Re-nanoliposomes within the brain, utilizing patient-specific pre-operative magnetic resonance imaging (MRI) to assign material properties for an advection-diffusion transport model. The model family is calibrated to single photon emission computed tomography (SPECT) images acquired during and after the infusion of 186Re-nanoliposomes for five patients enrolled in a Phase I/II trial (NCT Number NCT01906385), and is validated using a leave-one-out bootstrapping methodology for predicting the final distribution of the particles. After calibration, our models are capable of predicting the mid-delivery and final spatial distribution of 186Re-nanoliposomes with a Dice value of 0.69 ± 0.18 and a concordance correlation coefficient of 0.88 ± 0.12 (mean ± 95% confidence interval), using only the patient-specific, pre-operative MRI data, and calibrated model parameters from prior patients. These results demonstrate a proof-of-concept for a patient-specific modeling framework, which predicts the spatial distribution of nanoparticles. Further development of this approach could enable optimizing catheter placement for future studies employing convection-enhanced delivery.

Introduction: A tumor's blood supply and interstitial flow play an essential role in tumor growth, invasion, and treatment response. We have developed a methodology that employs quantitative MRI data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport within breast tumors. The dynamics of solute transport are characterized based on steady state flow fields, so that delivery of oxygen, nutrients, or therapies through the circulation can be estimated. It is a fundamentally new way to characterize breast tumor hemodynamics using MRI. Method: Eleven malignant and five benign lesions from 12 patients were included in this study. Vessel segmentation and tracking were performed to reconstruct the whole breast vasculature and to identify vessels feeding or draining tumors. Ultrafast dynamic contrast-enhanced MRI and diffusion-weighted imaging data were analyzed to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and ADC (apparent diffusion coefficient); these data were used to spatially assign flow direction, local vascular permeability, and tissue density, respectively. The model of steady state flow was described by three components: 1) blood flow following Poiseuille's law, 2) interstitial flow following Darcy's law, and 3) flux transmitted across the vascular walls following Starling's law. The behavior of drug delivery was described by an advection-diffusion equation in the interstitial tissue, with the profile of the vascular bolus as a source term. The whole system was solved with a 1D-3D coupled implementation. At the end of analysis, the tracer's propagation through the tissue-of-interest was visualized and hemodynamic characteristics are derived to compare the malignant and benign lesions. Result: Visualization of the time-resolved distribution and propagation of solute demonstrated the intratumoral heterogeneity of accessibility to drugs. Furthermore, we are currently calculating the spatially-resolved accumulation, wash-in rate, and infiltration duration of different drugs for each lesion, so that a quantitative comparison can be performed between malignant and benign lesions. Conclusion: We have developed a computational model, informed by patient-specific MRI data, to simulate the blood supply, interstitial fluid environment, and intratumorally heterogeneous access to therapies for breast tumors. It represents the first methodology of integrating CFD with patient specific MRI data for quantifying the spatiotemporally resolved drug propagation as well as the entire pressure and flow fields within the breast. NCI U01CA142565, U01CA CA174706, and R01 CA218700. CPRIT RR160005. Citation Format: Chengyue Wu, David A. Hormuth, Federico Pineda, Gregory S. Karczmar, Robert D. Moser, Thomas E. Yankeelov. Characterization of patient-specific drug delivery for breast cancer using image-guided computational fluid dynamics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4263.