Characterizing breast cancer response to neoadjuvant therapy based on biophysical modeling and multiparametric magnetic resonance imaging data.

Personalized medicine efforts are focused on identifying biomarkers to guide individualizing neoadjuvant therapy regimens. In this work, we aim to validate a previously developed image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy using a large, multi-site cohort. We retrospectively analyzed patients enrolled in the BMMR2 ACRIN 6698 subset at 10 institutions. Patients enrolled received four MRI examinations during neoadjuvant therapy with acquisitions at baseline (T 0 ), 3-weeks/early-treatment (T 1 ), 12-weeks/mid-treatment (T 2 ), and completion of therapy prior to surgery (T 3 ). A biophysical mathematical model of tumor growth is used extract metrics to characterize the dynamics of treatment response. Using predicted response at therapy conclusion and histogram summary metrics to quantify estimated tumor proliferation maps, we found univariate model-based metrics able to predict pathological response, with area under the receiver operating characteristic curve (AUC) ranging from 0.58 and 0.69 analyzing between T 0 and T 1 , and AUCs ranging from 0.72-0.76 analyzing between T 0 and T 2 . For hormone receptor (HR)-negative, human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients our model-based metrics achieved an AUC of 0.9 analyzing between T 0 and T 1 and AUC of 1.0 analyzing between T 0 and T 2 . This data shows the significant promise in developing these imaging-based biophysical mathematical modeling methods of dynamic characterization into a clinical decision support tool for individualizing treatment regimens based on patient-specific response.

Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation.

Current tools to assess breast cancer response to neoadjuvant chemotherapy cannot reliably predict disease eradication, which if possible, could allow early cessation of therapy. In this work, we assessed the ability of an image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy. We retrospectively analyzed patients enrolled in the I-SPY 2 TRIAL at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center. Patients enrolled on the study received four MR imaging examinations during neoadjuvant therapy with acquisitions at baseline (T0), 3-weeks/early-treatment (T1), 12-weeks/mid-treatment (T2), and completion of therapy prior to surgery (T3). We use a biophysical mathematical model of tumor growth to generate spatial estimates of tumor proliferation to characterize the dynamics of treatment response. Using histogram summary metrics to quantify estimated tumor proliferation maps, we found strong correlation of mathematical model-estimated tumor proliferation with residual cancer burden, with Pearson correlation coefficients ranging from 0.88 and 0.97 between T0 and T2, representing a significant improvement from conventional assessment methods of change in mean apparent diffusion coefficient and functional tumor volume. This data shows the significant promise of imaging-based biophysical mathematical modeling methods for dynamic characterization of patient-specific response to neoadjuvant therapy with correlation to residual disease outcomes.

Clinical assessment of a biophysical model for distinguishing tumor progression from radiation necrosis.

PURPOSE: The efficacy of an imaging-driven mechanistic biophysical model of tumor growth for distinguishing radiation necrosis from tumor progression in patients with enhancing lesions following stereotactic radiosurgery (SRS) for brain metastasis is validated. METHODS: We retrospectively assessed the model using 73 patients with 78 lesions and histologically confirmed radiation necrosis or tumor progression. Postcontrast T1-weighted MRI images were used to extract parameters for a mechanistic reaction-diffusion logistic growth model mechanically coupled to the surrounding tissue. The resulting model was then used to estimate mechanical stress fields, which were then compared with edema visualized on FLAIR imaging using DICE similarity coefficients. DICE, model, and standard radiographic morphometric analysis parameters were evaluated using a receiver operating characteristic (ROC) curve for prediction of radiation necrosis or tumor progression. Multivariate logistic regression models were then constructed using mechanistic model parameters or advanced radiomic features. An independent validation was performed to evaluate predictive performance. RESULTS: Tumor cell proliferation rate resulted in ROC AUC = 0.86, 95% CI: 0.76-0.95, P < 0.0001, 74% sensitivity and 95% specificity) and DICE similarity coefficient associated with high stresses demonstrated an ROC AUC = 0.93, 95% CI: 0.86-0.99, P < 0.0001, 81% sensitivity and 95% specificity. In a multivariate logistic regression model using an independent validation dataset, mechanistic modeling parameters had an ROC AUC of 0.95, with 94% sensitivity and 96% specificity. CONCLUSIONS: Imaging-driven biophysical modeling of tumor growth represents a novel method for accurately predicting clinically significant tumor behavior.

Characterization of multicellular breast tumor spheroids using image data-driven biophysical mathematical modeling.

Multicellular tumor spheroid (MCTS) systems provide an in vitro cell culture model system which mimics many of the complexities of an in vivo solid tumor and tumor microenvironment, and are often used to study cancer cell growth and drug efficacy. Here, we present a coupled experimental-computational framework to estimate phenotypic growth and biophysical tumor microenvironment properties. This novel framework utilizes standard microscopy imaging of MCTS systems to drive a biophysical mathematical model of MCTS growth and mechanical interactions. By extending our previous in vivo mechanically-coupled reaction-diffusion modeling framework we developed a microscopy image processing framework capable of mechanistic characterization of MCTS systems. Using MDA-MB-231 breast cancer MCTS, we estimated biophysical parameters of cellular diffusion, rate of cellular proliferation, and cellular tractions forces. We found significant differences in these model-based biophysical parameters throughout the treatment time course between untreated and treated MCTS systems, whereas traditional size-based morphometric parameters were inconclusive. The proposed experimental-computational framework estimates mechanistic MCTS growth and invasion parameters with significant potential to assist in better and more precise assessment of in vitro drug efficacy through the development of computational analysis methodologies for three-dimensional cell culture systems to improve the development and evaluation of antineoplastic drugs.