Photoacoustic Imaging as a Novel Non-invasive Biomarker to Assess Intestinal Tissue Oxygenation and Motility in Neonatal Rats.

BACKGROUND: Within the premature infant intestine, oxygenation and motility play key physiological roles in healthy development and disease such as necrotizing enterocolitis. To date, there are limited techniques to reliably assess these physiological functions that are also clinically feasible for critically ill infants. To address this clinical need, we hypothesized that photoacoustic imaging (PAI) can provide non-invasive assessment of intestinal tissue oxygenation and motility to characterize intestinal physiology and health. METHODS: Ultrasound and photoacoustic images were acquired in 2-day and 4-day old neonatal rats. For PAI assessment of intestinal tissue oxygenation, an inspired gas challenge was performed using hypoxic, normoxic, and hyperoxic inspired oxygen (FiO2). For intestinal motility, oral administration of ICG contrast agent was used to compare control animals to an experimental model of loperamide-induced intestinal motility inhibition. RESULTS: PAI demonstrated progressive increases in oxygen saturation (sO2) as FiO2 increased, while the pattern of oxygen localization remained relatively consistent in both 2-day and 4-day old neonatal rats. Analysis of intraluminal ICG contrast enhanced PAI images yielded a map of the motility index in control and loperamide treated rats. From PAI analysis, loperamide significantly inhibited intestinal motility, with a 32.6% decrease in intestinal motility index scores in 4-day old rats. CONCLUSION: These data establish the feasibility and application of PAI to non-invasively and quantitatively measure intestinal tissue oxygenation and motility. This proof-of-concept study is an important first step in developing and optimizing photoacoustic imaging to provide valuable insight into intestinal health and disease to improve the care of premature infants.

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.

Photoacoustic Imaging for Non-Invasive Assessment of Physiological Biomarkers of Intestinal Injury in Experimental Necrotizing Enterocolitis.

Background: Necrotizing enterocolitis (NEC) is an often-lethal disease of the premature infants' intestinal tract that is exacerbated by significant difficulties in early and accurate diagnosis. In NEC disease, the intestine often exhibits hypoperfusion and dysmotility, which contributes to advanced disease pathogenesis. However, these physiological features cannot be accurately and quantitively assessed within the current constraints of imaging modalities frequently used in the clinic (plain film X-ray and ultrasound). We have previously demonstrated the ability of photoacoustic imaging (PAI) to non-invasively and quantitively assess intestinal tissue oxygenation and motility in a healthy neonatal rat model. As a first-in-disease application, we evaluated NEC pathogenesis using PAI to assess intestinal health biomarkers in a preclinical neonatal rat experimental model of NEC. Methods: NEC was induced in neonatal rat pups from birth to 4 days old via hypertonic formula feeding, full-body hypoxic stress, and lipopolysaccharide administration to mimic bacterial colonization. Healthy breastfed (BF) controls and NEC rat pups were imaged at 2- and 4-days old. Intestinal tissue oxygen saturation was measured with PAI imaging for oxy- and deoxyhemoglobin levels. To measure intestinal motility, ultrasound and co-registered PAI cine recordings were used to capture intestinal peristalsis motion and contrast agent (indocyanine green) transit within the intestinal lumen. Additionally, both midplane two-dimensional and volumetric three-dimensional imaging acquisitions were assessed for oxygenation and motility. Results: NEC pups showed a significant decrease of intestinal tissue oxygenation as compared to healthy BF controls at both ages (2-days old: 55.90% +/- 3.77% vs 44.12% +/- 7.18%; 4-days old: 56.13% +/- 3.52% vs 38.86% +/- 8.33%). Intestinal motility, assessed using a computational intestinal deformation analysis, demonstrated a significant reduction in the intestinal motility index in both early (2-day) and established (4-day) NEC. Extensive NEC damage was confirmed with histology and dysmotility was confirmed by small intestinal transit assay. Conclusions: This study presents PAI as a successful emerging diagnostic imaging modality for both intestinal tissue oxygenation and intestinal motility disease hallmarks in a rat NEC model. PAI presents enormous significance and potential for fundamentally changing current clinical paradigms for detecting and monitoring intestinal pathologies in the premature infant.

Myocardial Elasticity Imaging Correlates with Histopathology in a Model of Anthracycline-Induced Cardiotoxicity.

Background: There is considerable focus on developing strategies for identifying subclinical cardiac decline prior to cardiac failure. Myocardial tissue elasticity changes may precede irreversible cardiac damage, providing promise for an early biomarker for cardiac decline. Biomarker strategies are of particular interest in cardio-oncology due to cardiotoxic effects of anti-neoplastic therapies, particularly anthracycline-based chemotherapeutics. Current clinical methods for diagnosing cardiotoxicity are too coarse to identify cardiac decline early enough for meaningful therapeutic intervention, or too cumbersome for clinical implementation. Methods: Utilizing changes in myocardial elasticity as a biomarker for subclinical cardiac decline, we developed a biomechanical model-based elasticity imaging methodology (BEIM) to estimate spatial maps of left ventricle (LV) myocardial elasticity. In this study, we employ this methodology to assess changes in LV elasticity in a non-human primate model of doxorubicin-induced cardiotoxicity. Cardiac magnetic resonance imaging of five African Green monkeys was acquired at baseline prior to doxorubicin administration, 6-weeks, and 15-weeks after final doxorubicin dose and histopathological samples of the LV were taken at 15-weeks after final doxorubicin dose. Spatial elasticity maps of the mid-short axis plane of the LV were estimated at each image acquisition. Global and regional LV elasticity were calculated and changes between imaging time points was assessed. LV elasticity at baseline and final time point were compared to cardiomyocyte size and collagen volume fraction measurements calculated from histopathological staining of archived tissue bank samples and study endpoint tissue samples utilizing Pearson's correlation coefficients. Results: We identify significant changes in LV elasticity between each imaging time point both globally and regionally. We also demonstrate strong correlation between LV elasticity and cardiomyocyte size and collagen volume fraction measurements. Results indicate that LV elasticity estimates calculated using BEIM correlate with histopathological changes that occur due to doxorubicin administration, validating LV elasticity solutions and providing significant promise for use of BEIM to non-invasively elucidate cardiac injury. Conclusions: This methodology can show progressive changes in LV elasticity and has potential to be a more sensitive indicator of elasticity changes than current clinical measures of cardiotoxicity. LV elasticity may provide a valuable biomarker for cardiotoxic effects of anthracycline-based chemotherapeutics and cardiac disease detection.

Photoacoustic Imaging as a Novel Non-Invasive Biomarker to Assess Intestinal Tissue Oxygenation and Motility in Neonatal Rats.

Background: Within the premature infant intestine, oxygenation and motility play key physiological roles in healthy development and disease such as necrotizing enterocolitis. To date, there are limited techniques to reliably assess these physiological functions that are also clinically feasible for critically ill infants. To address this clinical need, we hypothesized that photoacoustic imaging (PAI) can provide non-invasive assessment of intestinal tissue oxygenation and motility to characterize intestinal physiology and health. Methods: Ultrasound and photoacoustic images were acquired in 2-day and 4-day old neonatal rats. For PAI assessment of intestinal tissue oxygenation, an inspired gas challenge was performed using hypoxic, normoxic, and hyperoxic inspired oxygen (FiO2). For intestinal motility, oral administration of ICG contrast agent was used to compare control animals to an experimental model of loperamide-induced intestinal motility inhibition. Results: PAI demonstrated progressive increases in oxygen saturation (sO2) as FiO2 increased, while the pattern of oxygen localization remained relatively consistent in both 2-day and 4-day old neonatal rats. Analysis of intraluminal ICG contrast enhanced PAI images yielded a map of the motility index in control and loperamide treated rats. From PAI analysis, loperamide significantly inhibited intestinal motility, with a 32.6% decrease in intestinal motility index scores in 4-day old rats. Conclusion: These data establish the feasibility and application of PAI to non-invasively and quantitatively measure intestinal tissue oxygenation and motility. This proof-of-concept study is an important first step in developing and optimizing photoacoustic imaging to provide valuable insight into intestinal health and disease to improve the care of premature infants. Highlights: Intestinal tissue oxygenation and intestinal motility are important biomarkers of intestinal physiology in health and disease of premature infants.This proof-of-concept preclinical rat study is the first to report application of photoacoustic imaging for the neonatal intestine.Photoacoustic imaging is demonstrated as a promising non-invasive diagnostic imaging method for quantifying intestinal tissue oxygenation and intestinal motility in premature infants.

Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness.

Purpose: Cardiotoxicity of antineoplastic therapies is increasingly a risk to cancer patients treated with curative intent with years of life to protect. Studies highlight the importance of identifying early cardiac decline in cancer patients undergoing cardiotoxic therapies. Accurate tools to study this are a critical clinical need. Current and emerging methods for assessing cardiotoxicity are too coarse for identifying preclinical cardiac degradation or too cumbersome for clinical implementation. Approach: In the previous work, we developed a noninvasive biomechanical model-based elasticity imaging methodology (BEIM) to assess mechanical stiffness changes of the left ventricle (LV) based on routine cine cardiac magnetic resonance (CMR) images. We examine this methodology to assess methodological reproducibility. We assessed a cohort of 10 participants that underwent test/retest short-axis CMR imaging at baseline and follow-up sessions as part of a previous publicly available study. We compare test images to retest images acquired within the same session to assess within-session reproducibility. We also compare test and retest images acquired at the baseline imaging session to test and retest images acquired at the follow-up imaging session to assess between-session reproducibility. Results: We establish the within-session and between-session reproducibility of our method, with global elasticity demonstrating repeatability within a range previously demonstrated in cardiac strain imaging studies. We demonstrate increased repeatability of global elasticity compared to segmental elasticity for both within-session and between-session. Within-subject coefficients of variation for within-session test/retest images globally for all modulus directions and a mechanical fractional mechanical stiffness anisotropy metric ranged from 11% to 28%. Conclusions: Results suggest that our methodology can reproducibly generate estimates of relative mechanical elasticity of the LV and provides a threshold for distinguishing true changes in myocardial mechanical stiffness from experimental variation. BEIM has applications in identifying preclinical cardiotoxicity in breast cancer patients undergoing antineoplastic therapies.

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.

Chief cell plasticity is the origin of metaplasia following acute injury in the stomach mucosa.

OBJECTIVE: Metaplasia arises from differentiated cell types in response to injury and is considered a precursor in many cancers. Heterogeneous cell lineages are present in the reparative metaplastic mucosa with response to injury, including foveolar cells, proliferating cells and spasmolytic polypeptide-expressing metaplasia (SPEM) cells, a key metaplastic cell population. Zymogen-secreting chief cells are long-lived cells in the stomach mucosa and have been considered the origin of SPEM cells; however, a conflicting paradigm has proposed isthmal progenitor cells as an origin for SPEM. DESIGN: Gastric intrinsic factor (GIF) is a stomach tissue-specific gene and exhibits protein expression unique to mature mouse chief cells. We generated a novel chief cell-specific driver mouse allele, GIF-rtTA. GIF-GFP reporter mice were used to validate specificity of GIF-rtTA driver in chief cells. GIF-Cre-RnTnG mice were used to perform lineage tracing during homoeostasis and acute metaplasia development. L635 treatment was used to induce acute mucosal injury and coimmunofluorescence staining was performed for various gastric lineage markers. RESULTS: We demonstrated that mature chief cells, rather than isthmal progenitor cells, serve as the predominant origin of SPEM cells during the metaplastic process after acute mucosal injury. Furthermore, we observed long-term label-retaining chief cells at 1 year after the GFP labelling in chief cells. However, only a very small subset of the long-term label-retaining chief cells displayed the reprogramming ability in homoeostasis. In contrast, we identified chief cell-originating SPEM cells as contributing to lineages within foveolar cell hyperplasia in response to the acute mucosal injury. CONCLUSION: Our study provides pivotal evidence for cell plasticity and lineage contributions from differentiated gastric chief cells during acute metaplasia development.

Developing a biomechanical model-based elasticity imaging method for assessing hormone receptor positive breast cancer treatment-related myocardial stiffness changes.

Purpose: Assessing cardiotoxicity as a result of breast cancer therapeutics is increasingly important as breast cancer diagnoses are trending younger and overall survival is increasing. With evidence showing that prevention of cardiotoxicity plays a significant role in increasing overall survival, there is an unmet need for accurate non-invasive methods to assess cardiac injury due to cancer therapies. Current clinical methods are too coarse and emerging research methods have not yet achieved clinical implementation. Approach: As a proof of concept, we examine myocardial elasticity imaging in the setting of premenopausal women diagnosed with hormone receptor positive (HR-positive) breast cancer undergoing severe estrogen depletion, as cardiovascular injury from early estrogen depletion is well-established. We evaluate the ability of our model-based cardiac elasticity imaging analysis method to indicate subclinical cancer therapy-related cardiac decline by examining differences in the change in cardiac elasticity over time in two cohorts of premenopausal women either undergoing severe estrogen depletion for HR-positive breast cancer or triple negative breast cancer patients as comparators. Results: Our method was capable of producing functional mechanical elasticity maps of the left ventricle (LV). Using these elasticity maps, we show significant differences in cardiac mechanical elasticity in the HR-positive breast cancer cohort compared to the comparator cohort. Conclusions: We present our methodology to assess the mechanical stiffness of the LV by interrogating cardiac magnetic resonance images within a computational biomechanical model. Our preliminary study suggests the potential of this method for examining cardiac tissue mechanical stiffness properties as an early indicator of cardiac decline.

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.

Human placental-derived stem cell therapy ameliorates experimental necrotizing enterocolitis.

Necrotizing enterocolitis (NEC), a life-threatening intestinal disease, is becoming a larger proportionate cause of morbidity and mortality in premature infants. To date, therapeutic options remain elusive. Based on recent cell therapy studies, we investigated the effect of a human placental-derived stem cell (hPSC) therapy on intestinal damage in an experimental NEC rat pup model. NEC was induced in newborn Sprague-Dawley rat pups for 4 days via formula feeding, hypoxia, and LPS. NEC pups received intraperitoneal (ip) injections of either saline or hPSC (NEC-hPSC) at 32 and 56 h into NEC induction. At 4 days, intestinal macroscopic and histological damage, epithelial cell composition, and inflammatory marker expression of the ileum were assessed. Breastfed (BF) littermates were used as controls. NEC pups developed significant bowel dilation and fragility in the ileum. Further, NEC induced loss of normal villi-crypt morphology, disruption of epithelial proliferation and apoptosis, and loss of critical progenitor/stem cell and Paneth cell populations in the crypt. hPSC treatment improved macroscopic intestinal health with reduced ileal dilation and fragility. Histologically, hPSC administration had a significant reparative effect on the villi-crypt morphology and epithelium. In addition to a trend of decreased inflammatory marker expression, hPSC-NEC pups had increased epithelial proliferation and decreased apoptosis when compared with NEC littermates. Further, the intestinal stem cell and crypt niche that include Paneth cells, SOX9+ cells, and LGR5+ stem cells were restored with hPSC therapy. Together, these data demonstrate hPSC can promote epithelial healing of NEC intestinal damage.NEW & NOTEWORTHY These studies demonstrate a human placental-derived stem cell (hPSC) therapeutic strategy for necrotizing enterocolitis (NEC). In an experimental model of NEC, hPSC administration improved macroscopic intestinal health, ameliorated epithelial morphology, and supported the intestinal stem cell niche. Our data suggest that hPSC are a potential therapeutic approach to attenuate established intestinal NEC damage. Further, we show hPSC are a novel research tool that can be utilized to elucidate critical neonatal repair mechanisms to overcome NEC.

Group 2 Innate Lymphoid Cells Coordinate Damage Response in the Stomach.

BACKGROUND & AIMS: Severe injury to the lining of the stomach leads to changes in the epithelium (reprogramming) that protect and promote repair of the tissue, including development of spasmolytic polypeptide-expressing metaplasia (SPEM) and tuft and foveolar cell hyperplasia. Acute gastric damage elicits a type-2 inflammatory response that includes production of type-2 cytokines and infiltration by eosinophils and alternatively activated macrophages. Stomachs of mice that lack interleukin 33 (IL33) or interleukin 13 (IL13) did not undergo epithelial reprogramming after drug-induced injury. We investigated the role of group 2 innate lymphoid cells (ILC2s) in gastric epithelial repair. METHODS: Acute gastric injury was induced in C57BL/6J mice (wild-type and RAG1 knockout) by administration of L635. We isolated ILC2s by flow cytometry from stomachs of mice that were and were not given L635 and performed single-cell RNA sequencing. ILC2s were depleted from wild-type and RAG1-knockout mice by administration of anti-CD90.2. We assessed gastric cell lineages, markers of metaplasia, inflammation, and proliferation. Gastric tissue microarrays from patients with gastric adenocarcinoma were analyzed by immunostaining. RESULTS: There was a significant increase in the number of GATA3-positive ILC2s in stomach tissues from wild-type mice after L635-induced damage, but not in stomach tissues from IL33-knockout mice. We characterized a marker signature of gastric mucosal ILC2s and identified a transcription profile of metaplasia-associated ILC2s, which included changes in expression of Il5, Il13, Csf2, Pd1, and Ramp3; these changes were validated by quantitative polymerase chain reaction and immunocytochemistry. Depletion of ILC2s from mice blocked development of metaplasia after L635-induced injury in wild-type and RAG1-knockout mice and prevented foveolar and tuft cell hyperplasia and infiltration or activation of macrophages after injury. Numbers of ILC2s were increased in stomach tissues from patients with SPEM compared with patients with normal corpus mucosa. CONCLUSIONS: In analyses of stomach tissues from mice with gastric tissue damage and patients with SPEM, we found evidence of type 2 inflammation and increased numbers of ILC2s. Our results suggest that ILC2s coordinate the metaplastic response to severe gastric injury.

Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection.

Purpose: For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. Approach: This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of n = 756 simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of n = 84 deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. Results: In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. Conclusions: These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.

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.

Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy.

OBJECTIVE: Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties. METHODS: In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data. RESULTS: This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8 °C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions. CONCLUSION: These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties. SIGNIFICANCE: The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA.

Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the "equivalent dose metric." This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response.

Translating preclinical MRI methods to clinical oncology.

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases.

PURPOSE: Stereotactic radiosurgery (SRS) is used for local control treatment of patients with intracranial metastases. As a result of SRS, some patients develop radiation-induced necrosis. Radiographically, radiation-induced necrosis can appear similar to tumor recurrence in magnetic resonance (MR) T1 -weighted contrast-enhanced imaging, T2 -weighted MR imaging, and Fluid-Attenuated Inversion Recovery (FLAIR) MR imaging. Radiographic ambiguities often necessitate invasive brain biopsies to determine lesion etiology or cause delayed subsequent therapy initiation. We use a biomechanically coupled tumor growth model to estimate patient-specific model parameters and model-derived measures to noninvasively classify etiology of enhancing lesions in this patient population. METHODS: In this initial, preliminary retrospective study, we evaluated five patients with tumor recurrence and five with radiation-induced necrosis. Longitudinal patient-specific MR imaging data were used in conjunction with the model to parameterize tumor cell proliferation rate and tumor cell diffusion coefficient, and Dice correlation coefficients were used to quantify degree of correlation between model-estimated mechanical stress fields and edema visualized from MR imaging. RESULTS: Results found four statistically relevant parameters which can differentiate tumor recurrence and radiation-induced necrosis. CONCLUSIONS: This preliminary investigation suggests potential of this framework to noninvasively determine the etiology of enhancing lesions in patients who previously underwent SRS for intracranial metastases.

Applying dynamic contrast enhanced MSOT imaging to intratumoral pharmacokinetic modeling.

Examining the dynamics of an agent in the tumor microenvironment can offer critical insights to the influx rate and accumulation of the agent. Intratumoral kinetic characterization in the in vivo setting can further elicudate distribution patterns and tumor microenvironment. Dynamic contrast-enhanced Multispectral Optoacoustic Tomographic imaging (DCE-MSOT) acquires serial MSOT images with the administration of an exogenous contrast agent over time. We tracked the dynamics of a tumor-targeted contrast agent, HypoxiSense 680 (HS680), in breast xenograft mouse models using MSOT. Arterial input function (AIF) approach with MSOT imaging allowed for tracking HS680 dynamics within the mouse. The optoacoustic signal for HS680 was quantified using the ROI function in the ViewMSOT software. A two-compartment pharmacokinetics (PK) model constructed in MATLAB to fit rate parameters. The contrast influx (kin) and outflux (kout) rate constants predicted are kin = 1.96 × 10-2 s-1 and kout = 9.5 × 10-3 s-1 (R = 0.9945).

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer.

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.