Texas Medical Association

Abstract 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.

Abstract 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.

The aim of this study was to identify a simple surrogate to predict the future risk of multiple lost-time injuries. Employees of an academic medical center who sustained 5,906 injuries were followed from 1994 to 2017 or 1,046,218 person years. The odds ratio of having three or more lost-time injuries during their entire duration of employment was 2.12 (95% confidence interval: 1.60 to 2.79) for employees having their first lost-time injury within the first 6 months of employment versus those injured after that, controlling for demographics and employment duration. For each increasing year before the first lost-time injury, the probability of having three or more lost-time injuries decreased by 13%. Employment duration before the first lost-time injury may be used to predict future lost-time injuries without detailed information of underlying risk factors.

Simulation is increasingly used as a training tool for acute care medical-surgical nurses to improve patient safety outcomes. A synthesis of the evidence is needed to describe the characteristics of research studies about acute care nurse simulation trainings and patient safety. An additional purpose is to examine the effects of acute care registered nurse (RN) simulation trainings on patient safety outcomes. Five Internet databases were searched for articles published on any date through October 2018 examining the effect of RN simulation trainings on patient safety outcomes in the adult acute care setting. N=12 articles represented 844 RNs of varying experience levels and 271 interprofessional participants. Nine studies (75%) used high-fidelity scenarios developed locally about high risk but infrequent events. Five studies (42%) incorporated interdisciplinary team members in the scenarios and/or outcome evaluations. Outcome measures were self-reported, direct observation, or clinical indicators. All studies in this review achieved improved patient safety outcomes. It is unknown how outcomes vary for different groups of RNs because of insufficient gender, ethnicity/race, and age reporting. Findings support the design of simulation training research studies for patient safety outcomes and use of simulation training and research in acute care RNs. Additional high-quality research is needed to support this field. Future studies should include descriptors that characterize the sample (i.e., age, gender, education level, type of nursing degree, ethnicity or race, or years of experience); incorporate interdisciplinary teams; evaluate a combination of outcome measure types (i.e., self-report, direct observation, and clinical outcomes) both proximal and distal to the simulation; and that utilize standardized scenarios, validated outcome measure instruments, and standardized debriefing tools.

Abstract Introduction: The primary purpose of this study is to evaluate the repeatability and reproducibility of quantitative breast MRI across community imaging centers with the ultimate goal of using these techniques to predict breast cancer response early in the course of neoadjuvant therapy (NAT). Dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted MRI (DW-MRI), and magnetization transfer MRI (MT-MRI) performed early in the course of breast NAT has the potential to predict eventual response prior to changes in tumor size. This enables tailoring of treatment plans and the opportunity to substitute ineffective therapies with alternative approaches. We present preliminary results on the reproducibility and repeatability of T1, apparent diffusion coefficient (ADC), and magnetization transfer ratio (MTR) measurements in normal breast fibroglandular tissue (FGT) in the community setting. Experimental Design: MRI was performed at two community imaging centers and one academic research facility using 3T Siemens Skyra scanners equipped with 8- or 16-channel breast coils. To assess repeatability of the imaging techniques, normal subjects (N=10, ages 22-62) were scanned twice, separated by subject repositioning. To assess reproducibility across sites, normal subjects (N=3) were scanned at three imaging centers. To assess quantitative T1 measurements, subjects were scanned using a spoiled gradient echo (SPGE) sequence and variable flip angles (2, 4, 6, …, 20) with TR/TE = 7.9/2.71 ms and corrected for B1inhomogeneity. To assess the ADC, subjects were scanned using an echo-planar monopolar spin echo sequence with the following parameters: TR/TE = 3000/52 ms and b-values = 0, 200, 800 s/mm2. MT-MRI was acquired using two gradient echo sequences with TR/TE = 48.0/6.40 ms, one with the inclusion of a 1500 Hz off-resonance saturation pulse. FGT was segmented using k-means clustering. Women undergoing NAT for breast cancer are being recruited and scanned with DCE-MRI, DW-MRI, and MT-MRI at baseline (prior to beginning therapy) and three early time points during the course of NAT to evaluate early prediction of response to therapy. Results: Reproducibility scans of normal breast FGT yielded an average difference of 8.4% in T1 measurement, 7.0% in ADC measurement, and 12.7% in MTR measurement between sites. Repeatability scans of the same subject's FGT showed an average percent difference of 6.7% in T1 measurement, 4.5% in ADC measurement, and 11.6% in MTR measurement between the two scans. A multi-site trial performing quantitative DCE-MRI, DW-MRI, and MT-MRI in patients undergoing breast NAT in the community setting (N=16 at the time of submission) has been ongoing to predict response to NAT using quantitative MRI. Conclusion: Quantitative DCE-, DW-, and MT-MRI of the breast is both repeatable and reproducible across MRI scanners in community imaging centers. A quantitative breast MRI protocol can be deployed at community imaging centers for breast cancer patients. These results and ongoing work highlight the feasibility of future clinical dissemination of quantitative MRI for predicting early response to NAT, therefore expanding these novel techniques to a widespread patient population. We acknowledge the support of CPRIT RR160005. Citation Format: Sorace AG, Virostko J, Wu C, Jarrett AM, Barnes SL, Luci J, Patt DA, Goodgame B, Avery S, Yankeelov TE. Repeatability and reproducibility of quantitative breast MRI in community imaging centers: Preliminary results [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-08.

Introduction/purpose: A significant complication in the intervention of acute ischemic stroke is hemorrhagic transformation (HT). It has been postulated that perfusion permeability imaging showing increased blood brain barrier permeability can be used to predict hemorrhagic transformation and possibly alter therapies. Materials and Methods: We retrospectively reviewed 1040 sequential CT perfusion scans with permeability surface area product maps calculated using the Patlak model for all patients that exhibited stroke like symptoms between October 2011 and November 2012. The size of the permeability surface product was ranked on a qualitative three-part scale of small, moderate and large permeability changes. A change smaller than 25% of the image was considered a small result. A moderate result is a permeability change that is approximately 25% of the image. A large permeability change exceeds 25% of the image. Follow up non-contrast CT images (>24 hours but <15 days after initial perfusion imaging) were used to determine if HT had occurred in the cases where an increase in permeability surface product was observed. Results: There was a positive increase in permeability maps in 142 of the 1040 cases. The size of the permeability change was moderate to large in 101 of the positive cases (71%). Hemorrhagic transformation was observed in 12 patients that showed an increase in permeability surface product (8.4%). Of the cases that resulted in HT, nine (75%) resulted in an HI1 and HI2 subtypes. There were three (25%) of the more severe parenchymal hemorrhages (PH1, PH2) observed. Out of the 12 positive hemorrhagic transformations four (33%) were treated with iv-TPA and two (17%) received endovascular thrombectomies, while six (50%) did not receive TPA or endovascular intervention. Of the major parenchymal hemorrhages (PH1/2) two occurred after iv-TPA treatment of the stroke, with the other arising after endovascular thrombectomy. No difference was found in the size or degree of the permeability changes and the incidence of HT. Conclusions: Elevated permeability on CT perfusion imaging had no relevant predictive value for hemorrhagic transformation in acute ischemic stroke at our institution.