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A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition.Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters KThe DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points.The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for KIt is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

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.

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.

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.

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.

Background Iron deficiency anemia is the most common hematologic disorder in the United States and worldwide. Yet, clinical guidelines for the identification and management of this disorder in the emergency department are lacking. Objective of Review This clinical review examines strategies for identifying and treating iron deficiency anemia in the emergency department, with a focus on the role of oral iron therapy, intravenous iron therapy, as well as red blood cell transfusion. The article highlights both the available evidence on this topic and the need for future research. Discussion The diagnosis of iron deficiency anemia has important clinical implications and, although testing is generally straightforward, it may be under-recognized. The scant literature available describing emergency department practice patterns for iron deficiency anemia suggests there is room for improvement. In particular, intravenous iron may be underutilized and red blood cell transfusions administered too liberally. Conclusions Iron deficiency anemia is common and many patients can be treated effectively with oral iron. For selected patients with moderate-to-severe iron deficiency anemia, intravenous iron is safe and more effective than oral iron. Red blood cell transfusions should be used rarely for hemodynamically stable patients with iron deficiency irrespective of hemoglobin levels.

Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Methods Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Results Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p Conclusions Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

The aim of this study was to identify correlates of quality of life (QOL) for socioeconomically disadvantaged cancer patients receiving care in the "safety net" health system.This cross-sectional study used linear regressions to determine the effect of patient reported outcome measures (PRO) on QOL.Sample/Methods: Cancer patients (More than 60% of patients reported an annual income below $24,999. Forty-five percent of patients were either uninsured or county-funded. Depression, pain, and financial toxicity were found to be consistently significant correlates of QOL.Implications: Cancer patients with existing financial strain have unique psychosocial stressors. This study provides insight into the relationship between these stressors, and the need for targeted screening and intervention that address such aspects of care.

Objective To decrease the number of orders and total hospital spend for inpatient use of antineoplastic drugs of interest, while evaluating each case for urgent or emergent need for administration. Methodology This study is a multicenter, retrospective, cost-evaluation, cohort study performed in five Ascension Seton hospitals in the Austin, Texas area between 1 January 2013 and 31 December 2018. Patients were identified via a dispense analysis report for the antineoplastic drugs of interest. Results An overall reduction of 56% was seen in orders processed with a 62% decrease in annual hospital spending after implementation of the criteria-for-use algorithm. When results were evaluated without including rituximab orders, a reduction of 17% was seen in orders processed with a 21% decrease in annual hospital spending. Discussion and conclusion The decreases in our primary outcomes were primarily driven by a reduction in the use of one drug, rituximab. Overall, implementation of a criteria-for-use algorithm was effective in reducing both overall number of orders and hospital spending for restricted antineoplastic agents.

BACKGROUND The incidence of colorectal cancer (CRC) in adults younger than 50 years has increased in the United States over the past decades according to Surveillance, Epidemiology, and End Results data. National guidelines conflict over beginning screening at the age of 45 or 50 years. METHODS This was a retrospective study of National Cancer Data Base data from 2004 to 2015. The Cochran-Armitage test for trend was used to assess changes in the proportion of cases diagnosed at an age younger than 50 years. RESULTS This study identified 130,165 patients diagnosed at an age younger than 50 years and 1,055,598 patients diagnosed at the age of 50 years or older. The proportion of the total number of patients diagnosed with CRC at an age younger than 50 years rose (12.2% in 2015 vs 10.0% in 2004; P

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.

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.

Historically, the Hispanic population in the United States has had a lower incidence of cancer than the matched non-Hispanic population, despite disparities in access to health care, screening, and prevention. Our experience in Austin, Texas, directly contradicts this. We have seen a disproportionate amount of young Hispanic patients with advanced malignancies, particularly of the breast. The aim of this study was to compare the incidence of advanced breast malignancies. We performed a retrospective review over a 10-year period (2003–2013) of all newly diagnosed breast cancer patients. Data were collected from the cancer registry. Patients were divided into two groups: Hispanic versus non-Hispanic descent, with a subgroup of those aged less than 50 years. Primary outcome was the incidence of advanced cancers (stage 3 or 4). There were a total of 3968 breast cancer patients seen in our Shivers Cancer Center from 2003 to 2013, with an overall incidence of advanced breast cancer of 11.5 per cent. Of the patients aged less than 50 years, 14.2 per cent had advanced breast cancer. However, the rate among Hispanic patients was 21.3 per cent, whereas in non-Hispanic patients it was 13.5 per cent, P = 0.002. Being Hispanic was found to be an independent predictor of having advanced malignancies at a young age (odds ratio 1.7, confidence interval 1.1–2.5, P = 0.01). Here in Austin, Texas, we have found a higher overall incidence of breast cancer among young Hispanic women. This is important to recognize because more efforts may be required to increase screening and health-care access to this population.

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.

Vertebral compression fractures (VCFs) represent a significant cause of disability and primarily result from either underlying vertebral body neoplasms or osteoporosis. Vertebroplasty (VP) is a procedure commonly utilized to repair pathologic VCFs in order to manage pain and reinstate vertebral body height. However, there is a paucity of literature on how to manage painful multilevel VCFs with concomitant bilateral pedicle fractures. We describe a patient with a primary prostatic carcinoma and VCFs of the third and fourth lumbar vertebrae (L3 and L4, respectively) with concomitant bilateral pedicle fractures secondary to metastatic disease. Due to the degree of damage to the L3 and L4 vertebral bodies and pedicles, a VP performed via a percutaneous approach was deemed to be too high risk. VP for L3 and L4 was instead performed by utilizing stereotactic spine navigation and an intraoperative O-arm (Medtronic Corporation, Minneapolis, Minnesota). Our result indicates a potential role for stereotactic spine navigation in vertebroplasty for complex pathologic VCFs.

Introduction: We show that the combination of quantitative magnetic resonance imaging (MRI) and mathematical modeling can accurately predict tumor response for individual patients, and we demonstrate the selection of personalized therapeutic regimens using our mathematical model to vary, in silico, a range of clinically feasible treatment plans to achieve the greatest tumor control. Methods: Breast cancer patients (N = 18) were scanned by MRI at three time points during neoadjuvant therapy (NAT): 1) prior to NAT, 2) after one cycle of their initial NAT regimen, and 3) after the completion of their initial NAT regimen. Specifically, diffusion-weighted MRI data characterizing tumor cellularity was collected to estimate tumor cell burden, and dynamic contrast-enhanced MRI data was collected to estimate the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of the administered chemotherapies. To predict tumor response, we calibrated our tissue scale, 3D, biophysical mathematical model to each patient's MRI data set using their first two scans. Using the calibrated, patient-specific parameters, the model was projected forward to the third scan time. The predicted total tumor cellularity, volume, and longest axis were compared to the actual values measured from the patient's third scan. Following evaluation of the model's predictive ability, we then employed the model to identify potentially superior, clinically relevant, alternative dosing regimens for each patient. The alternative regimens were defined using the same total amount of drug each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Statistical analysis was completed with Pearson correlation and Wilcoxon signed rank test. Results: The model's predictions of tumor response are significantly correlated to the measured tumor burden at the time of scan 3 (r2 > 0.88, p < 0.01) for total cellularity, total volume, and longest axis. For the alternative dosing regimens assessed, the model predicted that individual patients could have achieved, on average, an additional 21% (0-46%) reduction in total cellularity. The optimal dosing regimens chosen by the model were predicted to significantly outperform standard regimens for tumor control (p < 0.001). Conclusions: These results suggest that the mathematical model can be predictive of tumor response by MRI in the clinical setting using data at the earliest times of therapy. With in silico studies, we illustrate how therapeutic regimens can be selected for individual patients for better tumor control, revealing that standard regimens may not be the most effective for every patient. These results represent a first step towards mathematically-based, personalized patient regimens. Future work aims to optimize therapy regimens via established optimal control theory methods. Citation Format: Angela M. Jarrett, Ernesto A. Lima, David A. Hormuth, Chengyue Wu, John Virostko, Anna G. Sorace, Julie C. DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Patient-specific neoadjuvant regimens for breast cancer identified via image-driven mathematical modeling [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 5485.

Background: Dynamic contrast-enhanced MRI (DCE-MRI) plays a crucial role in breast cancer detection and monitoring of therapeutic response by assessing the tumor vascular network. The MRI contrast agent enhances not only tumor tissue, but also normal breast tissue, a phenomena known as background parenchymal enhancement (BPE). The degree of BPE can be variable between different individuals, is influenced by the hormonal milieu, and displays variable anatomical and kinetic patterns. Changes in BPE intensity over the course of neoadjuvant therapy (NAT) have been found to correlate with response to therapy in both the ipsilateral and contralateral breast. The present study assesses changes in the rate of BPE over the course of NAT to predict eventual response to NAT. Importantly, this study was conducted in collaboration with community-care centers (i.e., not research-oriented medical centers). Methods: Women with locally advanced breast cancer (N = 19) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with a 8- or 16-channel breast coils. Gadolinium-based contrast agent (0.1 mmol/kg of Multihance or 10 mL of Gadovist) was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was collected in 10 sagittal slices with a temporal resolution of 7.27 sec for a total acquisition time of eight minutes. The tumor was semi-automatically segmented using a manually drawn region of interest (ROI) followed by fuzzy c-means clustering of a post-contrast high-resolution anatomical scan. Fibroglandular parenchyma was segmented from adipose tissue using fuzzy c-means clustering and further segmented into three concentric rings at prescribed radial distances from the tumor boundary. Enhancement time courses were temporally smoothed using a moving average filter of width 5 and normalized to baseline intensity to yield relative enhancement time courses. The enhancement slope, defined as the slope of the curve at half maximal intensity, was calculated from the kinetic uptake curve for the tumor, adipose, and concentric rings of fibroglandular tissue surrounding the tumor. Results: Prior to commencing NAT, women with faster tumoral enhancement had faster fibroglandular enhancement (r = 0.62, p < 0.05). There was no correlation between tumor and adipose enhancement rate or fibroglandular and adipose enhancement rate. Among concentric rings of fibroglandular tissue, the tissue most distal to the tumor enhanced faster than tissue more proximal to the tumor (p < 0.05). Over the course of NAT, the rate of fibroglandular enhancement tended to slow (p < 0.01). Women who ultimately achieved pathological complete response (pCR; n = 6) displayed a faster rate of fibroglandular enhancement at the 4th MRI than women with residual tumor at the time of resection (n = 13). This correlation was preserved (p < 0.01) in a subset of patients who received AC◊T therapy, the most common treatment arm in this study (n = 4 pCR, n = 8 non-pCR). Furthermore, this relationship persisted when the fibroglandular enhancement rate was normalized by the tumoral enhancement rate in both the full patient cohort and in the 12 patients who received AC◊T (p < 0.05, both cohorts). Conclusions: These results demonstrate that the rate of fibroglandular tissue enhancement in patients receiving NAT for breast cancer reflects response to therapy. The increased rate of fibroglandular enhancement in women achieving pCR may reflect treatment-mediated vascular remodeling in healthy breast tissue. Incorporating quantitative characterization of parenchymal enhancement kinetics into treatment response models warrants further investigation. Citation Format: John Virostko, Erin M Higgins, Chengyue Wu, Anna G Sorace, Debra Patt, Boone Goodgame, Thomas E Yankeelov. The rate of parenchymal enhancement during DCE-MRI reflects response to neoadjuvant therapy [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 PD9-07.

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.To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and TProspective.Ice-water DW-MRI and T3T Siemens Skyra MRI quantitative DW-MRI and TQuantitative DW-MRI and TAverage 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.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. TCommunity radiology practices can perform repeatable, reproducible, and accurate quantitative T1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Background The addition of targeted agents to thoracic radiation has not improved outcomes in patients with locally advanced non–small-cell lung cancer (NSCLC). To improve cure rates in locally advanced NSCLC, effective targeted therapies need to be identified that can be given safely with radiation therapy. Temsirolimus is an inhibitor of the mammalian target of rapamycin (mTOR) pathway and has single-agent activity in lung cancer. Inhibition of the mTOR pathway has been found to augment the cytotoxic effect of radiation in preclinical studies. There is scant clinical experience with mTOR inhibitors and radiation. Patients and Methods This was a phase I study evaluating the combination of temsirolimus with thoracic radiation in patients with NSCLC. Results Ten patients were enrolled in the study. The dose-limiting toxicities included sudden death, pneumonitis, and pulmonary hemorrhage. The maximum tolerated dose of temsirolimus that could be administered safely with concurrent radiotherapy (35 Gy in 14 daily fractions) was 15 mg intravenously weekly. Of the 8 evaluable patients, 3 had a partial response and 2 had stable disease. Conclusion The combination of temsirolimus 15 mg weekly and thoracic radiation is well tolerated and warrants further investigation, perhaps in a molecularly defined subset of patients.

11613 Background: Multiple studies show that patients with advanced cancer often believe the goal of treatment is cure. Our interprofessional team designed the Communicating Oncologic Prognosis with Empathy (COPE) guide to enhance communication for patients, families, and the healthcare team. This unique, single-page tool allows treating physicians to estimate prognosis for patients, including likelihood of living 6 mo, 1y, 2y, and 5y, with or without treatment. Methods: Patients with incurable cancer were enrolled at one oncology clinic. Using a pre/post-test design, participants completed the validated prognosis and treatment perception survey (PTPQ) then met with their oncologist. The COPE guide was used for the discussion and the patient received a copy of the completed COPE. The COPE guide was placed in the patient chart and the healthcare team (social work, spiritual care etc.) used it to provide patient support and explore the patient’s response to the information. Post-test was completed 4-8 weeks later. Qualitative exit interviews were used to assess patient reported impact on communication with medical team, caregivers and family, and quality of life. Results: 43 patients were enrolled and 25 completed all study measures. All patients had advanced cancer with 96% of patients (24) extremely unlikely to be cured ( < 1% chance). Oncologist’s treatment goals were better quality of life and longer life. All participants completing the study found the communication guide helpful, scoring 9-10 out of 10. 95% of patients enrolled wanted to hear details about their prognosis. Pre-intervention, 28% (7) of patients reported their primary goal of treatment as cure (n = 7; 28%) or to extend life as long as possible (n = 7; 28%). Pre-intervention, patient’s perceptions of their oncologist’s goals of treatment were cure (n = 11; 44%) or extend life (n = 8; 32%). At post-test, 8% of participants (2) inaccurately reported goal of treatment as cure (p < 0.05). Conclusions: These findings highlight major gaps in patients’ understanding of their prognosis. COPE is a promising mechanism to enhance patient-centered communication about prognosis and treatment for patients with incurable cancer.

Introduction: Early response assessment to neoadjuvant therapy (NAT) for locally advanced breast cancer would allow for more accurate prognosis and provide the opportunity to replace an ineffective treatment with an alternative regimen. This could potentially increase systemic treatment efficacy and avoid unnecessary side effects from ineffective therapies. Quantitative MRI has been shown to be beneficial in predicting breast tumor response to treatment early during the course of NAT within many academic environments. Importantly, integrating quantitative imaging techniques into the community-based setting has the potential to reach a large percentage of breast cancer patients, as most patients receive their care at private practice or community hospitals. This study evaluated the ability to implement quantitative dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) data in the community setting to predict the eventual response of breast tumors to NAT. Experimental Design: Women undergoing NAT for breast cancer (N=16) were scanned with DCE-MRI and DW-MRI at baseline (prior to beginning therapy, t1) and three longitudinal time points during the course of NAT to evaluate early response to therapy (t2, t3, t4). MRI was performed at two community imaging centers using a 3T Siemens Skyra scanners equipped with 8- or 16-channel breast coils. DW-MRI was acquired with a spin echo sequence with TR/TE = 3000/52 ms, b-values of 200, 800 s/mm2 and used to compute the apparent diffusion coefficient (ADC) values for every voxel. DCE-MRI data was collected (following a pre-contrast T1 map) with TR/TE/α = 7.02 ms/4.60 ms/6o, and a temporal resolution of 7.27 sec for eight minutes. A catheter placed within an antecubital vein delivered gadolinium-based contrast agent (0.1 mmol/kg of Multihance or 10 mL of Gadovist) at 2 mL/sec via a power injector after the acquisition of the first minute of dynamic scans (baseline).Quantitative measures of ADC (evaluating cellularity from DW-MRI) and Ktrans(evaluating vascular perfusion and permeability from DCE-MRI) were calculated for the segmented tumor volume. Imaging was compared to pathology reports at the conclusion of NAT. Results:The patients (n = 6) achieving a pathological complete response (pCR) revealed a 12.9% ± 19.1% increase in the mean ADC values of the tumor from t1 and t2. Conversely, patients (n = 10) that had residual disease burden after NAT (i.e., a non-pCR) had a decreased ADC, revealing a -8.0% ± 19.2% change between t1 and t2 (p = 0.06). The mean Ktrans values of the tumor decreased showing a change of -61.4% ± 18.2% from t1 to t2 in the pCR patients. Conversely, non-pCR patients had a 10.2% ± 80.4% increase in Ktrans between t1 and t2 (p = 0.14) Conclusion:Preliminary evidence reveals that quantitative DCE-MRI and DW-MRI can be implemented in community-based imaging settings to predict the response of breast tumors to NAT. Thus, our results provide evidence that quantitative DW-MRI and DCE-MRI can be disseminated across community imaging facilities, thereby dramatically increasing the patient population for which these techniques can serve. We acknowledge the support of CPRIT RR160005. Citation Format: Sorace AG, Virostko J, Wu C, Jarrett AM, Barnes SL, Ekrut D, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative breast MRI to predict response to neoadjuvant therapy in community imaging centers: Preliminary results [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-01-02.

490 Background: The incidence of colorectal cancer (CRC) in adults younger than age 50 has been increasing in the US since 1970. The US Preventive Services Task Force recommends screening for CRC beginning at age 50, while the American Cancer Society recently revised its guidelines to advise screening at age 45. We analyzed the National Cancer Database (NCDB) from 2004 through 2015 to determine whether the proportion of patients diagnosed younger than 50 has changed during this time. Methods: This was a retrospective study of NCDB data, which includes more than 70 percent of newly diagnosed cancer cases in the US. We examined the proportion of patients diagnosed prior to age 50 as our primary endpoint. We used the Cochran–Armitage test for trend to assess changes in the proportion of cases diagnosed at age < 50 years old as a function of year of diagnosis. Results: We identified 152,749 patients diagnosed under age 50 and 1,033,014 patients diagnosed at age 50 or greater. Over the study period, the proportion of the total number of patients diagnosed with colorectal cancer under age 50 increased (14.3% in 2015 vs. 11.5% in 2004, p < 0.0001). Younger adults with CRC presented with more advanced disease, with 49.9% stage III or IV disease, as compared to 40.0% in those diagnosed over age 50. Both men and women had a rising proportion of cases diagnosed younger than 50 (p < 0.0001) over time. In men, only non-Hispanic whites had an increase in diagnosis at ages less than 50 (p < 0.0001), while in women, all racial and ethnic subgroups had an increase in younger diagnoses over time (p < 0.01). All income quartiles (p < 0.001) demonstrated a proportional increase in younger adults over time, with the highest income quartile having the highest proportion of younger cases. The proportion of younger-onset CRC rose in urban areas (p < 0.001), but did not rise in rural areas. Conclusions: The proportion of persons diagnosed with CRC under age 50 in the US has continued to increase over the past decade. Younger adults presented with more advanced disease, suggesting that screening could improve outcomes.

38 Background: Clear communication between patients and providers is imperative to maximize patient outcomes, particularly for patients with incurable cancer who require prompt engagement in decision-making. In response to the need to engage in quality patient-centered communication, an interprofessional team, representing medicine, nursing, social work, and spiritual care developed a simple, single-page tool that summarizes the prognosis and treatment plan for patients with incurable cancers. This pilot study evaluates the communication guide, Communicating Oncologic Prognosis with Empathy (COPE), for discussing prognosis and treatment options for cancer patients. Methods: A pilot test of 30 participants with incurable cancer was completed at an oncology clinic. The communication guide was evaluated using a pre- and post-test design, participants completed a prognosis and treatment perception survey (PTPQ) prior to their first oncology appointment. Patients then met with their oncologist and the COPE guide was used to guide the discussion and patients received a copy of the COPE guide. The COPE guide was placed in the patient chart and other members of the healthcare team were able to use the tool to provide the patient with support and explore the patient’s response to information received. Patients completed the post-test PTPQ at 4 week follow up and completed exit interviews to assess patient reported impact on communication with medical team, caregivers, and quality of life. Results: Key findings revealed participants prefer receiving detailed information about prognosis and have differing perceptions about goals of treatment compared to the oncology team. The tool seemed to facilitate mostly accurate information recall regarding prognosis and treatment options. Conclusions: Despite the high value patients placed on knowing about prognosis and quality of communication with their medical team, the majority reported incongruent goals of treatment and likelihood of cure compared with their oncologist, highlighting major gaps in patients’ understanding of their prognosis. The COPE guide serves as a promising mechanism to enhance patient-centered communication about prognosis and treatment for patients with incurable cancer.

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 19-25 nt and play important roles in gene regulation in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis of stage I NSCLC. MiRNAs extracted from 527 stage I NSCLC patients were profiled on the human miRNA expression profiling v2 panel (Illumina). The expression profiles were analyzed for their association with cancer subtypes, lung cancer brain metastasis and recurrence/relapse free survival (RFS). MiRNA expression patterns between lung adenocarcinoma and squamous cell carcinoma differed significantly with 171 miRNAs, including Let-7 family members and miR-205. Ten miRNAs associated with brain metastasis were identified including miR-145*, which inhibit cell invasion and metastasis. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 34 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype, whereas the second containing 27 miRNAs was adenocarcinoma specific. Both signatures were validated using formalin-fixed paraffin embedded and/or fresh frozen tissues in independent data set with 170 stage I patients. Our findings have important prognostic or therapeutic implications for the management of stage I lung cancer patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.

Background: Despite undergoing curative resection, nearly a third of patients with stage I non-small cell lung cancer (NSCLC) die of recurrent disease. There are no reliable clinical or molecular predictors of relapse in patients with resected stage I NSCLC. Identifying patients at risk for relapse after surgical resection is one of the important challenges today. MicroRNAs (miRNAs) regulate hundreds of genes central to maintaining a cancer phenotype. Methods: In an exploratory study, we determined whether expression of six miRNAs (let-7a, miR-7, miR-21, miR-155, miR-210, and miR-221) previously reported to correlate with invasiveness or outcome in various human malignancies were associated with tumor recurrence in patients with resected stage I NSCLC. We measured expression of these miRNAs in formalin-fixed, paraffin-embedded tissue from both tumor and matched normal lung in a set of 46 patients with surgically resected T1 or T2 stage I NSCLC. Results: Averaged triplicate data showed that tumors which recurred had 0.14-fold lower miR-221 expression than those which did not recur ( p = 0.0036). In addition, increased miR-221in tumor tissue when compared with adjacent normal appearing lung in the same patient also correlated with nonrecurrence ( p = 0.0011). Parallel measurement of expression of selected downstream target genes regulated by miR-221, specifically, CDKN1B , CDKN1C , paralemmin-2, and CXCL12 , showed a near significant ( p = 0.0522) down-regulation of CDKN1C in tumors of patients with no recurrent disease, consistent with increased miR-221 activity in the same group. Conclusion: If confirmed in prospective studies, miRNA expression in resected NSCLC could potentially identify those at high risk of relapse after surgery.

Purpose: Quantification of accurate and early response to neoadjuvant therapy (NAT) provides the opportunity to replace an ineffective treatment with an alternative regimen, thereby potentially avoiding ineffective systemic therapy. Quantitative magnetic resonance imaging (MRI) has been shown to predict breast cancer response to treatment early during the course of NAT within academic medical centers. Integrating quantitative imaging techniques into community-based medical practices has the potential to reach a larger percentage of breast cancer patients, and allow more community center participation in clinical trials that require quantitative imaging. This study evaluated the reproducibility and accuracy of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in the community setting and the ability to implement these techniques to predict the eventual response of breast tumors to NAT. Experiment Procedure: MRI was performed at two community imaging centers and one academic research facility using a 3T Siemens Skyra scanners equipped with a breast coil. To assess reproducibility across sites, normal subjects (N=3) were scanned at three imaging centers. Quantitative T1 (required for pharmacokinetic modeling of DCE-MRI) and apparent diffusion coefficient (ADC) maps were calculated of normal fibroglandular breast tissue. Accuracy was tested through evaluation of T1 and DW-MRI in phantoms. Women undergoing NAT for breast cancer (N=10) were scanned with DCE-MRI and DW-MRI at baseline (prior to beginning therapy) and three longitudinal time points during the course of NAT to evaluate early prediction of response to therapy. Quantitative measures of ADC (evaluating cellularity from DW-MRI) and Ktrans (evaluating vascular perfusion and permeability from DCE-MRI) were calculated for the segmented tumor volume. Imaging was compared to pathology reports at the conclusion of NAT. Results: Reproducibility scans of normal breast fibroglandular tissue yielded an average difference of 8.4% and 7.0% in T1 and ADC measurements, respectively, across sites. Phantom studies revealed accurate measurements of T1 mapping and ADC, with reproducibility measurements showing a difference of 2.8% and 1.6%, respectively. Patients achieving a pathological complete response (pCR) revealed a 13.8% ± 19.0% increase in the mean ADC values of the tumor from t1 (baseline, prior to beginning NAT) and t2 (following one round of NAT) and a 15.4% ± 40.9% decrease in mean Ktrans. Conversely, patients that did not achieve non-pCR had little change in ADC (-0.9% ± 12.6% change between t1 and t2) and a 15.3% ± 42.8% increase in Ktrans. Conclusions: Quantitative MRI has been shown to be accurate and reproducible across community medical centers. Furthermore, the preliminary results discussed above parallel those previously reported in the academic research setting. Citation Format: Anna Sorace, Jack Virostko, Chengyue Wu, Angela M. Jarrett, Stephanie L. Barnes, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Quantitative MRI during neoadjuvant therapy for predicting breast cancer response in the community setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3043.

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.

Immunotherapy is an attractive therapeutic approach because of its theoretical specificity and the potential for long-term disease control. Nevertheless, despite decades of research, there has been no vaccine or other immune-based therapy approved for non-small cell lung cancer (NSCLC). Some of the main hurdles in the design of these therapies include the complex interaction between the host and the tumor, and the lack of reliable biomarkers for patient selection. Because tumors arise from normal tissues, they are poorly immunogenic. Furthermore, they evade the immune system by down-regulating antigens, decreasing expression of major histocompatibility complex class I, and secretion of cytokines that promote immune tolerance and immunosuppression. The ongoing studies on vaccine immune therapies were discussed at the 10th annual targeted therapies in lung cancer symposium and are summarized later. SUMMARY OF PRESENTATIONS GVAX Granulocyte macrophage colony-stimulating factor (GM-CSF) induces antigen expression and attracts antigenpresenting cells (APCs) to the site of vaccination. This feature was used as the rationale to develop an autologous tumor cell vaccine transfected with an adenovirus containing the GM-CSF gene (GVAX). In a multicenter phase I/II study involving patients with early or advanced stage NSCLC, 1 GVAX was administered every 2 weeks for a total of three to six vaccinations. The most common toxicity was a local injection site reaction. Among the 33 patients with advanced stage, three had durable responses lasting from 6 months to more than 22 months, including two patients with bronchioloalveolar carcinoma. A major drawback of GVAX is that being an autologous vaccine, it requires individual patient tissue and takes a long time to prepare. Among the 83 patients undergoing tumor harvest for the study, only 43 patients received the vaccine, and the median time from tissue collection to vaccine administration was 49 days. Belagenpumatucel-L (Lucanix)

BackgroundThe median age of patients with newly diagnosed non-small cell lung cancer (NSCLC) at presentation is 71 years. We conducted an analysis of Surveillance, Epidemiology, and End Results data to assess whether the presentation and outcomes of NSCLC in younger patients (age ≤40 years) are different from that in older patients (age >40 years).MethodsWe obtained the demographic, clinical, and outcomes data for all patients diagnosed with NSCLC from 1988 to 2003 in the Surveillance, Epidemiology, and End Results registry. Patients were grouped by age at diagnosis into younger than or equal to 40 years (younger cohort) or older than 40 years (older cohort).ResultsDuring the period analyzed, we identified 2775 patients with NSCLC in the younger cohort and 236,313 patients in the older cohort. Compared with the older group, the younger group had greater proportion of African Americans (19.2% versus 10.9%; p < 0.0001), Asian or Pacific Islander (10.3% versus 5.9%; p < 0.0001), women (48.7% versus 41.9%; p < 0.0001), and patients with stage IV disease (57.4% versus 43.0%; p < 0.0001). Adenocarcinoma was more common in younger patients than in the older patients (57.5% versus 45.2%; p < 0.0001). Squamous cell carcinoma was less prevalent in the younger cohort than in older cohort (12.5% versus 26.4%; p < 0.0001). Five-year overall survival and cancer specific survival were significantly better for younger patients than for older patients across all stages.ConclusionsThere is a greater representation of African Americans, Asians or Pacific Islanders, women, and adenocarcinoma histology in the younger cohort of patients with NSCLC compared with the older cohort. Despite presenting with stage IV disease more often, the overall and cancer-specific survivals are better in younger cohort than in the older cohort.

Purpose Half of all patients with non-small cell lung cancer (NSCLC) are 70 years or older at the time of diagnosis. Surgery is an option for fit elderly patients with early stage disease, but rates of disease recurrence after surgical resection are not well described. We report the outcomes in elderly patients (70 years or older) with stage I NSCLC after surgical resection. Patients and Methods We conducted a retrospective study of patients diagnosed with stage I NSCLC after surgical resection at Washington University School of Medicine-Alvin J. Siteman Cancer Center from 1990 to 2000. Demographic, pathologic, treatment, and follow-up data were collected. Recurrence rates and overall survival were calculated by the Kaplan-Meier method. Multivariate Cox proportional hazards models were used to detect associations between potential prognostic factors and survival and recurrence. Results Of the 715 patients with stage I NSCLC, 286 were 70 years or older at diagnosis. In this elderly cohort, the median age was 74 years (range, 70–89 years) and 140 of them were women (49%). Lobectomy was performed in 237 patients (83%) whereas 43 patients (15%) had a wedge or segmental resection, and six patients (2%) underwent pneumonectomy. Clinical and pathologic characteristics were not statistically different between the elderly and younger cohorts, with the exception that older patients were more likely to be white (90% versus 80%, p = 0.0003) and less likely to be smokers (88% versus 95%, p = 0.019) compared with the younger cohort. With a median follow-up of 4.6 years, the overall 5-year survival rate was 52% with a 5-year recurrence rate of 24%. In comparison, the patients younger than 70 years had a 5-year survival rate of 67% ( p Conclusions Although overall survival was worse in elderly patients, estimated disease recurrence rates after resection were identical.

Objective: There are no reliable markers to predict recurrence in resected Stage I non-small cell lung cancer (NSCLC). A validated clinical model to estimate the risk of recurrence would help select patients for adjuvant therapy. Methods: We reviewed the medical records of 715 patients who had a potentially curative resection for Stage I NSCLC at our institution from 1990 to 2000. Recurrence rates were estimated by the Kaplan-Meier method. A model to estimate risk of recurrence was developed by combining independent risk factors. Results: With a median follow-up of 4.7 years, the 5-year survival rates for Stages IA and IB were 66% and 55% respectively, and 5-year recurrence rates were 19% and 30%, respectively. Four factors were independently associated with tumor recurrence: tumor size >3 cm (hazard ratio [HR] = 2.4), surgery other than lobectomy (HR = 2.0), nonsquamous histology (HR = 1.4), and high-grade cellular differentiation (HR = 1.4). A scoring system for recurrence was developed by assigning 2 points for each major risk factor (tumor size and surgery) and 1 point for each minor risk factor (histologic subtype and cellular grade). Scores were grouped as low (0–1), intermediate (2–3), and high (>3), yielding 5-year estimates of risk of recurrence of 14%, 27%, and 43%, respectively. Conclusion: This model, based upon readily available clinicopathologic characteristics, can estimate the risk of recurrence in Stage I NSCLC, independent of T classification. This model could be used to select patients for adjuvant therapy if validated in independent data sets.

Purpose Approximately 20 to 40% of patients with surgically resected stage I non-small cell lung cancer (NSCLC) will develop recurrent disease. Positron emission tomography (PET) with 2-[ 18 F] fluoro-2-deoxy-D-glucose (FDG) is used often in staging NSCLC. We conducted this study to determine whether the preoperative maximum tumor standardized uptake value (SUV max ) was associated with recurrence in patients with resected stage I NSCLC. Patients and Methods We identified consecutive patients who underwent curative surgical resection for stage I NSCLC between 1999 and 2003 who had preoperative FDG-PET imaging. Patients were divided into two cohorts based on SUV max above or below the median for the group. Recurrence rates were estimated by the Kaplan-Meier method and overall survival was analyzed as a secondary end point. Results Of 136 patients who met inclusion criteria, 77 (57%) had T1 and 59 (43%) had T2 tumors. The median follow-up time was 46 months and 32 patients had a disease recurrence. The median SUV max was 5.5. The 5-year estimates of recurrence rates for patients with low and high SUV max were 14% and 37%, respectively ( p = 0.002), with 5-year overall survivals of 74% and 53%, respectively ( p = 0.006). In multivariate analyses based on SUV max , T-classification, age, and histology, high SUV max was independently associated with recurrence ( p = 0.002) and mortality ( p = 0.041). Conclusion High SUV max (≥5.5) on preoperative FDG-PET is an independent predictor of relapse and death in resected stage I NSCLC. Prospective trials of adjuvant chemotherapy in patients with stage I NSCLC and high SUV max should be considered.

In persons infected with hepatitis C virus (HCV), the incidence of cirrhosis and hepatocellular carcinoma (HCC) can be estimated by examining over time entire cohorts with known onset of HCV infection. We performed a systematic review of the literature to identify and to analyze studies that examine such cohorts.A search of all articles from 1980 to 2001 was performed. Published studies were included in which chronic HCV infection was defined by elevation of liver enzymes or persistent RNA. We excluded studies in which cohorts were selected from patients with prevalent liver disease or in whom the onset of infection could not be estimated. Two investigators abstracted the data. The incidences of cirrhosis and HCC were analyzed in all studies and in categories based on study design, mode of HCV acquisition, sample size and duration of follow-up, age at the onset of infection, and the quality of estimating the onset of HCV infection.Of the articles, 21 fulfilled the selection criteria. Studies varied in sample size (17-1,680), duration of follow-up (8-45 yr), total person-years (157-34,098), and the mean age at onset of HCV (5-58 yr). The mean time to end stage liver disease (ESLD) was 4-23 yr and to HCC was 9-31 yr. A funnel plot showed a possible publication bias against studies with low incidence of ESLD and HCC. The pooled weighted incidence rates for ESLD and HCC based on infection mode were as follows: community-acquired HCV, 1.9 and 0 per 1,000 person-years; transfusion associated, 4.5 and 0.7; hemophilia patients, 7.9 and 1.0; anti-D IgG, 0.7 and 0 per 1,000 person-years. Poisson regression modeling showed that the incidence of ESLD is increased in studies with a low quality estimate of the onset of HCV infection, in community-acquired and transfusion-associated HCV, and in studies with prospective design.We found large variation in the incidence estimates of cirrhosis and HCC. Short duration of follow-up, small sample size, and possible publication bias may explain some of this variation. Low quality estimates of the onset of HCV infection, a prospective study design, and a transfusion- or hemophilia-related mode of acquisition were independent predictors of high reported incidence of ESLD.

7636 Background: The presence of malignant pleural effusion (MPE) is associated with worse outcomes in patients with non-metastatic non-small cell lung cancer (NSCLC). As a result, the AJCC 7th edi...

22106 Background: There are only limited data available on the clinical presentation and outcomes of patients with extrapulmonary small cell carcinoma (EPSCC). We conducted an analysis of the data ...

18035 Background: Non-small cell lung cancer (NSCLC) is disease of the elderly and is rare in younger adults. NSCLC in younger patients may be biologically different and have a different presentation and natural history. Methods: We identified in the National Cancer Data Base (NCDB), all patients diagnosed with NSCLC from 1998 to 2003. NCDB is a cancer database maintained by the American College of Surgeons. The NCDB data is obtained from the cancer programs participating in the Commission on Cancer NCDB annual call for data. Data on gender, race, stage and histology were obtained from the NCDB. The characteristics of patients with NSCLC from 16–39 years of age were compared to the entire group. Results: In the NCDB, 551248 patients with NSCLC diagnosed from 1998- 2003 were identified. Of these 4093 were in the age group 16–39. We compared the characteristics of NSCLC in younger group to the entire cohort (Table). Younger patients were more likely to be women, 52% vs 42% (p [Table: see text] No significant financial relationships to disclose.

7621 Background: Non-small cell lung cancer (NSCLC) is diagnosed at a median age of 65 and is rare in younger people. The presentation and natural history of the disease in younger patients (age < 40) may not be the same as in typical, or older patients (age > 40). Methods: We identified in the SEER (Suveillance Epidemiology and End Results) registry, all patients diagnosed with NSCLC from 1990 to 2005. Patients were grouped by age at diagnosis, < 40 or = 40. Data on gender, cell type, extent of disease, overall survival, and cause of death were obtained from the SEER database. Cancer-specific survival was defined as time to death from cancer, in which case deaths from other causes are counted as censoring events. Results: In the SEER registry, 3,421 patients with NSCLC diagnosed at an age < 40 were identified, along with 323,566 patients age > 40. Younger patients were more likely to be women, 48% vs 43% (p < 0.0001), and more likely to present with distant disease 53% vs 49% (p < 0.0001). Adenocarcinoma and other, or non-specified types of NSCLC were more common in younger patients, 38% and 44% respectively, vs. 35% and 32% in the older patients. Squamous cell carcinoma was far less common in younger patients, 8.5% vs 23.5% in older patients (p No significant financial relationships to disclose.