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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 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.

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: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) 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 breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p < 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p < 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.

Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-02-09.

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.

There has been limited prospective investigation of correlates of persistent (repeated within and across relationships) physical intimate partner violence (IPV) victimization compared to episodic (not repeated within or across relationships) IPV in young adulthood. The primary aim of the current study was to examine prior victimization, posttraumatic stress disorder (PTSD) symptoms, psychological IPV (emotional abuse and threats), and remaining with the same partner, as unique correlates of persistent physical IPV during a 6-month period. Female undergraduates who completed baseline and follow-up assessments (n = 254) were classified into victimization groups according to their self-report of physical IPV: no physical IPV (65.0%), persistent IPV (at baseline and follow-up [11.0%]), and episodic IPV (at follow-up [9.8%] or baseline only [14.2%]). Multinomial logistic regression analyses showed that the number of prior victimizations, PTSD hyperarousal symptoms, psychological IPV at baseline, and remaining with the same partner at follow-up significantly discriminated the IPV groups. The likelihood of membership in the persistent physical IPV group was associated with all variables. Episodic physical IPV at baseline only was associated with fewer childhood victimizations and being in a new relationship at follow-up. Results highlight assessment and intervention approaches that may assist females experiencing both persistent and episodic physical IPV.

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.

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.

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.

Emerging research on dating aggression in early adolescence suggests that it occurs as early as 6th grade. However, our understanding of dating aggression during this developmental phase remains limited. The primary purpose of the current study was to examine gender differences in various potentially harmful dating behaviors, including physical and psychological dating aggression and victimization as well as jealous, controlling, and monitoring behaviors in a low-income, urban sample of middle school students (N = 3,174). Approximately 40% of youth reported physical dating aggression as a perpetrator or victim. Girls endorsed all forms of physical aggression at a higher rate than boys with one exception: physical restraint. Regarding physical dating victimization, boys reported higher rates of having been slapped; scratched; or kicked, bit, or hit than did girls. There were no statistically significant gender differences in the frequency of injury to self (6% of boys, 2% of girls) or partner (4% of boys and 5% of girls injured a partner). Although the overall injury rate was low, it was not inconsequential. Regarding other potentially harmful behaviors, girls and boys reported jealous accusations, monitoring and spiteful verbal and nonverbal retaliation with the highest frequency. Although, the vast majority of dating aggression was characterized as “just playing around,” many youth demanded apologies and a substantial minority broke up as a result of dating aggression. Of note, boys were significantly more likely to “do nothing” in response to dating aggression compared to girls. Results highlight the complex, variable meaning of aggression, jealousy, and control in early adolescent dating relationships.

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.

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.

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.

Purpose: (1) To assess prevalence of physical dating aggression and victimization among high school students; (2) to assess prevalence of mutual and exclusive aggression; (3) to determine whether aggression differs across ethnic groups and relationship type; and (4) to ascertain the likelihood of injury and breakup in individuals who reported that they were the recipients of physical aggression. Methods: Students (N 2363) from seven multiethnic high schools participated. Because males in high school date females younger than they and the reverse for females, and because males and females may underreport aggression, only within gender comparisons were conducted. Results: More females reported engaging in physical aggression (40%) than reported being victims of aggression (30%). Fewer males reported engaging in physical aggression (24%) than reported being victims of physical aggression (31%). If physical aggression occurred, typically both partners were aggressive. For females, exclusive engagement in physical aggression (perpetration) was reported at higher rates than exclusively being the recipient of physical aggression (victimization) and vice versa for males. Dating aggression was less prevalent among male Asian students than other ethnic groups. Engaged males and females reported the highest rates of physical aggression. Injury was reported by over 25% of males and females who reported being the recipients of physical aggression. Conclusions: Dating aggression intervention programs should address physical aggression of both males and females. Because approximately 30% of the high school males and females reported being the recipients of physical aggression by their partners, primary prevention efforts should occur before high school. © 2008 Society for Adolescent Medicine. All rights reserved.

Evaluation of universal dating violence prevention programs has rapidly expanded in the past two decades. Many programs demonstrate change in attitudes supportive of dating violence, and a few show evidence of behavior change; however, detailed analysis of process and fidelity of program implementation is generally neglected. An important goal of prevention research is to identify successful initiatives that can be replicated and disseminated in the field. The purpose of the current case study is to document the implementation process of a middle school–based dating violence prevention curriculum in economically disadvantaged urban neighborhoods. Particular attention is given to the school context, such as the process of school and teacher recruitment, the program model, and classroom implementation of the dating violence prevention program in four areas: teacher training, student outcomes, program fidelity, and student engagement. Nine health and physical education teachers from six urban middle schools participated. Results describe effective strategies to secure school participation and engagement, and provide evidence regarding methods to train health and physical education teachers in low-income, urban neighborhoods. Furthermore, classroom observations demonstrate that teachers successfully implemented the five-lesson curriculum, which resulted in positive student outcomes to prevent dating violence. This case study represents an important step in deepening our understanding of the mechanisms of program delivery.

Despite a strong body of evidence for an important association between marital discord and depression, issues of causality have been difficult to disentangle. In an attempt to better understand one aspect of the marital discord/depression link, the impact of severe negative marital events on the development of depression was assessed in women with no prior history of major depression (N = 50). The specific goals of the study were to examine rates of depression occurring after the severe negative marital event and to determine the types of events in marriage that wives perceived as severely negative. The rate of Major Depressive Episodes in this group of women experiencing a severe negative marital event (38%) was significantly higher than reported incidence rates. Additionally, participants had significantly elevated rates of depressive symptomatology both shortly after the event and two months later. The most frequently reported negative events were separation/divorce, extramarital affairs, and ...

The purpose of this study was to identify high school students’ actions in response to physical aggression in their dating relationships. The association of these actions with race/ethnicity and gender was also examined. From a sample of high school students (N = 476), a subsample who reported that they had experienced at least one episode of being victimized by physical aggression in a dating relationship (n = 183), served as the sample of interest. On average, students engaged in two help-seeking actions, with females reporting more actions than males. Overall, the most common responses to physical aggression in a dating relationship were aggressive action (e.g., fight back), informal help seeking, threatened or actual breakup, and doing nothing (males) or crying (females). Females were more likely to fight back than were males. Race was largely unrelated to students’ actions. Intervention opportunities and areas for future research are discussed.

Unilateral vocal cord paralysis (UVCP) is relatively common, and previously, thyroidectomy used to be the leading cause. We retrospectively reviewed 98 cases of UVCP. The left vocal cord was involved in 70% of the cases and the right vocal cord in 30%. The cause was neoplastic in 32%, surgical in 30%, idiopathic in 16%, traumatic in 11%, central in 8%, and infectious in 3% of the cases. Only 4 cases were the result of thyroid surgery. Evaluation consisted of a review of the history, a physical examination, and computerized scanning or magnetic resonance imaging, as needed. The functional recovery rate as related to the cause was as follows: surgery 31%, idiopathic 19%, traumatic 18%, and neoplastic 0%. Thirty-five percent of patients required medialization laryngoplasty or Teflon injection. Lung and skull base tumors and their surgical treatment are the most common causes of UVCP.

Given that dating violence is a serious problem for adolescents, research is needed to inform dating violence prevention programs which often target correlates of violence. The current two studies examined the multivariate correlates of dating violence in two high schools. The Riggs and O'Leary (1989) model of courtship aggression was used to select a number of variables that are most amenable to change through prevention and intervention strategies. Multivariate analyses, although showing similar discriminating variables for males and females, yielded slightly different patterns of predictors of male and female dating violence. The results have implications for the development of high school-based dating violence prevention programs and for the further examination of sex differences in dating violence.

We study the energy spectrum of symmetric double quantum dots in narrow-gap carbon nanotubes with one and two electrostatically confined electrons in the presence of spin-orbit and Coulomb interactions. Compared to GaAs quantum dots, the spectrum exhibits a much richer structure because of the spin-orbit interaction that couples the electron's isospin to its real spin through two independent coupling constants. In a single dot, both constants combine to split the spectrum into two Kramers doublets, while the antisymmetric constant solely controls the difference in the tunneling rates of the Kramers doublets between the dots. For the two-electron regime, the detailed structure of the spin-orbit split energy spectrum is investigated as a function of detuning between the quantum dots in a 22-dimensional Hilbert space within the framework of a single-longitudinal-mode model. We find a competing effect of the tunneling and Coulomb interaction. The former favors a left-right symmetric two-particle ground state, while in the regime where the Coulomb interaction dominates over tunneling, a left-right antisymmetric ground state is found. As a result, ground states on both sides of the $(11)$-$(02)$ degeneracy point may possess opposite left-right symmetry, and the electron dynamics when tuning the system from one side of the $(11)$-$(02)$ degeneracy point to the other is controlled by three selection rules (in spin, isospin, and left-right symmetry). We discuss implications for the spin-dephasing and Pauli blockade experiments., Comment: revised version, accepted for publication in Phys. Rev. B

Systematic review of clinical measurement.To find and synthesize evidence on the psychometric properties and usefulness of the neck disability index (NDI).The NDI is the most commonly used outcome measure for neck pain, and a synthesis of knowledge should provide a deeper understanding of its use and limitations.Using a standard search strategy (1966 to September 2008) and 4 databases (Medline, CINAHL, Embase, and PsychInfo), a structured search was conducted and supplemented by web and hand searching. In total, 37 published primary studies, 3 reviews, and 1 in-press paper were analyzed. Pairs of raters conducted data extraction and critical appraisal using structured tools. Ranking of quality and descriptive synthesis were performed.Horizon estimation suggested the potential for 1 missed paper. The agreement between raters on quality assessments was high(kappa = 0.82). Half of the studies reached a quality level greater than 70%. Failures to report clear psychometric objectives/hypotheses or to rationalize the sample size were the most common design flaws. Studies often focused on less clinically applicable properties, like construct validity or group reliability, than transferable data, like known group differences or absolute reliability (standard error of measurement [SEM] or minimum detectable change [MDC]). Most studies suggest that the NDI has acceptable reliability, although intraclass correlation coefficients (ICCs) range from 0.50 to 0.98. Longer test intervals and the definition of stable can influence reliability estimates. A number of high-quality published (Korean, Dutch, Spanish, French, Brazilian Portuguese) and commercially supported translations are available. The NDI is considered a 1-dimensional measure that can be interpreted as an interval scale. Some studies question these assumptions. The MDC is around 5/50 for uncomplicated neck pain and up to 10/50 for cervical radiculopathy. The reported clinically important difference (CID) is inconsistent across different studies ranging from 5/50 to 19/50. The NDI is strongly correlated (0.70) to a number of similar indices and moderately related to both physical and mental aspects of general health.The NDI has sufficient support and usefulness to retain its current status as the most commonly used self-report measure for neck pain. More studies of CID in different clinical populations and the relationship to subjective/work/function categories are required.

Background There is widespread concern that the quality of out-of-hours primary care for patients with complex needs may be at risk now that the new general medical services contract (GMS) has been implemented. Aim To explore changes in the use of out-of-hours services around the time of implementation of the new contract for patients with complex needs, using patients with cancer as an example. Design of study Longitudinal observational study. Setting Out-of-hours primary care provider covering Devon (adult population 900 000), UK. Method Two, 1-year periods corresponding to pre- (April 2003 to March 2004) and post-contract implementation (October 2004 to September 2005) were sampled. Call rates per 1000 of the adult population (age ≥16 years) were calculated for all calls (any cause) and cancer-related calls. Anonymised outcome and process measures data were extracted. Results Although overall call rates per 1000 population had increased by 26% (185 pre-contract to 233 post-contract), the proportion of cancer-related calls remained relatively constant (2.08% versus 1.96%). Around half (56%) of these callers had advanced cancer needs (including palliative care). By post-contract, the time taken to triage had significantly increased ( P

Many couples seeking therapy report the occurrence of severe, negative marital stressors (e.g., infidelity, threats of marital dissolution). In addition, existing research has demonstrated that these marital stressors precipitate Major Depressive Episodes and psychological symptoms. This longitudinal study examines the antecedents and consequences of negative marital stressors to help clinicians and researchers develop interventions that might prevent these stressors and their outcomes. Forty-one women completed a semistructured interview and measures of marital discord and depressive symptoms within one month after experiencing a marital stressor (baseline) and at a 16-month follow up. The results indicate that baseline marital discorded contributes to the occurrence of additional marital stressors during the follow-up period. Although baseline depressive symptoms do not predict additional marital stressors, depressive symptoms along with marital discord predict future depressive symptoms. Finally, baseline marital discord and additional marital stressors contribute to future marital dissolution. Clinical and research implications are discussed.

The purpose of this study was to determine the frequency of complete excision of infiltrating carcinoma at stereotactic 11-gauge directional vacuum-assisted breast biopsy and to evaluate the feasibility of measuring tumor size in stereotactic biopsy specimens in infiltrating carcinomas that were percutaneously excised.We performed retrospective review of 51 infiltrating carcinomas diagnosed using stereotactic 11-gauge directional vacuum-assisted biopsy that underwent subsequent surgery. For lesions yielding no residual infiltrating carcinoma at surgery, the maximal dimension of the tumor was measured in stereotactic biopsy specimens using ocular micrometry.In 10 (20%) (95% confidence intervals, 9.8-33.1%) of 51 infiltrating carcinomas diagnosed at stereotactic biopsy, surgery revealed no residual infiltrating carcinoma. Complete excision of infiltrating carcinoma was more frequent if 14 or more specimens were obtained (32% versus 0%, p.004), if the mammographic lesion was removed (35% versus 7%, p.03), and if the mammographic lesion size measured 0.7 cm or less (50% versus 16%, p = .08). Tumor size in stereotactic biopsy specimens was within 3 mm of mammographic lesion size in six (60%) of 10 lesions, including five (71%) of seven masses and one (33%) of three calcification lesions, but was smaller than the mammographic lesion size in eight (80%) of 10 lesions.Surgery revealed no residual infiltrating carcinoma in 10 (20%) of 51 infiltrating carcinomas diagnosed at stereotactic 11-gauge biopsy. Although tumor size can be assessed in stereotactic biopsy specimens in these lesions, such measurements may underestimate the maximal dimension of the tumor. Further study is needed to evaluate the usefulness of these measurements in guiding treatment decisions.

Objectives: The purpose of this pilot study was to evaluate a five-session dating violence prevention curriculum in terms of its effect on attitudes justifying the use of dating violence. Methods: The curriculum was implemented in all health classes in a Long Island, New York, school. A total of 193 students participated (boys, n = 106; girls, n = 87). A quasi-experimental design was used to evaluate change in attitudes justifying dating violence, with health classes randomly assigned to the treatment or no-treatment conditions. Results: Pre- to postprogram assessments indicated that there were significant decreases in overall attitudes justifying the use of dating violence as a means to resolve conflict among students exposed to the curriculum material, whereas those who were not exposed did not show attitude change from pre- to postprogram evaluation. Conclusions: The curriculum shows promise as an effective tool for changing attitudes condoning dating violence. Future research is needed to determine whether the observed attitude change is also linked to reduction in aggressive behaviors.