Jessica Gibbs

Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort

A quantitative measure of three-dimensional breast density derived from noncontrast magnetic resonance imaging (MRI) was investigated in 35 women at high-risk for breast cancer. A semiautomatic segmentation tool was used to quantify the total volume of the breast and to separate volumes of fibroglandular and adipose tissue in noncontrast MRI data. The MRI density measure was defined as the ratio of breast fibroglandular volume over total volume of the breast. The overall correlation between MRI and mammographic density measures was R(2)=.67. However the MRI/mammography density correlation was higher in patients with lower breast density (R(2)=.73) than in patients with higher breast density (R(2)=.26). Women with mammographic density higher than 25% exhibited very different magnetic resonance density measures spread over a broad range of values. These results suggest that MRI may provide a volumetric measure more representative of breast composition than mammography, particularly in groups of women with dense breasts. Magnetic resonance imaging density could potentially be quantified and used for a better assessment of breast cancer risk in these populations.

Quantification of breast tissue index from MR data using fuzzy clustering

The study objective was to develop a segmentation technique to quantify breast tissue and total breast volume from magnetic resonance imaging (MRI) data to obtain a breast tissue index (BTI) related to breast density. Our goal is to quantify MR breast density to improve breast cancer risk assessment for certain high-risk populations for whom mammography is of limited usefulness due to high breast density. A semi-automatic 3D segmentation technique was implemented based on a fuzzy c-means technique (FCM) to segment fibroglandular tissue from fat in the breast images. After validation on a phantom, our FCM technique was first used to test the breast tissue measures reproducibility in two consecutive MR examinations of the same patients. The technique was then applied to measure the BTI on 10 high-risk patients. Results of BTI obtained with the semi-automated FCM method were compared with BTI results for the same patients using two other techniques, manual delineation and global threshold. BTI measures correlated well with mammographic densities (Pearson coefficients r = 0.78 using MR manual delineation, and r = 0.75 using MR FCM). The breast tissue index could therefore become a common measure for future studies of using noncontrast MRI data.

Combined diffuse optical spectroscopy and contrast-enhanced magnetic resonance imaging for monitoring breast cancer neoadjuvant chemotherapy: a case study.

Monitoring tumor response to therapy can enable assessment of treatment efficacy, maximizing patient outcome and survival. We employ a noninvasive, handheld laser breast scanner (LBS) based on broadband diffuse optical spectroscopy (DOS) in conjunction with contrast-enhanced magnetic resonance imaging (cMRI) to assess tumor response to presurgical neoadjuvant chemotherapy. DOS and cMRI scans are performed after the first and fourth cycles of a doxorubicin/cyclophosphamide regimen in a patient with invasive ductal carcinoma. DOS measurements are used to quantify bulk tissue optical and physiological parameters, which are mapped to T2- and T1-weighted cMRI images. Initial DOS measurements show high tumor/normal contrast in total hemoglobin concentration (THC, 56+/-7 versus 27+/-4 microM) and water fraction (81.4+/-1% versus 24+/-3%) colocalized with regions of strongly enhancing T2-weighted and cMRI signals. After the fourth cycle of chemotherapy, we observe decreases in peak MRI contrast-enhancement values (37.6%) and apparent lesion volume (21.9 versus 13.7 cm3), which corresponds to physiological changes measured by DOS, including a 20 to 25% reduction in the spatial extent of the tumor and a 38.7% drop in mean total hemoglobin content (THC, 41.6 versus 23.4 microM). These data provide in vivo validation of the accuracy of broadband DOS and the sensitivity of optical methods to changes in tumor physiology.

Breast Stromal Enhancement on MRI Is Associated with Response to Neoadjuvant Chemotherapy

OBJECTIVE Cancerous neovascular changes in histologically normal-appearing breast tissue have been shown to increase risk for local recurrence after breast-conserving therapy. However, the imaging characteristics of this tissue have not been well studied. We hypothesized that signal enhancement ratios from dynamic contrast-enhanced breast MRI could be used to analyze the contrast kinetics of microvasculature in breast stroma beyond the tumor margin and that this information can be developed to improve local treatment options. MATERIALS AND METHODS Signal enhancement ratio analysis of nontumor breast stroma was performed on dynamic contrast-enhanced MRI scans of 42 patients who received neoadjuvant chemotherapy for invasive breast cancer performed before chemotherapy (scan 1) and after one cycle of chemotherapy (scan 2). Stromal signal enhancement ratio values were then correlated to several clinical parameters and to clinical outcome using univariate and multivariate analyses. Median follow-up for the group was 52.1 months. RESULTS On univariate analysis, factors that were significantly associated (p < 0.05) with disease-free survival included the mean stromal signal enhancement ratio at scan 2 (hazard ratio [HR] = 0.11, 95% CI = 0.013-0.88, p = 0.03), pretreatment tumor size (HR = 1.33, 95% CI = 1.07-1.66, p = 0.012), pretreatment tumor volume (HR = 1.04, 95% CI = 1.01-1.07, p = 0.006), and number of involved axillary lymph nodes (HR = 1.18, 95% CI = 1.05-1.32, p = 0.005). These factors were then analyzed in a multivariate Cox proportional hazards model. The only factor that was associated with disease-free survival was mean stromal signal enhancement ratio at scan 2 (HR = 0.11, 95% CI = 0.012-0.95, p < 0.045). CONCLUSION These findings indicate that breast stroma tissue outside the incident tumor can be quantified using signal enhancement ratio analysis on dynamic contrast-enhanced MRI. Stromal signal enhancement ratio is a potential indicator for response to treatment and for overall outcome in patients with breast cancer; however, these results should be validated in a prospective study.

Invasive Breast Cancer: Predicting Disease Recurrence by Using High-Spatial-Resolution Signal Enhancement Ratio Imaging

PURPOSE To retrospectively evaluate high-spatial-resolution signal enhancement ratio (SER) imaging for the prediction of disease recurrence in patients with breast cancer who underwent preoperative magnetic resonance (MR) imaging. MATERIALS AND METHODS This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. From 1995 to 2002, gadolinium-enhanced MR imaging data were acquired with a three time point high-resolution method in women undergoing neoadjuvant therapy for invasive breast cancers. Forty-eight women (mean age, 49.1 years; range, 29.7-72.4 years) were divided into recurrence-free or recurrence groups. Volume measurements were tabulated for SER values between set ranges; cutoff criteria were defined to predict disease recurrence after surgery. Wilcoxon rank sum tests and the multivariate Cox proportional hazards regression model were used for evaluation. RESULTS Breast tumor volume calculated from the number of voxels with SER values above a threshold corresponding to the upper limit of mean redistribution rate constant in benign tumors (0.88 minutes(-1)) and the volume of cancerous breast tissue infiltrating into the parenchyma were important predictors of disease recurrence. Seventy-five percent of patients with recurrence and 100% of deceased patients were identified as being at high risk for recurrence. Thirty percent of patients with recurrence and 67% of deceased patients were identified as having high risk before chemotherapy. No patients in the recurrence-free group were misidentified as likely to have recurrence. All three prechemotherapy parameters (total tumor volume, tumor volumes with high and low SER) and the postchemotherapy tumor volume with high SER were significantly different between the two groups. The multivariate Cox proportional hazards regression showed that, of the three prechemotherapy covariates, only the low SER and high SER tumor volumes (P = .017 and .049, respectively) were significant and independent predictors of tumor recurrence. Tumor volume with high SER was the only significant postchemotherapy covariate predictor (P = .038). CONCLUSION High-spatial-resolution SER imaging may improve prediction for patients at high risk for disease recurrence and death.

Kinetic Assessment of Breast Tumors Using High Spatial Resolution Signal Enhancement Ratio (SER) Imaging

The goal of this study was to investigate the relationship between an empirical contrast kinetic parameter, the signal enhancement ratio (SER), for three‐timepoint, high spatial resolution contrast‐enhanced (CE) MRI, and a commonly analyzed pharmacokinetic parameter, kep, using dynamic high temporal resolution CE‐MRI. Computer simulation was performed to investigate: 1) the relationship between the SER and the contrast agent concentration ratio (CACR) of two postcontrast timepoints (tp1 and tp2); 2) the relationship between the CACR and the redistribution rate constant (kep) based on a two‐compartment pharmacokinetic model; and 3) the sensitivity of the relationship between the SER and kep to native tissue T1 relaxation time, T10, and to errors in an assumed vascular input function. The relationship between SER and kep was verified experimentally using a mouse model of breast cancer. The results showed that a monotonic mathematical relationship between SER and kep could be established if the acquisition parameters and the two postinjection timepoints of SER, tp1, tp2, were appropriately chosen. The in vivo study demonstrated a close correlation between SER and kep on a pixel‐by‐pixel basis (Spearman rank correlation coefficient = 0.87 ± 0.03). The SER is easy to calculate and may have a unique role in breast tissue characterization. Magn Reson Med, 2007.

Characterization of breast lesions using the 3D FIESTA sequence and contrast‐enhanced magnetic resonance imaging

To determine whether combining 3D fast imaging employing steady‐state acquisition (FIESTA) and T1‐weighted contrast‐enhanced (CE) sequences could help characterize lesions in 32 women with benign, in situ, or invasive breast lesions. Since FIESTA provides both T1 and T2 information on the same three‐dimensional (3D) matrix as high‐resolution T1‐weighted dynamic data, we aimed to verify whether invasive lesions could be separated from in situ and/or benign lesions using quantitative FIESTA measures of tissue intensity and homogeneity.

Effect of MR Imaging Contrast Thresholds on Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes: A Subgroup Analysis of the ACRIN 6657/I-SPY 1 TRIAL.

Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (ΔFTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery. Early percent enhancement threshold (PEt) and signal enhancement ratio threshold (SERt) were varied. The predictive performance of resulting FTV predictors was evaluated using the area under the receiver operating characteristic curve. A total number of 116 patients were studied both as a full cohort and in the following groups defined by hormone receptor (HR) and HER2 receptor subtype: 45 HR+/HER2−, 39 HER2+, and 30 triple negatives. High AUCs were found at different ranges of PEt and SERt levels in different subtypes. Findings from this study suggest that the predictive performance to treatment response by MRI varies by contrast thresholds, and that pathologic complete response prediction may be improved through subtype-specific contrast enhancement thresholds. A validation study is underway with a larger patient population.

Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial

Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy.

Study of Breast Tissue Composition Using Magnetic Resonance Imaging and Diffuse Optical Spectroscopy

We combined Magnetic Resonance Imaging (MRI) and Diffuse Optical Spectroscopy (DOS) to study breast tissue composition in 20 healthy volunteers. A combination of MRI and DOS measures was found to be associated with breast density.