Belinda Curpen

Long-term results of screening with magnetic resonance imaging in women with BRCA mutations

Background:The addition of breast magnetic resonance imaging (MRI) to screening mammography for women with BRCA mutations significantly increases sensitivity, but there is little data on clinical outcomes. We report screening performance, cancer stage, distant recurrence rate, and breast cancer-specific mortality in our screening study.Methods:From 1997 to 2009, 496 women aged 25 to 65 years with a known BRCA1/2 mutation, of whom 380 had no previous cancer history, were enrolled in a prospective screening trial that included annual MRI and mammography.Results:In 1847 screening rounds, 57 cancers were identified (53 screen-detected, 1 interval, and 3 incidental at prophylactic mastectomy), of which 37 (65%) were invasive. Sensitivity of MRI vs mammography was 86% vs 19% over the entire study period (P<0.0001), but was 74% vs 35% from 1997 to 2002 (P=0.02) and 94% vs 9% from 2003 to 2009 (P<0.0001), respectively. The relative sensitivities of MRI and mammography did not differ by mutation, age, or invasive vs non-invasive disease. Of the incident cancers, 97% were Stage 0 or 1. Of 28 previously unaffected women diagnosed with invasive cancer, 1 BRCA1 mutation carrier died following relapse of a 3 cm, node-positive breast cancer diagnosed on her first screen at age 48 (annual breast cancer mortality rate=0.5%). Three patients died of other causes. None of the 24 survivors has had a distant recurrence at a median follow-up of 8.4 years since diagnosis.Conclusion:Magnetic resonance imaging surveillance of women with BRCA1/2 mutations will detect the majority of breast cancers at a very early stage. The absence of distant recurrences of incident cancers to date is encouraging. However, longer follow-up is needed to confirm the safety of breast surveillance.

Breast Cancers Detected with Imaging Screening in the BRCA Population: Emphasis on MR Imaging with Histopathologic Correlation

The benefit of screening with breast magnetic resonance (MR) imaging for certain patient populations at high risk for breast cancer, most notably patients with a genetic mutation in the BRCA1 or BRCA2 gene, has been established in numerous studies and is now becoming part of routine clinical practice. Despite the lower sensitivity of mammography compared with that of MR imaging, the former remains the standard of care for screening any patient population. In the BRCA1 and BRCA2 populations, the inferior sensitivity and specificity of ultrasonography (US) limit its role as a screening tool, but US remains a vital diagnostic tool because of its ability to provide guidance for biopsy of many suspicious lesions detected with MR imaging. Important features of a screening program with breast MR imaging include the following: optimization of the MR imaging technique, an awareness of the imaging features of invasive and noninvasive breast cancers detected with MR imaging, an understanding of the limitations of the various imaging modalities in both the initial screening and subsequent diagnostic work-up evaluations, and the requirement for MR imaging-guided biopsy.

Breast MRI as an Adjunct to Mammography for Breast Cancer Screening in High-Risk Patients: Retrospective Review

OBJECTIVE In July 2011, the provincial government of Ontario, Canada, approved funding for the addition of annual breast MRI to mammography screening for all women 30-69 years old considered to be at high risk for breast cancer. The purpose of this study was to evaluate the diagnostic performance of screening breast MRI as compared with mammography in a population-based high-risk screening program. MATERIALS AND METHODS A retrospective review identified 650 eligible high-risk women who underwent screening breast MRI and mammography between July 2011 and January 2013 at one institution. Results of 806 screening rounds (comprising both MRI and mammography) were reviewed. RESULTS Malignancy was diagnosed in 13 patients (invasive cancer in nine, ductal carcinoma in situ in three [one with microinvasion], and chest wall metastasis in one). Of the 13 cancers, 12 (92.3%) were detected by MRI and four (30.8%) by mammography. In nine of these patients, the cancer was diagnosed by MRI only, resulting in an incremental cancer detection rate of 10 cancers per 1000 women screened. MRI screening had significantly higher sensitivity than mammography (92.3% vs 30.8%) but lower specificity (85.9% vs 96.8%). MRI also resulted in a higher callback rate for a 6-month follow-up study (BI-RADS category 3 assessment) than mammography (119 [14.8%] vs 13 [1.6%]) and more image-guided biopsies than mammography (95 [11.8%] vs 19 [2.4%]). CONCLUSION MRI is a useful adjunct to mammography for screening in high-risk women, resulting in a significantly higher rate of cancer detection. However, this was found to be at the cost of more imaging and biopsies for lesions that ultimately proved to be benign.

Multiparametric Monitoring of Chemotherapy Treatment Response in Locally Advanced Breast Cancer Using Quantitative Ultrasound and Diffuse Optical Spectroscopy

Purpose This study evaluated pathological response to neoadjuvant chemotherapy using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOSI) biomarkers in locally advanced breast cancer (LABC). Materials and Methods The institutions ethics review board approved this study. Subjects (n = 22) gave written informed consent prior to participating. US and DOSI data were acquired, relative to the start of neoadjuvant chemotherapy, at weeks 0, 1, 4, 8 and preoperatively. QUS parameters including the mid-band fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumor ultrasound data using spectral analysis. In the same patients, DOSI was used to measure parameters relating to tumor hemoglobin and composition. Discriminant analysis and receiver-operating characteristic (ROC) analysis was used to classify clinical and pathological response during treatment and to estimate the area under the curve (AUC). Additionally, multivariate analysis was carried out for pairwise QUS/DOSI parameter combinations using a logistic regression model. Results Individual QUS and DOSI parameters, including the (SI), oxy-hemoglobin (HbO2), and total hemoglobin (HbT) were significant markers for response after one week of treatment (p < 0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI + HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0. Conclusions QUS and DOSI demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Multivariate QUS and DOSI parameters increased the sensitivity and specificity of classifying LABC patients as early as one week after treatment.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

MRI surveillance for women with dense breasts and a previous breast cancer and/or high risk lesion

BACKGROUND The role of surveillance breast MRI for women with mammographically dense breasts, a personal history of breast cancer (BC), atypical hyperplasia (AH), or lobular carcinoma in situ (LCIS) is unclear. We estimated the performance of annual surveillance MRI in women with a combination of these risk factors. METHODS We performed a retrospective review of the clinical, radiological, and pathological parameters of women who received annual concurrent surveillance breast MRI and mammography between 04/2013 and 12/2015 and fulfilled all of the following criteria: 1) age <70; 2) prior diagnosis of AH, LCIS or BC; 3) heterogeneously or extremely dense breast(s); and 4) did not qualify for our provincial breast MRI high risk screening program. RESULTS This study included 198 patients (266 MRI exams). MRI detected 15 cancers: 11 invasive stage I and 4 in-situ. All but 1 were mammographically occult and there were no interval cancers. The cancer detection rate (CDR) and false positive (FP) rate were 6.1% and 21% for round one and 4.7% and 12.5% for round two, respectively. Not being on anti-estrogen therapy and having a 1st degree relative with BC significantly increased the likelihood of tumor detection. CONCLUSIONS The CDR and FP rate of surveillance MRI in this study were comparable to those reported for women with BRCA mutations. The addition of annual MRI to mammography should be considered for surveillance of women with a combination of these risk factors, particularly if they have a family history of BC and are not on anti-estrogen therapy.

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Background:Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pretreatment DOS functional maps for predicting LABC response to NAC.Methods:Locally advanced breast cancer patients (n=37) underwent DOS breast imaging before starting NAC. Breast tissue parametric maps were constructed and texture analyses were performed based on grey-level co-occurrence matrices for feature extraction. Ground truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller–Payne pathological response criteria. The capability of DOS textural features computed on volumetric tumour data before the start of treatment (i.e., ‘pretreatment’) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naive Bayes, and k-nearest neighbour classifiers.Results:Data indicated that textural characteristics of pretreatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2 homogeneity resulted in the highest accuracy among univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5% and 89.0%, respectively, and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-contrast+HbO2-homogeneity, which resulted in a %Sn/%Sp=78.0/81.0% and an accuracy of 79.5%.Conclusions:This study demonstrated that the pretreatment DOS texture features can predict breast cancer response to NAC and potentially guide treatments.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients.

Rapid MRI of the breast in evaluating lesions discovered on screening

In Canada, breast MRI has traditionally been reserved for evaluation of disease extent in patients with known breast malignancy. More recently, MRI has been emerging as an instrument for breast screening. However, its utilization is limited by increased relative cost and increased reader time. In this study, we evaluate a rapid MRI protocol for breast cancer screening within a breast screening population.

Motion Corruption Detection in Breast DCE-MRI

Motion corruption can result in difficulty identifying lesions, and incorrect diagnoses by radiologists in cases of breast cancer screening using DCE-MRI. Although registration techniques can be used to correct for motion artifacts, their use has a computational cost and, in some cases can lead to a reduction in diagnostic quality rather than the desired improvement. In a clinical system it would be beneficial to identify automatically which studies have severe motion corruption and poor diagnostic quality and which studies have acceptable diagnostic quality. This information could then be used to restrict registration to only those cases where motion correction is needed, or it could be used to identify cases where motion correction fails. We have developed an automated method of estimating the degree of mis-registration present in a DCE-MRI study. We experiment using two predictive models; one based on a feature extraction method and a second one using a deep learning approach. These models are trained using estimates of deformation generated from unlabeled clinical data. We validate the predictions on a labeled dataset from radiologists denoting cases suffering from motion artifacts that affected their ability to interpret the image. By calculating a binary threshold on our predictions, we have managed to identify motion corrupted cases on our clinical dataset with an accuracy of 86% based on the area under the ROC curve. This approach is a novel attempt at defining a clinically relevant level of motion corruption.