Linda Pointon

BRCA1 mutation and young age predict fast breast cancer growth in the Dutch, United Kingdom, and Canadian magnetic resonance imaging screening trials

Purpose: Magnetic resonance imaging (MRI) screening enables early detection of breast cancers in women with an inherited predisposition. Interval cancers occurred in women with a BRCA1 mutation, possibly due to fast tumor growth. We investigated the effect of a BRCA1 or BRCA2 mutation and age on the growth rate of breast cancers, as this may influence the optimal screening frequency. Experimental Design: We reviewed the invasive cancers from the United Kingdom, Dutch, and Canadian MRI screening trials for women at hereditary risk, measuring tumor size at diagnosis and on preceding MRI and/or mammography. We could assess tumor volume doubling time (DT) in 100 cancers. Results: Tumor DT was estimated for 43 women with a BRCA1 mutation, 16 women with a BRCA2 mutation, and 41 women at high risk without an identified mutation. Growth rate slowed continuously with increasing age (P = 0.004). Growth was twice as fast in BRCA1 (P = 0.003) or BRCA2 (P = 0.03) patients as in high-risk patients of the same age. The mean DT for women with BRCA1/2 mutations diagnosed at ages ≤40, 41 to 50, and >50 years was 28, 68, and 81 days, respectively, and 83, 121, and 173 days, respectively, in the high-risk group. Pathologic tumor size decreased with increasing age (P = 0.001). Median size was 15 mm for patients ages ≤40 years compared with 9 mm in older patients (P = 0.003); tumors were largest in young women with BRCA1 mutations. Conclusion: Tumors grow quickly in women with BRCA1 mutations and in young women. Age and risk group should be taken into account in screening protocols.

A Pilot Study of Compositional Analysis of the Breast and Estimation of Breast Mammographic Density Using Three-Dimensional T1-Weighted Magnetic Resonance Imaging

Purpose: A method and computer tool to estimate percentage magnetic resonance (MR) imaging (MRI) breast density using three-dimensional T1-weighted MRI is introduced, and compared with mammographic percentage density [X-ray mammography (XRM)]. Materials and Methods: Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by water-containing tissue on three-dimensional T1-weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the Magnetic Resonance Imaging in Breast Screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21-point score and measured by applying a two-dimensional interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and interreader consistency. Results: Percent MRI density correlated strongly (r = 0.78; P < 0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2. Discussion: MRI provides direct three-dimensional measurement of the proportion of water-based tissue in the breast. It correlates well with visual and computerized percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk.(Cancer Epidemiol Biomarkers Prev 2008;17(9):2268–74)

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

OBJECTIVE In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. MATERIAL The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. METHODS The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. RESULTS Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). CONCLUSION It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important.

Protocol for a national multi-centre study of magnetic resonance imaging screening in women at genetic risk of breast cancer

The protocol of the national multicentre study of Magnetic Resonance Imaging (MRI) as a method of screening for breast cancer in women at genetic risk is described. The sensitivity and specificity of contrast-enhanced MRI will be compared with two-view X-ray mammography in a comparative trial. Approximately 500 women below the age of 50 at high genetic risk of breast cancer will be recruited per year for 3 years, with annual MRI and X-ray examination continuing for up to 5 years. A symptomatic cohort will be measured in the initial phase of the study to ensure consistent reporting between centres. The MRI examination will comprise an initial high-sensitivity screening measurement, followed by a high-specificity measurement in equivocal cases. Retrospective analysis will identify the most specific indicators of malignancy. Sensitivity and specificity, together with diagnostic performance, diagnostic impact and therapeutic impact will be assessed with reference to pathology, follow-up and changes in diagnostic certainty and therapeutic decisions. The psychological impact of screening in this high-risk group will be ascertained.

What is the recall rate of breast MRI when used for screening asymptomatic women at high risk

Breast screening acceptability is dependent on sensitivity and recall rate. We aimed to establish the recall rate for MRI and mammography, separately and together, when screening a cohort of women at high genetic risk. Women aged 35-49 years in the MARIBS study form the cohort. We analysed the recall rate, the number of extra tests and their effectiveness. Wilcoxon Rank test was used to estimate the effect of age and logistic regression with robust variance the effect of mammographic density on recall rates. The first 726 screening studies took place in 415 women. Following 86 of these recall occurred, comprising 140 additional investigations. 28 of the cases were resolved without further MRI, and 18 women had more than 2 additional tests. Neither age nor mammographic density was associated with recall. MRI had a recall of rate of 10.19%, and mammography 4.00%. The two techniques largely recalled different cases and 10 cases only (11.62% of those recalled) were abnormal by both tests. The two together had a recall rate of 11.85%. Recall rates varied widely between centres of the study. Breast MRI in asymptomatic high-risk women age 35-49 years largely recalls different women from mammography. The combined figure of approximately 12% may be acceptable for screening and will be useful for planning similar studies.

Eligibility for Magnetic Resonance Imaging Screening in the United Kingdom: Effect of Strict Selection Criteria and Anonymous DNA Testing on Breast Cancer Incidence in the MARIBS Study

Introduction: A UK multicenter study compared the performance of contrast enhanced-magnetic resonance imaging with X-Ray Mammography in women at high-risk of breast cancer commencing in 1997. Selection criteria were used to identify women with at least 0.9% annual risk of breast cancer. Methods: Women at high breast cancer risk, with a strong family history and/or high probability of a BRCA1/BRCA2/TP53 mutation, were recruited from 22 centers. Those not known as gene carriers were asked to give a blood sample, which was tested anonymously for mutations. Women ages 35 to 49 years were offered annual screening for 2 to 7 years. Study eligibility at entry was assessed retrospectively by detailed examination of pedigrees and overall eligibility accounting for computer risk assessment and mutation results. Results: Seventy-eight of 837 (9%) women entered for screening were ineligible using the strict entry criteria. Thirty-nine cancers were detected in 1,869 women-years in study (incidence 21 per 1,000). Including 3,561 further years follow-up, 28 more breast cancers were identified (12 of 1,000). Incidence rates for 759 eligible women were 22 of 1,000 in study and 13 of 1,000 in total follow-up, compared with 9 of 1,000 and 4 of 1,000, respectively, in 78 ineligible women. Breast cancer rates were higher for BRCA2 than BRCA1 after testing anonymized samples in this selected population at 65 of 1,000 in study and 36 of 1,000 in total follow-up for BRCA2 compared with 44 of 1,000 and 27 of 1,000 for BRCA1. Conclusions: Strict enforcement of study criteria would have minimally improved the power of the study, whereas testing for BRCA1/2 in advance would have substantially increased the detection rates. (Cancer Epidemiol Biomarkers Prev 2009;18(7):2123–31)

SOM-Based wavelet filtering for the exploration of medical images

In medical image analysis there are many applications that require the definition of characteristic image features. Especially computationally generated characteristic image features have potential for the exploration of large datasets. In this work, we propose a method for investigating time series of medical images using a combination of the Discrete Wavelet Transform and the Self Organizing Map. Our approach allows relevant image information to be identified in wavelet space. This enables us to develop a filter algorithm suitable to find and extract the characteristic image features and to suppress interfering non-relevant image information.

Content Based Image Retrieval for Dynamic Time Series Data

Content based image retrieval (CBIR) systems in the field of medical image analysis are an active field of research. They allow the user to compare a given case with others in order to assist in the diagnostic process. In this work a CBIR system is described working on datasets which are both time- and space-dependent. Different possible feature sets are investigated, in order to explore how these datasets are optimally represented in the corresponding database.

Multiscale entropy analysis in dynamic contrast-enhanced MRI

In this paper we apply multiscale entropy (MSE) analysis to data obtained from magnetic resonance imaging of the female breast. All cases include lesions that were histologically proven as malignant tumors. Our results indicate that multiscale entropy analysis can play an important role in the detection of tumor tissue when applied to single datasets, but does not allow to calculate universal morphological features. The performance of MSE was examined with respect to traditional features such as difference imaging.