Dense Mammograms Research Articles

Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.

This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach. Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed. We found that in the Chinese cohort, the AUC values for predicting false positives were 0.864 (CC) and 0.829 (MLO), while in the Australian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, the AUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Australian cohort, they were 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum response filter outputs were more prone to false positives, while areas with significant intensity changes and coarse textures were more likely to yield false negatives. This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features. This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts.

Screening mammography frequency following dense breast notification among a predominantly Hispanic/Latina screening cohort.

Nationally legislated dense breast notification (DBN) informs women of their breast density (BD) and the impact of BD on breast cancer risk and detection, but consequences for screening participation are unclear. We evaluated the association of DBN in New York State (NYS)with subsequent screening mammography in a largely Hispanic/Latina cohort. Women aged 40-60 were surveyed in their preferred language (33% English, 67% Spanish) during screening mammography from 2016 to 2018. We used clinical BD classification from mammography records from 2013 (NYSDBN enactment) through enrollment (baseline) to create a 6-category variable capturing prior and new DBN receipt (sent only after clinically dense mammograms). We used this variable to compare the number of subsequent mammograms (0, 1, ≥ 2) from 10 to 30months after baseline using ordinal logistic regression. In a sample of 728 women (78% foreign-born, 72% Hispanic, 46% high school education or less), first-time screeners and women who received DBN for the first timeafter prior non-dense mammograms had significantly fewer screening mammograms within 30months of baseline (Odds Ratios range: 0.33 (95% Confidence Interval (CI) 0.12-0.85) to 0.38 (95% CI 0.17-0.82)) compared to women with prior mammography but no DBN. There were no differences in subsequent mammogram frequency between women with multiple DBN and those who never received DBN. Findings were consistent across age, language, health literacy, and educationgroups. Women receiving their first DBN after previous non-dense mammograms have lower mammography participation within 2.5years. DBN has limited influence on screening participation of first-time screeners and those with persistent dense mammograms.

Performance Analysis of Graph theory-based Contrast Limited Adaptive Histogram Equalization for Image Enhancement

Nowadays, image enhancement has become a major area of research because of the development of applications that are based on vision.Several digital image processing systems employ such image enhancement strategies with the help of graph theory. As the visibility level in low contrast image features is very less,several image enhancement strategies have been introduced with spatial transformations to enhance image qualityfor improved visualization. Nowadays, image processing plays an important role in the analysis of a patient’s health status and has become extremely popular in medical areas for a wide range of clinical assessments. Generally, medical images contain several complex areas and thereby,few pre-processing approaches are applied to reduce the challenges that occur during different phases of the CAD system. Furthermore, because of external noise interferences, poor illuminating settings as well as other imaging device limitations, the clinical diagnosis becomes a challenging process and medical images do not provide important information for precise categorization. Medical images are available in a variety of applications such as computed tomography, Magnetic Resonance Imaging (MRI), mammography, chest X-ray (CXR), and many more. Only the pixel intensity variations between different areas as well as object boundary information are essential for categorization and must be enhanced simultaneously. As a result, the rate of classification in medical images and intensity are increased so that every object during the analysis can be easily identified. The main goal of any image enhancement process is to enhance the quality of the image by reducing noise and on other hand by using three different algorithms such as Luminance Modulation (LM), Gradient Modulation (GM), and Dynamic Histogram Equalization (DHE). These three algorithms are designed with the help of graph theory for effective preservation of edges, losses, and efficient smoothing and to preserve the basic information without any modifications. Image restoration is also referred to as image enhancement and it is concerned with the precise assessment of real images. Generally, the degradation process is not included in many of the image-enhancement approaches that are already existing. Furthermore, with the application of enhancement techniques, the degradation process for medical images results in some significant performance loss. Several techniques have been proposed and the technique which is examined in this research is image enhancement that is based on histogram which mainly concentrates on equalizing the histogram of values. Histogram Equalization (HE) possesses a few basic properties such as altering spatial patterns as well as intensity which in turn results in significant challenges in medical imaging. As a result,Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed in this work as a feasible approach for medical image analysis to address the problem. The suggested research work demonstrates that the intensity limiting image enhancement with histogram equalization detects the irregularities in dense mammograms with enhanced quality.

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice

ObjectiveLegislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. MethodsThis institutional review board–approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. ResultsWe identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). DiscussionA DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.

Evaluation of cfDNA as an early detection assay for dense tissue breast cancer

A cell-free DNA (cfDNA) assay would be a promising approach to early cancer diagnosis, especially for patients with dense tissues. Consistent cfDNA signatures have been observed for many carcinogens. Recently, investigations of cfDNA as a reliable early detection bioassay have presented a powerful opportunity for detecting dense tissue screening complications early. We performed a prospective study to evaluate the potential of characterizing cfDNA as a central element in the early detection of dense tissue breast cancer (BC). Plasma samples were collected from 32 consenting subjects with dense tissue and positive mammograms, 20 with positive biopsies and 12 with negative biopsies. After screening and before biopsy, cfDNA was extracted, and whole-genome next-generation sequencing (NGS) was performed on all samples. Copy number alteration (CNA) and single nucleotide polymorphism (SNP)/insertion/deletion (Indel) analyses were performed to characterize cfDNA. In the positive-positive subjects (cases), a total of 5 CNAs overlapped with 5 previously reported BC-related oncogenes (KSR2, MAP2K4, MSI2, CANT1 and MSI2). In addition, 1 SNP was detected in KMT2C, a BC oncogene, and 9 others were detected in or near 10 genes (SERAC1, DAGLB, MACF1, NVL, FBXW4, FANK1, KCTD4, CAVIN1; ATP6V0A1 and ZBTB20-AS1) previously associated with non-BC cancers. For the positive–negative subjects (screening), 3 CNAs were detected in BC genes (ACVR2A, CUL3 and PIK3R1), and 5 SNPs were identified in 6 non-BC cancer genes (SNIP1, TBC1D10B, PANK1, PRKCA and RUNX2; SUPT3H). This study presents evidence of the potential of using cfDNA somatic variants as dense tissue BC biomarkers from a noninvasive liquid bioassay for early cancer detection.

Strengths and challenges of the artificial intelligence in the assessment of dense breasts.

High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.

CLAHE Parameters Effects on the Quantitative and Visual Assessment of Dense Breast Mammograms

In Brazil, breast cancer is the disease with the highest mortality rate in women, mainly due to late diagnosis. The digital image processing has been used in order to provide both an improvement in the quality of the mammographic images as well as the evaluation of these images by radiologist. The goal of this work is to apply and evaluate (by an experienced radiologist) an adaptive histogram equalization algorithm and the variation of its parameters in mammographic digital images of dense breasts. We applied the Contrast-limited Adaptive Histogram Equalization (CLAHE) with different parameters (window size) in 98 images for the purpose of visual analysis and comparison with the respective original image. The peak signal-to-noise ratio (PSNR) ratio, the variance and Structural Similarity Index (SSIM) were calculated, as well as a visual evaluation of an experienced and specialized radiologist. The quantitative results showed the proximity between the averages of PSNR values for the set of windows tested. According to the radiologist assessment, the window size 15x15 provided a better contrast between fibroglandular tissue and adjacent structures. This study contributed to the contrast enhancement in dense mammograms, that is, belonging to those patients who present a higher risk of developing breast cancer. With the application of a simple mathematically and fast computational processing technique, it was possible to obtain better resultant images compared to the original ones, able to aid radiologists in a better diagnostic accuracy and in the early diagnosis of breast cancer.

Investigation on ROI size and location to classify mammograms

Breast cancer is the major cause of death among women and early detection can lead to a longer survival. Computer Aided Diagnosis (CAD) system helps radiologists in the accurate detection of breast cancer. In medical images a Region of Interest (ROI) is a portion of image which carries the important information related to the diagnosis and it forms the basis for applying shape and texture techniques for cancer detection. Several ROI sizes and locations have been proposed for computer aided diagnosis systems. In the present work various ROI sizes have been used to determine the appropriate ROI size to classify fatty and dense mammograms. Two types of mammograms i.e. fatty and dense are used from the MIAS database. Various texture features have been determined from each ROI size for the analysis of texture characteristics. Fisher discriminant ratio is used to select the most relevant features for classification. Finally linear SVM is used for the purpose of classification. Highest classification accuracy of 96.1% was achieved for ROI size 200×200 pixels.

Investigation on ROI size and location to classify mammograms

Breast cancer is the major cause of death among women and early detection can lead to a longer survival. Computer Aided Diagnosis (CAD) system helps radiologists in the accurate detection of breast cancer. In medical images a Region of Interest (ROI) is a portion of image which carries the important information related to the diagnosis and it forms the basis for applying shape and texture techniques for cancer detection. Several ROI sizes and locations have been proposed for computer aided diagnosis systems. In the present work various ROI sizes have been used to determine the appropriate ROI size to classify fatty and dense mammograms. Two types of mammograms i.e. fatty and dense are used from the MIAS database. Various texture features have been determined from each ROI size for the analysis of texture characteristics. Fisher discriminant ratio is used to select the most relevant features for classification. Finally linear SVM is used for the purpose of classification. Highest classification accuracy of 96.1% was achieved for ROI size 200×200 pixels.

False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer.

Early automatic breast cancer detection from mammograms is based on the extraction of lesions, known as microcalcifications (MCs). This paper proposes a new and simple system for microcalcification detection to assist in early breast cancer detection. This work uses the two most recognized public mammogram databases, MIAS and DDSM. We are introducing a MC detection method based on (1) Beucher gradient for detection of regions of interest (ROIs), (2) an annulus model for extraction of few and effective features from candidates to MCs, and (3) one classification stage with two different classifiers, k Nearest Neighbor (KNN) and Support Vector Machine (SVM). For dense mammograms in the MIAS database, the performance metrics achieved are sensitivity of 0.9835, false alarm rate of 0.0083, accuracy of 0.9835, and area under the ROC curve of 0.9980 with a KNN classifier. The proposed MC detection method, based on a KNN classifier, achieves, a sensitivity, false positive rate, accuracy and area under the ROC curve of 0.9813, 0.0224, 0.9795 and 0.9974 for the MIAS database; and 0.9035, 0.0439, 0.9298 and 0.9759 for the DDSM database. By slightly reducing the true positive rate the method achieves three instances with false positive rate of 0: 2 on fatty mammograms with KNN and SVM, and one on dense with SVM. The proposed method gives better results than those from state of the art literature, when the mammograms are classified in fatty, fatty-glandular, and dense.

Abstract P4-01-11: Diagnostic performance of supplemental screening with molecular breast imaging in women with dense breast tissue

Abstract Purpose: The aim of this study was to retrospectively assess the diagnostic performance of molecular breast imaging (MBI) as a supplementary screening tool for women with dense breast tissue. We had previously reported excellent cancer detection rates and positive predictive values, whereas in this report we present 1-year follow up data and present sensitivity, specificity and negative predictive values. Methods: Molecular Breast Imaging (MBI) was implemented in routine clinical practice at a large, community-based breast imaging center. Women 25 to 90 years of age with dense breasts and negative mammogram results who underwent subsequent screening with 300 MBq (8 mCi) 99mTc-sestamibi MBI were retrospectively analyzed. Outcome measures included sensitivity, specificity, and positive and negative predictive values. Results: In 1,376 women with a complete reference standard, 17 (1.2%) were diagnosed with cancer. MBI detected cancer in 15 subjects such that 2 interval cancers occurred and were considered false negatives. Of those 17 cancers, 1 (5.9%) were ductal carcinoma in situ only, 13 were invasive tumors (76.5%); 1 (5.9%) patient was diagnosed with bilateral disease. Overall sensitivity of MBI in this sample was 88.2% (95% CI 63.6 – 98.5), while specificity was also high at 92.4% (95% CI 90.9 – 93.8). Positive predictive value for recall (PPV1) was 12.7% (95% CI 7.3 – 20.1), while negative predictive value for recall (NPV1) was calculated as 99.8% (95% CI 99.4 – 100.0). Conclusions: When incorporated into a community-based clinical practice environment, MBI demonstrated high sensitivity, specificity and negative predictive value. These results demonstrate the utility of MBI as a supplementary screening tool to mammography for women with dense breasts. Citation Format: Redfern RE, Shermis RB, Chen JT, Kudrolli H. Diagnostic performance of supplemental screening with molecular breast imaging in women with dense breast tissue [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-01-11.

Impact of Breast Density Notification Legislation on Radiologists' Practices of Reporting Breast Density: A Multi-State Study.

Purpose To evaluate the impact of breast density notification legislation on breast density reporting by radiologists nationally. Materials and Methods The institutional review board exempted this HIPAA-compliant retrospective study from the requirement for informed consent. State-level data over a 5-year period on breast density categorization and breast cancer detection rate were collected from the National Mammography Database (NMD). Z tests were used to calculate differences in proportions. Results Facilities in 13 of 17 states that had breast density notification legislation as of 2014 submitted data to the NMD before and after law enactment. A total of 1 333 541 mammographic studies (hereafter called "mammograms") over a 30-month period, beginning 20 months before and continuing 10 months after law enactment, were included in the analysis. There was a small but statistically significant decrease in the percentage of mammograms reported as showing dense breast tissue (hereafter called "dense mammograms") in the month before law enactment compared with the month after (43.0% [22 338 of 52 000] vs 40.0% [18 604 of 46 464], P < .001). There was no statistically significant difference in the percentage of mammograms reported as dense in the month before law enactment compared with the 10th month after (43.0% [22 338 of 52 000] vs 42.8% [15 835 of 36 991], P = .65). There were no significant differences in the breast cancer detection rate between the month before and the month after law enactment (3.9 vs 3.8 cancers per 1000 mammograms, P = .79) or between the month before law enactment and the 10th month after (3.9 vs 4.2 cancers per 1000 mammograms, P = .55). In 21 analyzed states without breast density notification legislation, the percentage of mammograms reported as dense did not decrease below 42.8% (43 363 of 101 394) from 2010 to 2014, in contrast to 13 analyzed states with breast density notification legislation, which reached a nadir of 39.3% (20 965 of 53 360) (P < .001). Conclusion The percentage of mammograms reported as dense slightly decreased immediately after enactment of breast density notification legislation but then returned to prelegislation percentages within 10 months. (©) RSNA, 2016.

Automated Classification of Fatty and Dense Mammograms

Automated Classification of Fatty and Dense Mammograms

A bilateral analysis scheme for false positive reduction in mammogram mass detection

In this paper, a bilateral image analysis scheme is developed for the purpose of reducing false positives (FPs) in the detection of masses in dense mammograms. It consists of two steps: a region matching step for determining the correspondence between a pair of mammograms, and a bilateral similarity analysis step for discarding FPs in the detection. For the first step, a matching cost is defined to quantify the credibility of the corresponding region in a pair of bilateral mammograms. For the second step, a similarity measurement is introduced to discriminate between mass and normal for a pair of bilateral regions based on both global and local image appearances. The proposed scheme is tested on a set of 332 mammograms. The results show that the proposed scheme could obtain better performance when compared with several existing bilateral analysis schemes. With detection sensitivity at 85%, the proposed bilateral scheme could reduce the FP rate of a unilateral scheme from 3.64 to 2.39 per image, a 34% reduction.

CFS-SMO based classification of breast density using multiple texture models.

It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density.

Supplementary Breast Ultrasound Screening in Asian Women with Negative But Dense Mammograms—A Pilot Study

Dense breasts are common in Asian women and they limit the sensitivity of mammography. This study evaluates the performance of supplementary breast ultrasound screening in Asian women with dense mammograms. The study was approved by the hospital's Institutional Review Board. A prospective clinical trial was performed between September 2002 and November 2004. Asymptomatic Asian women with negative and dense mammograms were offered supplementary ultrasound screening for breast cancer. Ultrasound assessment was categorised as U1 to U4. U1 and U2 cases were recommended routine interval screening mammography. U3 cases were recommended follow-up ultrasound in 6 months and routine interval screening mammography and U4 cases were recommended biopsy. One hundred and forty-one women with mean age of 45.1 years were enrolled into the study. Mean scan time was 13.0 minutes (± 5.6 minutes) for bilateral vs 11.0 minutes (± 1.4 minutes) for unilateral scans. There were 10 patients and 14 patients in the in the U3 and U4 categories, respectively. Two U4 category patients were diagnosed with malignancy-a-6 mm ductal carcinoma-in-situ and a 13- mm invasive ductal carcinoma. The breast cancer detection rate was 1.4%. Sensitivity and specifi city were 100% (2/2) and 88.5% (92/104) respectively. The positive predictive value was 14.3% (2/14) and the negative predictive value was 100% (92/92). This pilot study reveals the usefulness of supplementary ultrasound screening in detecting early stage mammographically and clinically occult breast cancers in Asian women with dense breasts. A larger long-term study is, however, needed to assess its feasibility and impact on breast cancer prognosis.

Yield of Selective Magnetic Resonance Imaging in Preoperative Workup of Newly Diagnosed Breast Cancer Patients Planned for Breast Conserving Surgery

The role of routine preoperative magnetic resonance imaging (MRI) in newly diagnosed breast cancer patients planned for breast conserving surgery is presently being debated. In our medical center we practice selective use of preoperative MRI; we sought to examine the yield of MRI in this highly selected group of patients. A retrospective study of all newly diagnosed breast cancer patients presenting between January 2007 and July 2010 to the Tel Aviv Sourasky Medical Center (Tel Aviv, Israel) was completed. Patients planned for breast conserving surgery who underwent preoperative MRI were included in this study. Patients and tumor characteristics, indication for MRI, findings on MRI, consequent workup, and impact on surgical treatment were recorded. Association between preoperative characteristics and yield of MRI was examined. During the study period, 105 patients that were candidates for breast conserving surgery underwent preoperative evaluation with MRI. Use of breast MRI increased over time. Rates of mastectomy were stable throughout the study years. Dense mammogram was the most frequent (51, 68%) indication for MRI. Additional suspicious findings were found in 41 (39%) patients, prompting further workup including 36 biopsies in 25 patients, of which 22 (61%) were with cancer. These additional findings prompted a change in the surgical plan in a third of the patients. In most patients (92; 88%) clear margins were achieved. Limiting the use of MRI in the preoperative workup of breast cancer patients to a selected group of patients can increase the yield of MRI.

Commentary on: Incidental Breast Cancers Identified in a One-Stop Symptomatic Breast Clinic

Commentary on: Incidental Breast Cancers Identified in a One-Stop Symptomatic Breast Clinic

Contrast-enhanced breast magnetic resonance imaging: the surgical perspective

Background Contrast-enhanced breast magnetic resonance imaging (MRI) has shown excellent sensitivity (93%) for breast malignancies. The clinical role and value of MRI for the breast surgeon remains unresolved and controversial. Methods A retrospective review of clinical and imaging records was undertaken for 79 surgical patients evaluated by MRI as part of their initial assessment. Results Of 79 patients, 71 (90%) had dense mammograms, 64 (81%) had known primary breast cancer (BrCA), 42 (53%) had a family history of BrCA, and 8 (10%) had known breast atypia. MRI identified a larger than clinically suspected BrCA size in 10 (16%) patients and greater BrCA extent in 26 (41%) patients. Of 15 non-BrCA patients, 10 (67%) had a biopsy after the MRI with identification of BrCA in 6 (40%) patients. Overall, there were 20 MRI-initiated core biopsies, of which 14 (70%) identified BrCA. Of 70 BrCA patients, 13 (19%) had neoadjuvant chemotherapy, 35 (50%) had breast-conserving surgery, 36 (51%) had total mastectomy, and 14 (22%) had bilateral total mastectomy. MRI had good pathology correlation in 56 of 62 (90%) patients. In patients with known BrCA (by core biopsy), none of the 29 breast-conserving surgery resections undertaken after MRI had a positive margin or required re-excision. Conclusions MRI is very accurate for BrCA evaluation, both for lesion size and extent. With good collaboration between the radiologist and surgeon, MRI is a powerful 3-dimensional and communication tool for the breast surgeon and the patient.

Contrast enhancement in dense breast images using the modulation transfer function.

This work proposes a method aimed at enhancing the contrast in dense breast images in mammography. It includes a new preprocessing technique, which uses information on the modulation transfer function (MTF) of the mammographic system in the whole radiation field. The method is applied to improve the efficiency of a computer-aided diagnosis (CAD) scheme. Seventy-five regions of interest (ROIs) from dense mammograms were acquired in two pieces of equipment (a CGR Senographe 500t and a Philips Mammodiagnost) and were digitized in a Lumiscan 50 laser scanner. A computational procedure determines the effective focal spot size in each region of interest from the measured focal spot in the center for a given mammographic equipment. Using computational simulation the MTF is then calculated for each field region. A procedure that enlarges the high-frequency portion of this function is applied and a convolution between the resulting new function and the original image is performed. Both original and enhanced images were submitted to a processing procedure for detecting clustered microcalcifications in order to compare the performance for dense breast images. ROIs were divided into four groups, two for each piece of equipment-one with clustered microcalcifications and another without microcalcifications. Our results show that in about 10% of the enhanced images more signals were detected when compared to the results for the original dense breast images. This is important because the usual processing techniques used in CAD schemes present poor results when applied to dense breast images. Since the MTF method is a well-recognized tool in the evaluation of radiographic systems, this new technique could be used to associate quality assurance procedures with the processing schemes employed in CAD for mammography.