Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram

PURPOSE: To evaluate the outcomes and diagnostic performance of ultrasonography after a Breast Imaging Reporting and Data System (Bi-RADS) category 0 mammogram. MATERIAL AND METHODS: This retrospective study reviewed 4,384 consecutive patients who underwent a screening mammography from January 2005 to July 2006; 391 of the 4,384 exams were classified as Bi-RADS category 0. After exclusions, 241 patients received subsequent sonogram. Ultrasonography was considered diagnostic when the Bi-RADS category was changed to 2, 4, or 5, and it was considered indeterminate (Bi-RADS 3) when the results indicated that the patients should return for a mammographic follow-up. The outcomes of these patients were assessed to evaluate the diagnostic performance of ultrasonography. RESULTS: The mean age of the patients was 53.3 years (ranging from 35 to 81). Of the 241 patients, ultrasonography was considered diagnostic in 146 (60.6%) patients and indeterminate in 95 (39.4%) patients. In the diagnostic group, 111 out of 146 patients (70.2%) had a sonogram result of Bi-RADS category 2 after a 2-year follow-up without evidence of malignancy. Furthermore, 35 out of 146 patients (29.8%) had a suspicious sonogram with a result of Bi-RADS category 4. After a tissue sampling procedure, 10 patients were confirmed to have breast cancer, and 25 had benign histopathological features without any evidence of malignancy after a 2-year follow-up. The sensitivity of ultrasonography was 100%, specificity was 89.1%, and overall accuracy was 89.6%. CONCLUSIONS: Based on the degree of resolution and its diagnostic performance, ultrasonography was determined to be an excellent method for the subsequent evaluation of Bi-RADS 0 mammograms

The added value of quantitative multi-voxel MR spectroscopy in breast magnetic resonance imaging

To determine whether quantitative multivoxel MRS improves the accuracy of MRI in the assessment of breast lesions. Twenty-five consecutive patients with 26 breast lesions a parts per thousand yen1 cm assessed as BI-RADS 3 or 4 with mammography underwent quantitative multivoxel MRS and contrast-enhanced MRI. The choline (Cho) concentration was calculated using the unsuppressed water signal as a concentration reference. ROC analysis established the diagnostic accuracy of MRI and MRS in the assessment of breast lesions. Respective Cho concentrations in 26 breast lesions re-classified by MRI as BI-RADS 2 (n = 5), 3 (n = 8), 4 (n = 5) and 5 (n = 8) were 1.16 +/- 0.43 (mean +/- SD), 1.43 +/- 0.47, 2.98 +/- 2.15 and 4.94 +/- 3.10 mM. Two BI-RADS 3 lesions and all BI-RADS 4 and 5 lesions were malignant on histopathology and had Cho concentrations between 1.7 and 11.8 mM (4.03 +/- 2.72 SD), which were significantly higher (P = 0.01) than that in the 11 benign lesions (0.4-1.5 mM; 1.19 +/- 0.33 SD). Furthermore, Cho concentrations in the benign and malignant breast lesions in BI-RADS 3 category differed (P = 0.01). The accuracy of combined multivoxel MRS/breast MRI BI-RADS re-classification (AUC = 1.00) exceeded that of MRI alone (AUC = 0.96 +/- 0.03). These preliminary data indicate that multivoxel MRS improves the accuracy of MRI when using a Cho concentration cut-off a parts per thousand currency sign1.5 mM for benign lesions. Key Points aEuro cent Quantitative multivoxel MR spectroscopy can improve the accuracy of contrast-enhanced breast MRI. aEuro cent Multivoxel-MRS can differentiate breast lesions by using the highest Cho-concentration. aEuro cent Multivoxel-MRS can exclude patients with benign breast lesions from further invasive diagnostic procedures

Diagnostic Challenge of Invasive Lobular Carcinoma of the Breast: What Is the News? Breast Magnetic Resonance Imaging and Emerging Role of Contrast-Enhanced Spectral Mammography

Invasive lobular carcinoma is the second most common histologic form of breast cancer, representing 5% to 15% of all invasive breast cancers. Due to an insidious proliferative pattern, invasive lobular carcinoma remains clinically and radiologically elusive in many cases. Breast magnetic resonance imaging (MR) is considered the most accurate imaging modality in detecting and staging invasive lobular carcinoma and it is strongly recommended in pre-operative planning for all ILC. Contrast-enhanced spectral mammography (CESM) is a new diagnostic method that enables the accurate detection of malignant breast lesions similar to that of breast MR. CESM is also a promising breast imaging method for planning surgeries. In this study, we compare the ability of contrast-enhanced spectral mammography (CESM) with breast MR in the preoperative assessment of the extent of invasive lobular carcinoma. All patients with proven invasive lobular carcinoma treated in our breast cancer center underwent preoperative breast MRI and CESM. Images were reviewed by two dedicated breast radiologists and results were compared to the reference standard histopathology. CESM was similar and in some cases more accurate than breast MR in assessing the extent of disease in invasive lobular cancers. Further evaluation in larger prospective randomized trials is needed to validate our preliminary results

INbreast: Toward a Full-field Digital Mammographic Database

Rationale and Objectives Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and Methods Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. Results The new database—INbreast—has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. Conclusion The strengths of the actually presented database—INbreast—relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging

Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models\u27 predictions. Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a database of 2258 cases mixed from multiple institutions. The generalization of the models was tested on second set of 2177 cases. Clusters were identified in the database using a priori knowledge and unsupervised learning methods (agglomerative hierarchical clustering followed by K-Means, SOM, AutoClass). The performance of the global models over the clusters was examined and local models were trained for clusters. While some local models were superior to some global models, we were unable to build a modular CAD system that was better than the global BP-ANN model. The ensemble systems based on simplistic combination schemes did not result in significant improvements and more complicated combination schemes were found to be unduly optimistic. One of the most striking results of this dissertation was that CAD systems trained on a mixture of lesion types performed much better on masses than on calcifications. Our study of the institutional effects suggests that models built on cases mixed between institutions may overcome some of the weaknesses of models built on cases from a single institution. It was suggestive that each of the unsupervised methods identified a cluster of younger women with well-circumscribed or obscured, oval-shaped masses that accounted for the majority of the BP-ANN’s recommendations for follow up. From the cluster analysis and the CART models, we determined a simple diagnostic rule that performed comparably to the global BP-ANN. Approximately 98% sensitivity could be maintained while providing approximately 26% specificity. This should be compared to the clinical status quo of 100% sensitivity and 0% specificity on this database of indeterminate cases already referred to biopsy

Metabolomic analysis of plasma from breast tumour patients. A pilot study

Background: Patients at risk of breast cancer are submitted to mammography, resulting in a classification of the lesions following the Breast Imaging Reporting and Data System (BI-RADS®). Due to BI-RADS 3 classification problems and the great uncertainty of the possible evolution of this kind of tumours, the integration of mammographic imaging with other techniques and markers of pathology, as metabolic information, may be advisable.Design and Methods: Our study aims to evaluate the possibility to quantify by gas chromatography-mass spectrometry (GC-MS) specific metabolites in the plasma of patients with mammograms classified from BI-RADS 3 to BI-RADS 5, to find similarities or differences in their metabolome. Samples from BI-RADS 3 to 5 patients were compared with samples from a healthy control group. This pilot project aimed at establishing the sensitivity of the metabolomic classification of blood samples of patients undergoing breast radiological analysis and to support a better classification of mammographic cases.Results: Metabolomic analysis revealed a panel of metabolites more abundant in healthy controls, as 3-aminoisobutyric acid, cholesterol, cysteine, stearic, linoleic and palmitic fatty acids. The comparison between samples from BI-RADS 3 and BI-RADS 5 patients, revealed the importance of 4-hydroxyproline, found in higher amount in BI-RADS 3 subjects.Conclusion: Although the low sample number did not allow the attainment of high validated statistical models, some interesting data were obtained, revealing the potential of metabolomics for an improvement in the classification of different mammographic lesions