scholarly journals Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Lambert T. Leong ◽  
Serghei Malkov ◽  
Karen Drukker ◽  
Bethany L. Niell ◽  
Peter Sadowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Breast Imaging ◽  
Characteristic Curve ◽  
Breast Cancer Mortality ◽  
Digital Breast Tomosynthesis ◽  
Dual Energy ◽  
Imaging Biomarkers ◽  
Breast Cancers ◽  
Full Field ◽  
Compositional Information

Abstract Background While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection. Methods Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology. Results The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74–0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60–0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Conclusion Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

Dual-energy three compartment breast imaging (3CB) for novel compositional biomarkers to improve detection of malignant lesions

2021 ◽  
Author(s):  
Lambert Leong ◽  
Serghei Malkov ◽  
Karen Drukker ◽  
Bethany Niell ◽  
Peter Sadowski ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Operating Characteristic ◽  
Imaging Technique ◽  
Characteristic Curve ◽  
Dual Energy ◽  
Imaging Biomarkers ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Malignant Lesions ◽  
Operating Characteristic Curve

Abstract We explore a compositional breast imaging technique known as three compartment breast (3CB) to improve malignancy detection. The addition of 3CB compositional information to computer-aided detection (CAD) software improved malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieved an AUC of 0.69 (CI 0.60-0.78). We also identified that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

scholarly journals Utility of Digital Breast Tomosynthesis with Two-Dimensional Synthesized Mammography Images: A Pictorial Essay

2021 ◽  
Author(s):  
Pradipta C. Hande ◽  
Sabita S. Desai ◽  
Sarabjeet K. Arneja ◽  
Sreedevi Sathian
Keyword(s):  
High Resolution ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Two Dimensional ◽  
Breast Cancers ◽  
Data Set ◽  
Breast Tomosynthesis ◽  
Full Field ◽  
Full Field Digital Mammography ◽  
Pictorial Essay

Abstract Background Mammography has been established as the key modality in the detection and diagnosis of breast cancers. Digital breast tomosynthesis (DBT) has emerged as a mammographic technique which allows improved visualization of abnormalities by reducing the effect of overlapping breast tissue. Purpose This article is a pictorial essay which highlights the advantages of DBT with two-dimensional (2D) synthesized mammography (2DSM) images, its clinical applications, and its role in breast imaging. Materials and Methods Selenia Dimensions HD mammography machine performs DBT which acquires a series of low-dose digital mammographic images of the compressed breast followed by full-field digital mammography. Software using specialized algorithms helps to create a 2DSM image reconstructed from the DBT data set. The images are interpreted on a dedicated work station on high-resolution monitors by the radiologist. American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) lexicon is used for reporting. High-resolution breast ultrasound which includes evaluation of the axilla is done for all cases. Conclusion DBT improves detection and better characterization of lesions which thereby increases confidence of interpretation of mammograms and assigning BI-RADS categories for further management.

Automatic detection of various patterns of calcification distribution on mammograms: a graph convolution network approach (Preprint)

2021 ◽  
Author(s):  
Melissa Min-Szu Yao ◽  
Hao Du ◽  
Mikael Hartman ◽  
Wing P. Chan ◽  
Mengling Feng
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Automatic Detection ◽  
Ground Truth ◽  
Architectural Distortion ◽  
Breast Cancers ◽  
Convolutional Network ◽  
Mammographic Images

UNSTRUCTURED Purpose: To develop a novel artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 200 patients classified as Category 4 or 5 according to the American College of Radiology Breast Imaging Reporting and Database System, which showed calcifications according to the mammographic reports and diagnosed breast cancers. The calcification distributions were classified as either diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer as a single or combined characterization such as a mass, asymmetry, or architectural distortion with or without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph convolutional network-based model was developed. 401 mammographic images from 200 cases of breast cancer were divided based on calcification distribution pattern: diffuse (n = 24), regional (n = 111), group (n = 201), linear (n = 8) or segmental (n = 57). The classification performances were measured using metrics including precision, recall, F1 score, accuracy and multi-class area under receiver operating characteristic curve. The proposed achieved precision of 0.483 ± 0.015, sensitivity of 0.606 (0.030), specificity of 0.862 ± 0.018, F1 score of 0.527 ± 0.035, accuracy of 60.642% ± 3.040% and area under the curve of 0.754 ± 0.019, finding method to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. Conclusion: The proposed deep neural network framework is an AI solution to automatically detect and classify calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.

Advanced Imaging Techniques for the Detection of Breast Cancer

2012 ◽  
pp. 65-69 ◽  
Author(s):  
Maxine Jochelson
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Imaging ◽  
Early Stage ◽  
Imaging Techniques ◽  
Breast Cancer Mortality ◽  
Digital Breast Tomosynthesis ◽  
Ct Scanning ◽  
High Risk Women ◽  
Contrast Enhanced

Overview: Mammography is the only breast imaging examination that has been shown to reduce breast cancer mortality. Population-based sensitivity is 75% to 80%, but sensitivity in high-risk women with dense breasts is only in the range of 50%. Breast ultrasound and contrast-enhanced breast magnetic resonance imaging (MRI) have become additional standard modalities used in the diagnosis of breast cancer. In high-risk women, ultrasound is known to detect approximately four additional cancers per 1,000 women. MRI is exquisitely sensitive for the detection of breast cancer. In high-risk women, it finds an additional four to five cancers per 100 women. However, both ultrasound and MRI are also known to lead to a large number of additional benign biopsies and short-term follow-up examinations. Many new breast imaging tools have improved and are being developed to improve on our current ability to diagnose early-stage breast cancer. These can be divided into two groups. The first group is those that are advances in current techniques, which include digital breast tomosynthesis and contrast-enhanced mammography and ultrasound with elastography or microbubbles. The other group includes new breast imaging platforms such as breast computed tomography (CT) scanning and radionuclide breast imaging. These are exciting advances. However, in this era of cost and radiation containment, it is imperative to look at all of them objectively to see which will provide clinically relevant additional information.

Comparison of full-field digital mammography and digital breast tomosynthesis in ultrasonography-detected breast cancers

The Breast ◽  
2015 ◽  
Vol 24 (5) ◽  
pp. 649-655 ◽  
Author(s):  
Kyung Jin Nam ◽  
Boo-Kyung Han ◽  
Eun Sook Ko ◽  
Ji Soo Choi ◽  
Eun Young Ko ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Breast Cancers ◽  
Breast Tomosynthesis ◽  
Full Field ◽  
Full Field Digital Mammography

scholarly journals Diagnostic accuracy of digital breast tomosynthesis in combination with 2D mammography for the characterisation of mammographic abnormalities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vithya Visalatchi Sanmugasiva ◽  
Marlina Tanty Ramli Hamid ◽  
Farhana Fadzli ◽  
Faizatul Izza Rozalli ◽  
Chai Hong Yeong ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Asian Population ◽  
Architectural Distortion ◽  
Breast Lesions ◽  
Cross Sectional ◽  
Breast Tomosynthesis ◽  
Full Field

AbstractThis study aims to assess the diagnostic accuracy of digital breast tomosynthesis in combination with full field digital mammography (DBT + FFDM) in the charaterisation of Breast Imaging-reporting and Data System (BI-RADS) category 3, 4 and 5 lesions. Retrospective cross-sectional study of 390 patients with BI-RADS 3, 4 and 5 mammography with available histopathology examination results were recruited from in a single center of a multi-ethnic Asian population. 2 readers independently reported the FFDM and DBT images and classified lesions detected (mass, calcifications, asymmetric density and architectural distortion) based on American College of Radiology-BI-RADS lexicon. Of the 390 patients recruited, 182 malignancies were reported. Positive predictive value (PPV) of cancer was 46.7%. The PPV in BI-RADS 4a, 4b, 4c and 5 were 6.0%, 38.3%, 68.9%, and 93.1%, respectively. Among all the cancers, 76% presented as masses, 4% as calcifications and 20% as asymmetry. An additional of 4% of cancers were detected on ultrasound. The sensitivity, specificity, PPV and NPV of mass lesions detected on DBT + FFDM were 93.8%, 85.1%, 88.8% and 91.5%, respectively. The PPV for calcification is 61.6% and asymmetry is 60.7%. 81.6% of cancer detected were invasive and 13.3% were in-situ type. Our study showed that DBT is proven to be an effective tool in the diagnosis and characterization of breast lesions and supports the current body of literature that states that integrating DBT to FFDM allows good characterization of breast lesions and accurate diagnosis of cancer.

A comparison between digital breast tomosynthesis and full-field digital mammography for the detection of breast cancers

Breast Cancer ◽  
2015 ◽  
Vol 23 (6) ◽  
pp. 886-892 ◽  
Author(s):  
Woo Jung Choi ◽  
Hak Hee Kim ◽  
Sun Young Lee ◽  
Eun Young Chae ◽  
Hee Jung Shin ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Breast Cancers ◽  
Breast Tomosynthesis ◽  
Full Field ◽  
Full Field Digital Mammography

scholarly journals Current Status of Contrast Enhanced Mammography: A Comprehensive Review

2021 ◽  
pp. 084653712110290
Author(s):  
Anat Kornecki
Keyword(s):  
Breast Imaging ◽  
Dual Energy ◽  
Current Status ◽  
Resonance Imaging ◽  
Comprehensive Review ◽  
Full Field ◽  
Dce Mri ◽  
Full Field Digital Mammography ◽  
Contrast Enhanced ◽  
Diagnostic Setting

Objectives: The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography. Background: Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality. Conclusion: Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.

scholarly journals Lobular Breast Cancer Conspicuity on Digital Breast Tomosynthesis Compared to Synthesized 2D Mammography: A Multireader Study

2021 ◽  
Vol 7 (9) ◽  
pp. 185
Author(s):  
Giovanna Romanucci ◽  
Lisa Zantedeschi ◽  
Anna Ventriglia ◽  
Sara Mercogliano ◽  
Maria Vittoria Bisighin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Matched Pairs ◽  
Breast Cancers ◽  
Test Results ◽  
Lobular Breast Cancer ◽  
Point Scale ◽  
Breast Tomosynthesis ◽  
Invasive Lobular Breast Cancer

Objectives: To compare the conspicuity of lobular breast cancers at digital breast tomosynthesis (DBT) versus synthesized 2D mammography (synt2D). Materials and methods: Seventy-six women (mean age 61.2 years, range 50–74 years) submitted to biopsy in our institution, from 2019 to 2021, with proven invasive lobular breast cancer (ILC) were enrolled in this retrospective study. The participants underwent DBT and synt2D. Five breast radiologists, with different years of experience in breast imaging, independently assigned a conspicuity score (ordinal 6-point scale) to DBT and synt2D. Lesion conspicuity was compared, for each reader, between the synt2D overall conspicuity interpretation and DBT overall conspicuity interpretation using a Wilcoxon matched pairs test. Results: A total of 50/78 (64%) cancers were detected on both synt2D and DBT by all the readers, while 28/78 (26%) cancers where not recognized by at least one reader on synt2D. For each reader, in comparison with synt2D, DBT increased significantly the conspicuity of ILC (p < 0.0001). The raw proportion of high versus low conspicuity by modality confirmed that cancers were more likely to have high conspicuity at DBT than synt2D. Conclusions: ILCs were more likely to have high conspicuity at DBT than at synt2D, increasing the chances of the detection of ILC breast cancer.

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