1
|
Singla DV, Garg DD, Bhavith. Optimizing contrast enhanced mammography: A comprehensive review of artefacts, causes, and remedies. Curr Probl Diagn Radiol 2025:S0363-0188(25)00091-X. [PMID: 40360342 DOI: 10.1067/j.cpradiol.2025.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2025] [Revised: 05/02/2025] [Accepted: 05/06/2025] [Indexed: 05/15/2025]
Abstract
Contrast enhanced mammography (CEM) is a promising imaging technique in breast imaging, combining efficiency and cost-effectiveness with the ability to provide structural as well as functional information. However, like all imaging modalities, CEM is prone to artifacts that can occur at various stages of the process, including patient preparation, image acquisition, equipment calibration, and digital subtraction. Recognising and rectifying these artifacts is essential for achieving optimal image quality and accurate diagnosis. The purpose of this article is to familiarise the readers with common artifacts encountered during CEM and minimise their impact on image interpretation, with a focus on strategies for optimising CEM imaging. We have also described a few previously uncharted CEM-specific artifacts observed in our clinical experience. Additionally, this review highlights major pitfalls encountered during CEM reporting and measures to improve diagnostic accuracy.
Collapse
Affiliation(s)
- Dr Veenu Singla
- PGIMER, Department of Radiodiagnosis, Chandigarh 160012, India.
| | - Dr Dollphy Garg
- PGIMER, Department of Radiodiagnosis, Chandigarh 160012, India.
| | - Bhavith
- PGIMER, Department of Radiodiagnosis, Chandigarh 160012, India.
| |
Collapse
|
2
|
Gennaro G, Vatteroni G, Bernardi D, Caumo F. Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study. Eur Radiol Exp 2024; 8:113. [PMID: 39400659 PMCID: PMC11473475 DOI: 10.1186/s41747-024-00516-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 09/19/2024] [Indexed: 10/15/2024] Open
Abstract
BACKGROUND Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. METHODS A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. RESULTS On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. CONCLUSION One system outperformed others in DES algorithms, providing higher CNR at lower doses. RELEVANCE STATEMENT This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. KEY POINTS DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose.
Collapse
Affiliation(s)
| | - Giulia Vatteroni
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Radiology Department, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Daniela Bernardi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Radiology Department, IRCCS Humanitas Research Hospital, Milan, Italy
| | | |
Collapse
|
3
|
Cortes-Rojas FD, Hernández-Rodríguez YM, Bayareh-Mancilla R, Cigarroa-Mayorga OE. An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis. Diagnostics (Basel) 2024; 14:2144. [PMID: 39410548 PMCID: PMC11475286 DOI: 10.3390/diagnostics14192144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 10/20/2024] Open
Abstract
Breast cancer remains a major health concern worldwide, requiring the advancement of early detection methods to improve prognosis and treatment outcomes. In this sense, mammography is regarded as the gold standard in breast cancer screening and early detection. However, in a scenario where extensive analysis is required, a large set of mammograms conducted by radiologists may carry out false negative or false positive diagnoses. Therefore, artificial intelligence has emerged in recent years as a method for enhancing timing in breast cancer diagnosis. Nonetheless, preprocessing stages are required to prepare the mammography dataset to enhance learning models to correctly identify breast anomalies. In this paper, we introduce a novel method employing convolutional neural networks (CNNs) to segment the pectoral muscle in 1288 mediolateral oblique mammograms (MLOs), thereby addressing class imbalance and overfitting between classes, and dataset augmentation based on translation, rotation, and scale transformation. The effectiveness of the model was assessed through a confusion matrix and performance metrics, highlighting an average Dice coefficient of 0.98 and a Jaccard index of 0.96. The outcomes demonstrate the model capability to accurately identify three classes: pectoral muscle, breast, and background. This study emphasizes the importance of tackling class imbalance problems and augmenting data for the training of models for reliable early breast cancer detection.
Collapse
Affiliation(s)
- Fausto David Cortes-Rojas
- Departamento de Ingeniería Eléctrica/Sección de Bioelectrónica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, Ciudad de México 07360, Mexico;
| | | | - Rafael Bayareh-Mancilla
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. IPN No. 2580, Ciudad de México 07340, Mexico;
| | - Oscar Eduardo Cigarroa-Mayorga
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. IPN No. 2580, Ciudad de México 07340, Mexico;
| |
Collapse
|
4
|
Ripaud E, Jailin C, Quintana GI, Milioni de Carvalho P, Sanchez de la Rosa R, Vancamberg L. Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography. Phys Med Biol 2024; 69:115013. [PMID: 38657641 DOI: 10.1088/1361-6560/ad42ff] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/24/2024] [Indexed: 04/26/2024]
Abstract
Background.Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described inbreast imaging reporting and data system(BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM.Materials and methods.A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated.Results.The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2-71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements.Conclusion.The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.
Collapse
|
5
|
Covington MF, Salmon S, Weaver BD, Fajardo LL. State-of-the-art for contrast-enhanced mammography. Br J Radiol 2024; 97:695-704. [PMID: 38374651 PMCID: PMC11027262 DOI: 10.1093/bjr/tqae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/23/2023] [Accepted: 01/12/2024] [Indexed: 02/21/2024] Open
Abstract
Contrast-enhanced mammography (CEM) is an emerging breast imaging technology with promise for breast cancer screening, diagnosis, and procedural guidance. However, best uses of CEM in comparison with other breast imaging modalities such as tomosynthesis, ultrasound, and MRI remain inconclusive in many clinical settings. This review article summarizes recent peer-reviewed literature, emphasizing retrospective reviews, prospective clinical trials, and meta-analyses published from 2020 to 2023. The intent of this article is to supplement prior comprehensive reviews and summarize the current state-of-the-art of CEM.
Collapse
Affiliation(s)
- Matthew F Covington
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, United States
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, Salt Lake City, UT, 84112, United States
| | - Samantha Salmon
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, United States
| | - Bradley D Weaver
- Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, 84112, United States
| | - Laurie L Fajardo
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, United States
| |
Collapse
|
6
|
Marshall NW, Cockmartin L, Bosmans H. Investigation of test methods for QC in dual-energy based contrast-enhanced digital mammography systems: II. Artefacts/uniformity, exposure time and phantom-based dosimetry. Phys Med Biol 2023; 68:215016. [PMID: 37820686 DOI: 10.1088/1361-6560/ad027f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Part II of this study describes constancy tests for artefacts and image uniformity, exposure time, and phantom-based dosimetry; these are applied to four mammography systems equipped with contrast enhanced mammography (CEM) capability. Artefacts were tested using a breast phantom that simulated breast shape and thickness change at the breast edge. Image uniformity was assessed using rectangular poly(methyl)methacrylate PMMA plates at phantom thicknesses of 20, 40 and 60 mm, for the low energy (LE), high energy (HE) images and the recombined CEM image. Uniformity of signal and of the signal to noise ratio was quantified. To estimate CEM exposure times, breast simulating blocks were imaged in automatic exposure mode. The resulting x-ray technique factors were then set manually and exposure time for LE and HE images and total CEM acquisition time was measured with a multimeter. Mean glandular dose (MGD) was assessed as a function of simulated breast thickness using three different phantom compositions: (i) glandular and adipose breast tissue simulating blocks combined to give glandularity values that were typical of those in a screening population, as thickness was changed (ii) PMMA sheets combined with polyethylene blocks (iii) PMMA sheets with spacers. Image uniformity was superior for LE compared to HE images. Two systems did not generate recombined images for the uniformity test when the detector was fully covered. Acquisition time for a CEM image pair for a 60 mm thick breast equivalent phantom ranged from 3.4 to 10.3 s. Phantom composition did not have a strong influence on MGD, with differences generally smaller than 10%. MGD for the HE images was lower than for the LE images, by a factor of between 1.3 and 4.0, depending on system and simulated breast thickness. When combined with the iodine signal assessment in part I, these tests provide a comprehensive assessment of CEM system imaging performance.
Collapse
Affiliation(s)
- N W Marshall
- UZ Gasthuisberg, Department of Radiology, Herestraat 49, B-3000 Leuven, Belgium
- Medical Imaging Research Center, Medical Physics and Quality Assessment, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - L Cockmartin
- UZ Gasthuisberg, Department of Radiology, Herestraat 49, B-3000 Leuven, Belgium
| | - H Bosmans
- UZ Gasthuisberg, Department of Radiology, Herestraat 49, B-3000 Leuven, Belgium
- Medical Imaging Research Center, Medical Physics and Quality Assessment, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| |
Collapse
|
7
|
Jailin C, Mohamed S, Iordache R, Milioni De Carvalho P, Ahmed SY, Abdel Sattar EA, Moustafa AFI, Gomaa MM, Kamal RM, Vancamberg L. AI-Based Cancer Detection Model for Contrast-Enhanced Mammography. Bioengineering (Basel) 2023; 10:974. [PMID: 37627859 PMCID: PMC10451612 DOI: 10.3390/bioengineering10080974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/12/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. MATERIALS & METHODS A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. RESULTS The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. CONCLUSION The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability.
Collapse
Affiliation(s)
| | - Sara Mohamed
- GE HealthCare, 283 Rue de la Miniére, 78530 Buc, France
| | | | | | - Salwa Yehia Ahmed
- Baheya Foundation for Early Detection and Treatment of Breast Cancer, El Haram, Giza 78530, Egypt
| | | | - Amr Farouk Ibrahim Moustafa
- Baheya Foundation for Early Detection and Treatment of Breast Cancer, El Haram, Giza 78530, Egypt
- National Cancer Institute, Cairo University, 1 Kasr Elainy Street Fom Elkalig, Cairo 11511, Egypt
| | - Mohammed Mohammed Gomaa
- Baheya Foundation for Early Detection and Treatment of Breast Cancer, El Haram, Giza 78530, Egypt
- National Cancer Institute, Cairo University, 1 Kasr Elainy Street Fom Elkalig, Cairo 11511, Egypt
| | - Rashaa Mohammed Kamal
- Baheya Foundation for Early Detection and Treatment of Breast Cancer, El Haram, Giza 78530, Egypt
- Radiology Department, Kasr El Ainy Hospital, Cairo University, Cairo 11511, Egypt
| | | |
Collapse
|
8
|
Harper LK, Faulk EA, Patel B, Collins P, Rochman C. How to Recognize and Correct Artifacts on Contrast-Enhanced Mammography. JOURNAL OF BREAST IMAGING 2023; 5:486-497. [PMID: 38416909 DOI: 10.1093/jbi/wbad041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Indexed: 03/01/2024]
Abstract
Contrast-enhanced mammography (CEM) has emerged as an important new technology in breast imaging. It can demonstrate a number of imaging artifacts that have the potential to limit interpretation by either obscuring or potentially mimicking disease. Commonly encountered artifacts on CEM include patient motion artifacts (ripple and misregistration), pectoral highlighting artifact, breast implant artifact, halo artifact, corrugation artifact, cloudy fat artifact, contrast artifacts (retention and contamination), skin artifacts (skin line enhancement and skin overexposure), and skin lesions. Skin lesions may demonstrate a variety of imaging appearances and have both benign and malignant etiologies. It is important that the technologist, radiologist, and physicist be aware of potential artifacts and skin enhancement on CEM that may affect interpretation and understand their causes and potential solutions.
Collapse
Affiliation(s)
- Laura K Harper
- Mayo Clinic Arizona, Department of Radiology, Phoenix, AZ, USA
| | - Ellen A Faulk
- University of Virginia, Department of Radiology and Medical Imaging, Charlottesville, VA, USA
| | - Bhavika Patel
- Mayo Clinic Arizona, Department of Radiology, Phoenix, AZ, USA
| | - Patricia Collins
- University of Virginia, Department of Radiology and Medical Imaging, Charlottesville, VA, USA
| | - Carrie Rochman
- University of Virginia, Department of Radiology and Medical Imaging, Charlottesville, VA, USA
| |
Collapse
|
9
|
Bennett C, Woodard S, Zamora K. A pictorial guide to artifacts on contrast mammography: How to avoid pitfalls and improve interpretation. Clin Imaging 2023; 101:215-222. [PMID: 37429167 DOI: 10.1016/j.clinimag.2023.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
Contrast-enhanced mammography (CEM) is an increasingly accepted emerging imaging modality that demonstrates a similar sensitivity to MRI but has the advantage of being less time consuming and inexpensive. The use of CEM continues to expand as it is recognized and utilized as a valuable tool for diagnostic and potentially screening examinations. As with any radiologic examination, artifacts occur and knowledge of these is important for adequate image interpretation. The purpose of this paper is to provide a pictorial review the common artifacts encountered on CEM examinations and identify causes and potential resolutions.
Collapse
Affiliation(s)
- Caroline Bennett
- Heersink School of Medicine, University of Alabama Birmingham, 510 20th St S, Birmingham, AL 35233, United States
| | - Stefanie Woodard
- University of Alabama at Birmingham, Department of Radiology, JTN 478, 619 20th Street South, Birmingham, AL 35249, United States
| | - Kathryn Zamora
- University of Alabama at Birmingham, Department of Radiology, JTN 478, 619 20th Street South, Birmingham, AL 35249, United States.
| |
Collapse
|
10
|
Jailin C, Milioni De Carvalho P, Mohamed S, Vancamberg L, Amr Farouk Ibrahim M, Gomaa MM, Kamal RM, Muller S. Deformable registration with intensity correction for CESM monitoring response to Neoadjuvant Chemotherapy. Biomed Phys Eng Express 2023; 9. [PMID: 36758233 DOI: 10.1088/2057-1976/acba9f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/09/2023] [Indexed: 02/11/2023]
Abstract
This paper proposes a robust longitudinal registration method for Contrast Enhanced Spectral Mammography in monitoring neoadjuvant chemotherapy. Because breast texture intensity changes with the treatment, a non-rigid registration procedure with local intensity compensations is developed. The approach allows registering the low energy images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding recombined images. The difference of registered images, called residual, makes vanishing the breast texture that did not changed between the two exams. Consequently, this registered residual allows identifying local density and iodine changes, especially in the lesion area. The method is validated with a synthetic NAC case where ground truths are available. Then the procedure is applied to 51 patients with 208 CESM image pairs acquired before and after the chemotherapy treatment. The proposed registration converged in all 208 cases. The intensity-compensated registration approach is evaluated with different mathematical metrics and through the repositioning of clinical landmarks (RMSE: 5.9 mm) and outperforms state-of-the-art registration techniques.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Rasha Mohammed Kamal
- Baheya Foundation For Early Detection And Treatment Of Breast Cancer, El Haram, Giza, Egypt
| | | |
Collapse
|
11
|
Taylor DB, Burrows S, Saunders CM, Parizel PM, Ives A. Contrast-enhanced mammography (CEM) versus MRI for breast cancer staging: detection of additional malignant lesions not seen on conventional imaging. Eur Radiol Exp 2023; 7:8. [PMID: 36781808 PMCID: PMC9925630 DOI: 10.1186/s41747-022-00318-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 12/15/2022] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND Contrast-enhanced mammography (CEM) is more available than MRI for breast cancer staging but may not be as sensitive in assessing disease extent. We compared CEM and MRI in this setting. METHODS Fifty-nine women with invasive breast cancer underwent preoperative CEM and MRI. Independent pairs of radiologists read CEM studies (after reviewing a 9-case set prior to study commencement) and MRI studies (with between 5 and 25 years of experience in breast imaging). Additional lesions were assigned National Breast Cancer Centre (NBCC) scores. Positive lesions (graded NBCC ≥ 3) likely to influence surgical management underwent ultrasound and/or needle biopsy. True-positive lesions were positive on imaging and pathology (invasive or in situ). False-positive lesions were positive on imaging but negative on pathology (high-risk or benign) or follow-up. False-negative lesions were negative on imaging (NBCC < 3 or not identified) but positive on pathology. RESULTS The 59 women had 68 biopsy-proven malignant lesions detected on mammography/ultrasound, of which MRI demonstrated 66 (97%) and CEM 67 (99%) (p = 1.000). Forty-one additional lesions were detected in 29 patients: six of 41 (15%) on CEM only, 23/41 (56%) on MRI only, 12/41 (29%) on both; CEM detected 1/6 and MRI 6/6 malignant additional lesions (p = 0.063), with a positive predictive value (PPV) of 1/13 (8%) and 6/26 (23%) (p = 0.276). CONCLUSIONS While MRI and CEM were both highly sensitive for lesions detected at mammography/ultrasound, CEM may not be as sensitive as MRI in detecting additional otherwise occult foci of malignancy. TRIAL REGISTRATION Australian and New Zealand Clinical Trials Registry: ACTRN 12613000684729.
Collapse
Affiliation(s)
- Donna B. Taylor
- grid.416195.e0000 0004 0453 3875Department of Diagnostic and Interventional Radiology, Royal Perth Hospital, Wellington Street, Perth, 6000 WA Australia ,grid.1012.20000 0004 1936 7910Medical School, The University of Western Australia (M570), 35 Stirling Highway, Perth, Australia
| | - Sally Burrows
- grid.1012.20000 0004 1936 7910Medical School, The University of Western Australia (M570), 35 Stirling Highway, Perth, Australia
| | - Christobel M. Saunders
- grid.416153.40000 0004 0624 1200Department of Surgery, Royal Melbourne Hospital, 300 Grattan Street, Parkville, VIC Australia
| | - Paul M. Parizel
- grid.416195.e0000 0004 0453 3875Department of Diagnostic and Interventional Radiology, Royal Perth Hospital, Wellington Street, Perth, 6000 WA Australia ,grid.1012.20000 0004 1936 7910Medical School, The University of Western Australia (M570), 35 Stirling Highway, Perth, Australia
| | - Angela Ives
- grid.1012.20000 0004 1936 7910Medical School, The University of Western Australia (M570), 35 Stirling Highway, Perth, Australia
| |
Collapse
|