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Reyes A, Sun L, Ha R, Desperito E, Brown M, Francescone MA, Ugras S, Wiechmann L, Rao R, Taback B. A Comprehensive Assessment of Radial Scars on Core Needle Biopsy in Patients with or without Breast Cancer: Upgrade Rate and Implications on Management. Clin Breast Cancer 2024; 24:e273-e278. [PMID: 38402106 DOI: 10.1016/j.clbc.2024.01.020] [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: 09/17/2023] [Revised: 01/22/2024] [Accepted: 01/28/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Radial scars/radial sclerosing lesions (RS) are benign breast lesions identified on core needle biopsy (CNB) which can upgrade to malignancy at excision. There is limited data on RS detection and upgrade rates with more sensitive imaging such as magnetic resonance imaging (MRI) and none during their detection for breast cancer workup and its implication on patient treatment decisions. METHODS A retrospective institutional study of RS diagnosed on CNB between January 2008 and December 2017 was conducted. Clinicopathologic and radiologic features of RS, patient treatment decisions, upgrade rates and long-term follow-up were examined. RESULTS We identified 133 patients with RS on CNB, of whom 106 opted for surgery for an upgrade rate to malignancy of 1.9%, 2 patients. Radial scar was diagnosed on mammogram in 60%, MRI in 25% and ultrasound in 15% of patients. In this cohort, 32 patients had their RS detected during breast cancer workup (coexistent group) and they were more likely to have their radial scar detected by MRI (60% vs. 14%, P < .001) and undergo more extensive surgery (94% vs. 75%, P = .02). Among the 27 patients electing observation of their RS, only one (3.7%) developed breast cancer. CONCLUSIONS Our results show an extremely low upgrade rate to malignancy of RS, regardless if there is coexisting breast cancer elsewhere. Despite this, RS still prompted more extensive surgical excisions. The findings do not support excision of RS even among breast cancer patients when identified at a separate site from their cancer.
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Affiliation(s)
- Arith Reyes
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY.
| | - Luona Sun
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY
| | - Richard Ha
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Elise Desperito
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Marc Brown
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Mark A Francescone
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Stacy Ugras
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY
| | - Lisa Wiechmann
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY
| | - Roshni Rao
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY
| | - Bret Taback
- Department of Surgery, Breast Surgery Division, Columbia University Medical Center, New York, NY
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Goh Y, Quek ST, Pillay P, Chou CP. Evaluation of architectural distortion with contrast-enhanced mammography. Clin Radiol 2024; 79:163-169. [PMID: 38114374 DOI: 10.1016/j.crad.2023.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/21/2023]
Abstract
Architectural distortion (AD) is the third most common abnormality detected on mammograms. In the absence of an accurate non-invasive tool to evaluate ADs, clinical management often requires surgical excision for histological diagnosis. This problem is expected to worsen with the growing use of digital breast tomosynthesis (DBT) and the resultant increasing detection of ADs. There is therefore a great clinical need for a diagnostic imaging tool to complement non-enhanced mammography for the evaluation of AD. Contrast-enhanced mammography (CEM) is an emerging breast imaging method that uses contrast media and the principle of dual-energy subtraction to evaluate vascularity of suspicious breast lesions. CEM, a cost-effective alternative to breast magnetic resonance imaging (MRI), can be used to evaluate AD by juxtaposing CEM images with non-enhanced mammograms for comparison. In this review, the authors aim to provide readers with an overview of the interpretation of AD on CEM using imaging examples. Relevant imaging features of CEM and their respective significance will be matched with information from a literature review. Finally, the authors would like to highlight the added value of CEM in relevant clinical applications in the assessment of AD.
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Affiliation(s)
- Y Goh
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - S T Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - P Pillay
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - C-P Chou
- Kaohsiung Veterans General Hospital, Radiology Department, No. 386, Dazhong 1st Rd, Zuoying Dist., Kaohsiung City 81362, Taiwan, ROC.
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Elfgen C, Leo C, Kubik-Huch RA, Muenst S, Schmidt N, Quinn C, McNally S, van Diest PJ, Mann RM, Bago-Horvath Z, Bernathova M, Regitnig P, Fuchsjäger M, Schwegler-Guggemos D, Maranta M, Zehbe S, Tausch C, Güth U, Fallenberg EM, Schrading S, Kothari A, Sonnenschein M, Kampmann G, Kulka J, Tille JC, Körner M, Decker T, Lax SF, Daniaux M, Bjelic-Radisic V, Kacerovsky-Strobl S, Condorelli R, Gnant M, Varga Z. Third International Consensus Conference on lesions of uncertain malignant potential in the breast (B3 lesions). Virchows Arch 2023:10.1007/s00428-023-03566-x. [PMID: 37330436 DOI: 10.1007/s00428-023-03566-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/01/2023] [Accepted: 05/17/2023] [Indexed: 06/19/2023]
Abstract
The heterogeneous group of B3 lesions in the breast harbors lesions with different malignant potential and progression risk. As several studies about B3 lesions have been published since the last Consensus in 2018, the 3rd International Consensus Conference discussed the six most relevant B3 lesions (atypical ductal hyperplasia (ADH), flat epithelial atypia (FEA), classical lobular neoplasia (LN), radial scar (RS), papillary lesions (PL) without atypia, and phyllodes tumors (PT)) and made recommendations for diagnostic and therapeutic approaches. Following a presentation of current data of each B3 lesion, the international and interdisciplinary panel of 33 specialists and key opinion leaders voted on the recommendations for further management after core-needle biopsy (CNB) and vacuum-assisted biopsy (VAB). In case of B3 lesion diagnosis on CNB, OE was recommended in ADH and PT, whereas in the other B3 lesions, vacuum-assisted excision was considered an equivalent alternative to OE. In ADH, most panelists (76%) recommended an open excision (OE) after diagnosis on VAB, whereas observation after a complete VAB-removal on imaging was accepted by 34%. In LN, the majority of the panel (90%) preferred observation following complete VAB-removal. Results were similar in RS (82%), PL (100%), and FEA (100%). In benign PT, a slim majority (55%) also recommended an observation after a complete VAB-removal. VAB with subsequent active surveillance can replace an open surgical intervention for most B3 lesions (RS, FEA, PL, PT, and LN). Compared to previous recommendations, there is an increasing trend to a de-escalating strategy in classical LN. Due to the higher risk of upgrade into malignancy, OE remains the preferred approach after the diagnosis of ADH.
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Affiliation(s)
- Constanze Elfgen
- Breast-Center Zurich, Zurich, Switzerland.
- University of Witten-Herdecke, Witten, Germany.
| | - Cornelia Leo
- Breast Center, Kantonsspital Baden, Baden, Switzerland
| | | | - Simone Muenst
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Noemi Schmidt
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Cecily Quinn
- Irish National Breast Screening Program & Department of Histopathology, St. Vincent's University Hospital Dublin and School of Medicine, University College Dublin, Dublin, Ireland
| | - Sorcha McNally
- Radiology Department, St. Vincent University Hospital, Dublin, Ireland
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ritse M Mann
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Maria Bernathova
- Department of Radiology and Nuclear Medicine, Medical University Vienna, Vienna, Austria
| | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University Graz, Graz, Austria
| | - Michael Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | | | - Martina Maranta
- Department of Gynecology, County Hospital Chur, Chur, Switzerland
| | - Sabine Zehbe
- Radiology Section, Breast Center Stephanshorn, St. Gallen, Switzerland
| | | | - Uwe Güth
- Breast-Center Zurich, Zurich, Switzerland
| | - Eva Maria Fallenberg
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Simone Schrading
- Department of Radiology, County Hospital Lucerne, Lucerne, Switzerland
| | - Ashutosh Kothari
- Breast Surgery Unit, Guy's and St Thomas's NHS Foundation Trust, London, UK
| | | | - Gert Kampmann
- Centro di Radiologia e Senologia Luganese, Lugano, Switzerland
| | - Janina Kulka
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University Budapest, Budapest, Hungary
| | | | | | - Thomas Decker
- Breast Pathology, Reference Centers Mammography Münster, University Hospital Münster, Münster, Germany
| | - Sigurd F Lax
- Department of Pathology, Hospital Graz II, Graz, and School of Medicine, Johannes Kepler University Linz, Linz, Austria
| | - Martin Daniaux
- BrustGesundheitZentrum Tirol, University Hospital Innsbruck, Innsbruck, Austria
| | - Vesna Bjelic-Radisic
- University of Witten-Herdecke, Witten, Germany
- Breast Unit, Helios University Hospital, University Witten/Herdecke, Witten, Germany
| | | | | | - Michael Gnant
- Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria
| | - Zsuzsanna Varga
- Department of Pathology and Molecular Pathology, University Hospital Zürich, Zürich, Switzerland
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Pathologic Outcomes in Single Versus Multiple Areas of Architectural Distortion on Digital Breast Tomosynthesis. AJR Am J Roentgenol 2023; 220:50-62. [PMID: 35895298 DOI: 10.2214/ajr.22.27625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND. Digital breast tomosynthesis (DBT) has led to increased detection of architectural distortion (AD). Management of patients with multiple areas of AD is not established. OBJECTIVE. The purpose of this article is to compare pathologic outcomes between single and multiple areas of AD identified on DBT. METHODS. This retrospective study included 402 patients (mean age, 56 years) who underwent image-guided core needle biopsy of AD visualized on DBT between April 7, 2017, and April 16, 2019. Patients were classified as having a single or multiple areas of AD according to the presence of distinct areas of AD described in the clinical radiology reports. The pathologic diagnosis for each AD was on the basis of the most aggressive pathology identified on either biopsy or surgical excision, if performed. Patients with single and multiple areas of AD were compared. RESULTS. The sample included 372 patients with a single AD (145 benign, 121 high risk, 105 malignant, one other) and 30 patients with multiple visualized ADs, including 66 biopsied ADs (10 benign, 35 high risk, 21 malignant). At pathologic assessment on a per-lesion basis, multiple compared with single ADs showed higher frequency of high-risk pathology (53.0% vs 32.5%, p = .002) but no difference in frequency of malignancy (31.8% vs 28.2%, p = .56). In multivariable analysis of a range of patient-related characteristics, the presence of single versus multiple areas of AD was not independently associated with malignancy (p = .51). In patients with multiple areas of AD, the most aggressive pathology (benign, high risk, or malignant) across all ADs was not associated with the number of ADs (p = .73). In 8 of 24 patients with at least two ipsilateral biopsied ADs, the ipsilateral areas varied in terms of most aggressive pathology; in 5 of 10 patients with contralateral biopsied ADs, the contralateral areas varied in most aggressive pathology. CONCLUSION. The presence of multiple areas of AD, compared with a single AD, was significantly more likely to yield high-risk pathology but was not significantly different in yield of malignancy. In patients with multiple ADs, multiple ipsilateral or contralateral ADs commonly varied in pathologic classification (benign, high risk, or malignant). CLINICAL IMPACT. These findings may help guide management of AD visualized by DBT, including multiple ADs. For patients with multiple areas of AD, biopsy of all areas may be warranted given variation in pathologic diagnoses.
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Liu Y, Tong Y, Wan Y, Xia Z, Yao G, Shang X, Huang Y, Chen L, Chen DQ, Liu B. Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network. Front Oncol 2023; 13:1119743. [PMID: 37035200 PMCID: PMC10075355 DOI: 10.3389/fonc.2023.1119743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Background Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis. Methods This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020. Patients with AD in the breast in FFDM were included. Machine learning models for AD identification were developed using the Mask RCNN method. Receiver operating characteristics (ROC) curves, their areas under the curve (AUCs), and recall/sensitivity were used to evaluate the models. Models with the highest AUCs were selected for malignant AD diagnosis. Results A total of 349 AD patients (190 with malignant AD) were enrolled. EfficientNetV2, EfficientNetV1, ResNext, and ResNet were developed for AD identification, with AUCs of 0.89, 0.87, 0.81 and 0.79. The AUC of EfficientNetV2 was significantly higher than EfficientNetV1 (0.89 vs. 0.78, P=0.001) for malignant AD diagnosis, and the recall/sensitivity of the EfficientNetV2 model was 0.93. Conclusion The Mask-RCNN-based EfficientNetV2 model has a good diagnostic value for malignant AD.
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Affiliation(s)
- Yuanyuan Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yunfei Tong
- Department of Engineering, Shanghai Yanghe Huajian Artificial Intelligence Technology Co., Ltd, Shanghai, China
| | - Yun Wan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ziqiang Xia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guoyan Yao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaojing Shang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Huang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lijun Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Daniel Q. Chen
- Artificial Intelligence (AI), Research Lab, Boston Meditech Group, Burlington, MA, United States
- *Correspondence: Bo Liu, ; Daniel Q. Chen,
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu, ; Daniel Q. Chen,
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Chen X, Zhang Y, Zhou J, Wang X, Liu X, Nie K, Lin X, He W, Su MY, Cao G, Wang M. Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning. Front Oncol 2022; 12:991892. [PMID: 36582788 PMCID: PMC9792864 DOI: 10.3389/fonc.2022.991892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022] Open
Abstract
Purpose To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. Materials and Methods A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant. Results The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas. Conclusion The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.
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Affiliation(s)
- Xiao Chen
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States,Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jiahuan Zhou
- Department of Radiology, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Xinmiao Liu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Xiaomin Lin
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenwen He
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan,*Correspondence: Min-Ying Su, ; Guoquan Cao, ; Meihao Wang,
| | - Guoquan Cao
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,*Correspondence: Min-Ying Su, ; Guoquan Cao, ; Meihao Wang,
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,*Correspondence: Min-Ying Su, ; Guoquan Cao, ; Meihao Wang,
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Editor's Notebook: July 2022. AJR Am J Roentgenol 2022; 219:3-4. [PMID: 35723243 DOI: 10.2214/ajr.22.27784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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