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Wang J, Sun H, Jiang K, Cao W, Chen S, Zhu J, Yang X, Zheng J. CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107831. [PMID: 37783114 DOI: 10.1016/j.cmpb.2023.107831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 12/25/2022] [Accepted: 09/23/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. METHODS The proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers' features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. RESULTS We performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. CONCLUSIONS The experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.
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Affiliation(s)
- Jingkun Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Haotian Sun
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Ke Jiang
- Gusu School, Nanjing Medical University, Suzhou 215006, China; Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China
| | - Weiwei Cao
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shuangqing Chen
- Gusu School, Nanjing Medical University, Suzhou 215006, China; Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou 215153, China
| | - Xiaodong Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
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Hussain S, Lafarga-Osuna Y, Ali M, Naseem U, Ahmed M, Tamez-Peña JG. Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics 2023; 24:401. [PMID: 37884877 PMCID: PMC10605943 DOI: 10.1186/s12859-023-05515-6] [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: 04/07/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico.
| | - Yareth Lafarga-Osuna
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, Australia
| | - Masroor Ahmed
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Jose Gerardo Tamez-Peña
- School of Medicine and Health Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
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Xu M, Yang H, Yang Q, Teng P, Hao H, Liu C, Yu S, Liu G. Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04859-z. [PMID: 37208454 DOI: 10.1007/s00432-023-04859-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. METHODS The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. RESULTS The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. CONCLUSION The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Huimin Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Qi Yang
- Department of Radiology, The First Hospital of Jilin University, No.71 Xinmin Street, Changchun, 130012, China.
| | - Peihong Teng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Haifeng Hao
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Chang Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Shaonan Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
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Xiao B, Sun H, Meng Y, Peng Y, Yang X, Chen S, Yan Z, Zheng J. Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network. Biomed Eng Online 2021; 20:71. [PMID: 34320986 PMCID: PMC8317331 DOI: 10.1186/s12938-021-00908-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/15/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The classification of benign and malignant microcalcification clusters (MCs) is an important task for computer-aided diagnosis (CAD) of digital breast tomosynthesis (DBT) images. Influenced by imaging method, DBT has the characteristic of anisotropic resolution, in which the resolution of intra-slice and inter-slice is quite different. In addition, the sharpness of MCs in different slices of DBT is quite different, among which the clearest slice is called focus slice. These characteristics limit the performance of CAD algorithms based on standard 3D convolution neural network (CNN). METHODS To make full use of the characteristics of the DBT, we proposed a new ensemble CNN, which consists of the 2D ResNet34 and the anisotropic 3D ResNet to extract the 2D focus slice features and 3D contextual features of MCs, respectively. Moreover, the anisotropic 3D convolution is used to build 3D ResNet to avoid the influence of DBT anisotropy. RESULTS The proposed method was evaluated on 495 MCs in DBT images of 275 patients, which are collected from our collaborative hospital. The area under the curve (AUC) of receiver operating characteristic (ROC) and accuracy of classifying benign and malignant MCs using decision-level ensemble strategy were 0.8837 and 82.00%, which were significantly higher than the experimental results of 2D ResNet34 (AUC: 0.8264, ACC: 76.00%) and anisotropic 3D ResNet (AUC: 0.8455, ACC: 76.00%). Compared with the results of 3D features classification in the radiomics, the AUC of the deep learning method with decision-level ensemble strategy was improved by 0.0435, and the F1 score was improved from 79.37 to 85.71%. More importantly, the sensitivity increased from 78.13 to 84.38%, and the specificity increased from 66.67 to 77.78%, which effectively reduced the false positives of diagnosis CONCLUSION: The results fully prove that the ensemble CNN can effectively integrate 2D features and 3D features, improve the classification performance of benign and malignant MCs in DBT, and reduce the false positives.
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Affiliation(s)
- Bingbing Xiao
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Haotian Sun
- University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - You Meng
- Department of Breast Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
- Gusu School, Nanjing Medical University, Suzhou, China
| | - Yunsong Peng
- University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Shuangqing Chen
- Gusu School, Nanjing Medical University, Suzhou, China.
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China.
| | - Jian Zheng
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
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