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Chikarmane SA, Smith S. Background Parenchymal Enhancement: A Comprehensive Update. Radiol Clin North Am 2024; 62:607-617. [PMID: 38777537 DOI: 10.1016/j.rcl.2023.12.013] [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] [Indexed: 05/25/2024]
Abstract
Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.
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Affiliation(s)
- Sona A Chikarmane
- Breast Imaging Division, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Sharon Smith
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
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Müller-Franzes G, Khader F, Tayebi Arasteh S, Huck L, Bode M, Han T, Lemainque T, Kather JN, Nebelung S, Kuhl C, Truhn D. Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization. Radiology 2024; 312:e232304. [PMID: 39012249 DOI: 10.1148/radiol.232304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.
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Affiliation(s)
- Gustav Müller-Franzes
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Firas Khader
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Soroosh Tayebi Arasteh
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Luisa Huck
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Maike Bode
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Tianyu Han
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Teresa Lemainque
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Jakob Nikolas Kather
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Sven Nebelung
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Christiane Kuhl
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Daniel Truhn
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
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Wang H, H M van der Velden B, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial. Eur J Radiol 2024; 175:111442. [PMID: 38583349 DOI: 10.1016/j.ejrad.2024.111442] [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: 10/24/2023] [Revised: 02/06/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. METHODS This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. RESULTS The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). CONCLUSION It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.
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Affiliation(s)
- Hui Wang
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Erik Verburg
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marije F Bakker
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Carla H van Gils
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Kenneth G A Gilhuijs
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
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Nowakowska S, Borkowski K, Ruppert C, Hejduk P, Ciritsis A, Landsmann A, Marcon M, Berger N, Boss A, Rossi C. Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake. Bioengineering (Basel) 2024; 11:556. [PMID: 38927793 PMCID: PMC11200390 DOI: 10.3390/bioengineering11060556] [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: 02/15/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network's predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.
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Affiliation(s)
- Sylwia Nowakowska
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | | | - Carlotta Ruppert
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
- b-rayZ AG, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Patryk Hejduk
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | - Alexander Ciritsis
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
- b-rayZ AG, Wagistrasse 21, 8952 Schlieren, Switzerland
| | - Anna Landsmann
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | - Magda Marcon
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | - Nicole Berger
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | - Andreas Boss
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
| | - Cristina Rossi
- Diagnostic and Interventional Radiology, University Hospital Zürich, University Zürich, Rämistrasse 100, 8091 Zürich, Switzerland (C.R.)
- b-rayZ AG, Wagistrasse 21, 8952 Schlieren, Switzerland
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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.
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [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: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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Du Y, Wang D, Liu M, Zhang X, Ren W, Sun J, Yin C, Yang S, Zhang L. Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images. J Cancer Res Ther 2024; 20:625-632. [PMID: 38687933 DOI: 10.4103/jcrt.jcrt_1796_23] [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: 08/10/2023] [Accepted: 12/01/2023] [Indexed: 05/02/2024]
Abstract
OBJECTIVE To establish a multimodal model for distinguishing benign and malignant breast lesions. MATERIALS AND METHODS Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. RESULTS We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. CONCLUSION Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.
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Affiliation(s)
- Yanan Du
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Qianfoshan Hospital, Jinan City, Shandong Province, China
| | - Dawei Wang
- Department of Health Management Shandong University of Traditional Chinese Medicine, Jinan City, Shandong Province, China
| | - Menghan Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Qianfoshan Hospital, Jinan City, Shandong Province, China
| | - Xiaodong Zhang
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan City, Shandong Province, China
| | - Wanqing Ren
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan City, Shandong Province, China
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Qianfoshan Hospital, Jinan City, Shandong Province, China
| | - Jingxiang Sun
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan City, Shandong Province, China
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Qianfoshan Hospital, Jinan City, Shandong Province, China
| | - Chao Yin
- Department of Radiology, Yantai Taocun Central Hospital, Yantai City, Shandong Province, China
| | - Shiwei Yang
- Department of Anorectal Surgery, The First Affiliated Hospital of Shandong First Medical University and Qianfoshan Hospital, Jinan City, Shandong Province, China
| | - Li Zhang
- Department of Pharmacology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan City, Shandong Province, China
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Zhang B, Zhu J, Zhang P, Wei Y, Li Y, Xu A, Zhang Y, Zheng H, Dong X, Yang K, Dong C, Chen Z, Li X, Cheng L. A background parenchymal enhancement quantification framework of breast magnetic resonance imaging. Quant Imaging Med Surg 2023; 13:8350-8357. [PMID: 38106260 PMCID: PMC10721989 DOI: 10.21037/qims-23-514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 09/15/2023] [Indexed: 12/19/2023]
Abstract
Background Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. Methods The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Results Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. Conclusions The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.
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Affiliation(s)
- Boya Zhang
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jingjin Zhu
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Peifang Zhang
- Department of Big Data Center, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yufan Wei
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Yan Li
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Aoxi Xu
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Yiheng Zhang
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Hongye Zheng
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiaohan Dong
- Department of Radiology, The Sixth Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Kaizhou Yang
- Department of Radiology, The Sixth Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Chuang Dong
- Department of Radiology, The Sixth Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Zhengming Chen
- Department of Radiology, The Sixth Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Liuquan Cheng
- Department of Radiology, The Sixth Medical Center of Chinese People’s Liberation Army General Hospital, Beijing, China
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9
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Bhowmik A, Monga N, Belen K, Varela K, Sevilimedu V, Thakur SB, Martinez DF, Sutton EJ, Pinker K, Eskreis-Winkler S. Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model. Invest Radiol 2023; 58:710-719. [PMID: 37058323 PMCID: PMC11334216 DOI: 10.1097/rli.0000000000000976] [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] [Indexed: 04/15/2023]
Abstract
OBJECTIVES The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. MATERIALS AND METHODS In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. RESULTS In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. CONCLUSIONS Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
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Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Natasha Monga
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kristin Belen
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Keitha Varela
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sunitha B. Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Danny F. Martinez
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth J. Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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10
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Bokacheva L. Automated Background Parenchymal Enhancement Measurements at MRI to Predict Breast Cancer Risk. Radiology 2023; 308:e231765. [PMID: 37750769 PMCID: PMC10546279 DOI: 10.1148/radiol.231765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 09/27/2023]
Affiliation(s)
- Louisa Bokacheva
- From the Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, 145 E 32nd St, 5th Floor, New York, NY 10016
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11
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Kokalj Ž, Džeroski S, Šprajc I, Štajdohar J, Draksler A, Somrak M. Machine learning-ready remote sensing data for Maya archaeology. Sci Data 2023; 10:558. [PMID: 37612295 PMCID: PMC10447422 DOI: 10.1038/s41597-023-02455-x] [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: 03/08/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
In our study, we set out to collect a multimodal annotated dataset for remote sensing of Maya archaeology, that is suitable for deep learning. The dataset covers the area around Chactún, one of the largest ancient Maya urban centres in the central Yucatán Peninsula. The dataset includes five types of data records: raster visualisations and canopy height model from airborne laser scanning (ALS) data, Sentinel-1 and Sentinel-2 satellite data, and manual data annotations. The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas - artificial reservoirs) within the study area, their exact locations, and boundaries. The dataset is ready for use with machine learning, including convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.
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Affiliation(s)
- Žiga Kokalj
- Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia.
| | - Sašo Džeroski
- Information and Communication Technologies, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
- Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Ivan Šprajc
- Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia
| | - Jasmina Štajdohar
- Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia
| | - Andrej Draksler
- Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia
| | - Maja Somrak
- Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia
- Information and Communication Technologies, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
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12
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Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
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13
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Grøvik E, Hoff SR. Editorial for "Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist". J Magn Reson Imaging 2022; 56:1077-1078. [PMID: 35343010 DOI: 10.1002/jmri.28183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 03/15/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Endre Grøvik
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Alesund, Norway.,Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Solveig Roth Hoff
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Alesund, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
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