1
|
Shamir SB, Sasson AL, Margolies LR, Mendelson DS. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering (Basel) 2024; 11:451. [PMID: 38790318 PMCID: PMC11117903 DOI: 10.3390/bioengineering11050451] [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: 03/21/2024] [Revised: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
Collapse
Affiliation(s)
- Stephanie B. Shamir
- Department of Diagnostic, Molecular and Interventional Radiology, The Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | | | | | | |
Collapse
|
2
|
Bauer E, Levy MS, Domachevsky L, Anaby D, Nissan N. Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review. Clin Imaging 2021; 83:41-50. [PMID: 34953310 DOI: 10.1016/j.clinimag.2021.11.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/29/2021] [Accepted: 11/15/2021] [Indexed: 12/20/2022]
Abstract
Within the past decade, background parenchymal enhancement (BPE) and background parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the diagnosis and treatment monitoring of breast cancer. Growing evidence supports the role of breast parenchyma vascularity and metabolic activity as probable risk factors for breast cancer development. Furthermore, in the presence of a newly-diagnosed breast cancer, added clinically-relevant data was surprisingly found in the respective imaging properties of the non-affected contralateral breast. Evaluation of the contralateral BPE and BPU have been found to be especially instrumental in predicting the prognosis of a patient with breast cancer and even anticipating their response to neoadjuvant chemotherapy. Simultaneously, further research has found a link between these two biomarkers, even though they represent different physical properties. The aim of this review is to provide an up to date summary of the current clinical applications of BPE and BPU as breast cancer imaging biomarkers with the hope that it propels their further usage in clinical practice.
Collapse
Affiliation(s)
- Ethan Bauer
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Miri Sklair Levy
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Liran Domachevsky
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Noam Nissan
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel.
| |
Collapse
|
3
|
AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2021; 49:596-608. [PMID: 34374796 PMCID: PMC8803815 DOI: 10.1007/s00259-021-05492-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 07/06/2021] [Indexed: 12/17/2022]
Abstract
Purpose To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. Methods A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar’s test. Results Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). Conclusion A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05492-z.
Collapse
|
4
|
Nam Y, Park GE, Kang J, Kim SH. Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models. J Magn Reson Imaging 2020; 53:818-826. [PMID: 33219624 DOI: 10.1002/jmri.27429] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). PURPOSE To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification. STUDY TYPE Retrospective. POPULATION A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set. FIELD STRENGTH/SEQUENCE 3T and 1.5T; T2 -weighted, fat-saturated T1 -weighted (T1 W) with dynamic contrast enhancement (DCE). ASSESSMENT Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre- and postcontrast T1 W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. STATISTICAL TESTS Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification. RESULTS The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods. DATA CONCLUSION This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Yoonho Nam
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.,Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Ga Eun Park
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Junghwa Kang
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| |
Collapse
|
5
|
Leithner D, Horvat JV, Bernard-Davila B, Helbich TH, Ochoa-Albiztegui RE, Martinez DF, Zhang M, Thakur SB, Wengert GJ, Staudenherz A, Jochelson MS, Morris EA, Baltzer PAT, Clauser P, Kapetas P, Pinker K. A multiparametric [ 18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2019; 46:1878-1888. [PMID: 31197455 PMCID: PMC6647078 DOI: 10.1007/s00259-019-04331-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/03/2019] [Indexed: 02/03/2023]
Abstract
Purpose To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. Methods In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). Results There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis. Conclusion A multiparametric [18F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.
Collapse
Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Blanca Bernard-Davila
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Michelle Zhang
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Georg J Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Anton Staudenherz
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria.
| |
Collapse
|
6
|
Is Background Parenchymal Enhancement in Breast Magnetic Resonance Imaging Associated with Breast Cancer? INTERNATIONAL JOURNAL OF CANCER MANAGEMENT 2018. [DOI: 10.5812/ijcm.64918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
7
|
You C, Kaiser AK, Baltzer P, Krammer J, Gu Y, Peng W, Schönberg SO, Kaiser CG. The Assessment of Background Parenchymal Enhancement (BPE) in a High-Risk Population: What Causes BPE? Transl Oncol 2018; 11:243-249. [PMID: 29413756 PMCID: PMC5884181 DOI: 10.1016/j.tranon.2017.12.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 12/07/2017] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To investigate promoting factors for background parenchymal enhancement (BPE) in MR mammography (MRM). METHODS 146 patients were retrospectively evaluated, including 91 high-risk patients (50 BRCA patients, 41 patients with elevated lifetime risk). 56 screening patients were matched to the high-risk cases on the basis of age. The correlation of BPE with factors such as fibroglandular tissue (FGT), age, menopausal status, breast cancer, high-risk precondition as well as motion were investigated using linear regression. RESULTS BPE positively correlated with FGT (P<.001) and negatively correlated with menopausal status (P<.001). Cancer did not show an effect on BPE (P>.05). A high-risk precondition showed a significant impact on the formation of BPE (P<.05). However, when corrected for motion, the correlation between BPE and a high-risk precondition became weak and insignificant, and a highly significant association between BPE and motion was revealed (P<.01). CONCLUSION BPE positively correlated with FGT and negatively correlated with age. Cancer did not have an effect on BPE. A high-risk precondition appears to have a negative effect on BPE. However, when corrected for motion, high-risk preconditions became insignificant. Technical as well as physiological influences seem to play an important role in the formation of BPE.
Collapse
Affiliation(s)
- Chao You
- Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | | | - Pascal Baltzer
- Department of Neuroradiology, Friedrich-Alexander-University Hospital Erlangen-Nürnberg
| | - Julia Krammer
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim-University of Heidelberg
| | - Yajia Gu
- Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Weijun Peng
- Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Stefan O Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim-University of Heidelberg
| | - Clemens G Kaiser
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim-University of Heidelberg.
| |
Collapse
|
8
|
Arslan G, Çelik L, Çubuk R, Çelik L, Atasoy MM. Background parenchymal enhancement: is it just an innocent effect of estrogen on the breast? Diagn Interv Radiol 2017; 23:414-419. [PMID: 29097344 PMCID: PMC5669540 DOI: 10.5152/dir.2017.17048] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 06/18/2017] [Accepted: 06/23/2017] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to retrospectively analyze whether background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) correlates with menarche, menopause, reproductive period, menstrual cycle, gravidity-parity, family history of breast cancer, and the Breast Imaging-Reporting and Data System (BI-RADS) category of the patient. METHODS The study included 126 pre- and 78 postmenopausal women who underwent breast MRI in our institute between 2011 and 2016. Patients had filled a questionnaire form before the MRI. Two radiologists blinded to patient history graded the BPEs and the results were compared and analyzed. RESULTS The BPE was correlated with patient age and the day of menstrual cycle (P < 0.01 for both). No correlation was found with menarche age, menopause age, total number of reproductive years, and family history of breast cancer. In the moderate BPE group, only 1 out of 35 patients and in the marked BPE group only 1 out of 13 patients were postmenopausal and had BI-RADS scores of 4 and 5, respectively. CONCLUSION Increased symmetrical BPE is mainly due to current hormonal status in the premenopausal women. High-grade BPE, whether symmetrical or not, is rarely seen in postmenopausal women; hence, these patients should be further investigated or closely followed up.
Collapse
Affiliation(s)
- Gözde Arslan
- From the Department of Radiology (G.A. , L.Ç. (0000-0001-7030-4999), R.Ç., M.M.A.), Maltepe University School of Medicine, İstanbul, Turkey; Radiologica Imaging Center (L.Ç. (0000-0001-6692-0828)), İstanbul, Turkey
| | - Levent Çelik
- From the Department of Radiology (G.A. , L.Ç. (0000-0001-7030-4999), R.Ç., M.M.A.), Maltepe University School of Medicine, İstanbul, Turkey; Radiologica Imaging Center (L.Ç. (0000-0001-6692-0828)), İstanbul, Turkey
| | - Rahmi Çubuk
- From the Department of Radiology (G.A. , L.Ç. (0000-0001-7030-4999), R.Ç., M.M.A.), Maltepe University School of Medicine, İstanbul, Turkey; Radiologica Imaging Center (L.Ç. (0000-0001-6692-0828)), İstanbul, Turkey
| | - Levent Çelik
- From the Department of Radiology (G.A. , L.Ç. (0000-0001-7030-4999), R.Ç., M.M.A.), Maltepe University School of Medicine, İstanbul, Turkey; Radiologica Imaging Center (L.Ç. (0000-0001-6692-0828)), İstanbul, Turkey
| | - Mehmet Mahir Atasoy
- From the Department of Radiology (G.A. , L.Ç. (0000-0001-7030-4999), R.Ç., M.M.A.), Maltepe University School of Medicine, İstanbul, Turkey; Radiologica Imaging Center (L.Ç. (0000-0001-6692-0828)), İstanbul, Turkey
| |
Collapse
|