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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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Kim YS, Yun BL, Chu AJ, Lee SH, Shin HJ, Kim SM, Jang M, Shin SU, Moon WK. Background Breast Parenchymal Signal During Menstrual Cycle on Diffusion-Weighted MRI: A Prospective Study in Healthy Premenopausal Women. Korean J Radiol 2024; 25:511-517. [PMID: 38807333 PMCID: PMC11136950 DOI: 10.3348/kjr.2023.1189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/30/2024] Open
Abstract
OBJECTIVE To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women. MATERIALS AND METHODS Seven healthy premenopausal women (median age, 37 years; range, 33-49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm² (b800), 1200 sec/mm² (b1200), and a synthetic 1500 sec/mm² (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed. RESULTS The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP). CONCLUSION Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.
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Affiliation(s)
- Yeon Soo Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Bo La Yun
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
| | - A Jung Chu
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sun Mi Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Mijung Jang
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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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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Shimizu A, Iwabuchi Y, Tsukada J, Nakahara T, Sakurai R, Tonda K, Jinzaki M. Correlation between breast cancer and background parenchymal uptake on 18F-fluorodeoxyglucose positron emission tomography. Eur J Radiol 2024; 173:111378. [PMID: 38382424 DOI: 10.1016/j.ejrad.2024.111378] [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: 11/13/2023] [Revised: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
PURPOSE This study aimed to investigate differences in background parenchymal uptake (BPU) between patients with and without breast cancer using 18F-fluorodeoxyglucose positron emission tomography. METHODS Female patients (n = 130, 62.9 ± 12.7 years) with newly diagnosed breast cancer and 50 healthy participants (59.6 ± 13.3 years) without breast cancer were retrospectively included. BPU was evaluated using the maximum standardized uptake value. Data on participant age, body mass index, blood glucose level, and menopausal status were collected from medical records. Breast density was evaluated using mammography. Logistic regression analysis and receiver operating characteristic curves were used to examine the correlation between breast cancer and various characteristic factors, including BPU. RESULTS The BPU of patients with breast cancer was significantly higher than that of controls (P < 0.001). The results of logistic regression analysis regarding the presence of breast cancer demonstrated that BPU and menopausal status showed higher odds ratios of 13.6 and 4.25, respectively. The area under the receiver operating characteristic curve for BPU was 0.751. CONCLUSIONS Patients with breast cancer showed higher 18F-fluorodeoxyglucose-BPU. Glucose metabolism of mammary glands may correlate with the development of breast cancer.
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Affiliation(s)
- Atsushi Shimizu
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan.
| | - Jitsuro Tsukada
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Takehiro Nakahara
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Ryosuke Sakurai
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan; Department of Radiology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga-gun, Tochigi 321-0293, Japan
| | - Kai Tonda
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo 160-8582, Japan
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Zheng G, Hou J, Shu Z, Peng J, Han L, Yuan Z, He X, Gong X. Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue. BMC Med Imaging 2024; 24:22. [PMID: 38245712 PMCID: PMC10800060 DOI: 10.1186/s12880-024-01198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.
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Affiliation(s)
- Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Xiangyang Gong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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Yuan Y, Xiang Z, Xia Y, Xie J, Jiang X, Lu Z. The role of ATP binding cassette (ABC) transporters in breast cancer: Evaluating prognosis, predicting immunity, and guiding treatment. Channels (Austin) 2023; 17:2273247. [PMID: 37905302 PMCID: PMC10761142 DOI: 10.1080/19336950.2023.2273247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
Breast cancer is currently the most prevalent form of cancer worldwide. Nevertheless, there remains limited clarity regarding our understanding of the tumor microenvironment and metabolic characteristics associated with it. ATP-binding cassette (ABC) transporters are the predominant transmembrane transporters found in organisms. Therefore, it is essential to investigate the role of ABC transporters in breast cancer. Transcriptome data from breast cancer patients were downloaded from the TCGA database. ABC transporter-related genes were obtained from the Genecards database. By LASSO regression, ABC-associated prognostic signature was constructed in breast cancer. Subsequently, immune microenvironment analysis was performed. Finally, cell experiments were performed to verify the function of ABCB7 in the breast cancer cell lines MDA-MB-231 and MCF-7. Using the ABC transporter-associated signature, we calculated a risk score for each breast cancer patient. Patients with breast cancer were subsequently categorized into high-risk and low-risk groups, utilizing the median risk score as the threshold. Notably, patients in the high-risk group exhibited significantly worse prognosis (P<0.05). Additionally, differences were observed in terms of immune cell infiltration levels, immune correlations, and gene expression of immune checkpoints between the two groups. Functional experiments conducted on breast cancer cell lines MDA-MB-231 and MCF-7 demonstrated that ABCB7 knockdown significantly diminished cell activity, proliferation, invasion, and migration. These findings emphasize the significance of understanding ABC transporter-mediated metabolic and transport characteristics in breast cancer, offering promising directions for further research and potential therapeutic interventions.
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Affiliation(s)
- Yuan Yuan
- Department of Laboratory Medicine, The Seventh People’s Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhouhong Xiang
- Department of Laboratory Medicine, The Seventh People’s Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuhua Xia
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, China Three Gorges University, Yichang, Hubei, China
| | - Jiaheng Xie
- Department of Plastic and Cosmetic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiudi Jiang
- Department of Laboratory Medicine, The Seventh People’s Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhicheng Lu
- Department of Laboratory Medicine, The Seventh People’s Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Nowakowska S, Borkowski K, Ruppert CM, Landsmann A, Marcon M, Berger N, Boss A, Ciritsis A, Rossi C. Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI. Insights Imaging 2023; 14:185. [PMID: 37932462 PMCID: PMC10628070 DOI: 10.1186/s13244-023-01531-5] [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: 07/11/2023] [Accepted: 09/25/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVES Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. METHODS For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. RESULTS To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006). CONCLUSIONS Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required. CRITICAL RELEVANCE STATEMENT A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor. KEY POINTS • Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift.
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Affiliation(s)
- Sylwia Nowakowska
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | | | - Carlotta M Ruppert
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Anna Landsmann
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Magda Marcon
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Nicole Berger
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- Present Address: Institut RadiologieSpital Lachen, Oberdorfstrasse 41, 8853, Lachen, Switzerland
| | - Andreas Boss
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- Present address: GZO AG Spital Wetzikon, Spitalstrasse 66, 8620, Wetzikon, Switzerland
| | - Alexander Ciritsis
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- b-rayZ AG, Wagistrasse 21, 8952, Schlieren, Switzerland
| | - Cristina Rossi
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- b-rayZ AG, Wagistrasse 21, 8952, Schlieren, Switzerland
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8
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Nissan N, Anaby D, Mahameed G, Bauer E, Moss Massasa EE, Menes T, Agassi R, Brodsky A, Grimm R, Nickel MD, Roccia E, Sklair-Levy M. Ultrafast DCE-MRI for discriminating pregnancy-associated breast cancer lesions from lactation related background parenchymal enhancement. Eur Radiol 2023; 33:8122-8131. [PMID: 37278853 DOI: 10.1007/s00330-023-09805-8] [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: 09/22/2022] [Revised: 03/31/2023] [Accepted: 04/27/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVE To investigate the utility of ultrafast dynamic-contrast-enhanced (DCE) MRI in visualization and quantitative characterization of pregnancy-associated breast cancer (PABC) and its differentiation from background-parenchymal-enhancement (BPE) among lactating patients. MATERIALS AND METHODS Twenty-nine lactating participants, including 10 PABC patients and 19 healthy controls, were scanned on 3-T MRI using a conventional DCE protocol interleaved with a golden-angle radial sparse parallel (GRASP) ultrafast sequence for the initial phase. The timing of the visualization of PABC lesions was compared to lactational BPE. Contrast-noise ratio (CNR) was compared between the ultrafast and conventional DCE sequences. The differences in each group's ultrafast-derived kinetic parameters including maximal slope (MS), time to enhancement (TTE), and area under the curve (AUC) were statistically examined using the Mann-Whitney test and receiver operator characteristic (ROC) curve analysis. RESULTS On ultrafast MRI, breast cancer lesions enhanced earlier than BPE (p < 0.0001), enabling breast cancer visualization freed from lactation BPE. A higher CNR was found for ultrafast acquisitions vs. conventional DCE (p < 0.05). Significant differences in AUC, MS, and TTE values were found between the tumor and BPE (p < 0.05), with ROC-derived AUC of 0.86 ± 0.06, 0.82 ± 0.07, and 0.68 ± 0.08, respectively. The BPE grades of the lactating PABC patients were reduced as compared with the healthy lactating controls (p < 0.005). CONCLUSION Ultrafast DCE MRI allows BPE-free visualization of lesions, improved tumor conspicuity, and kinetic quantification of breast cancer during lactation. Implementation of this method may assist in the utilization of breast MRI for lactating patients. CLINICAL RELEVANCE The ultrafast sequence appears to be superior to conventional DCE MRI in the challenging evaluation of the lactating breast. Thus, supporting its possible utilization in the setting of high-risk screening during lactation and the diagnostic workup of PABC. KEY POINTS • Differences in the enhancement slope of cancer relative to BPE allowed the optimal visualization of PABC lesions on mid-acquisitions of ultrafast DCE, in which the tumor enhanced prior to the background parenchyma. • The conspicuity of PABC lesions on top of the lactation-related BPE was increased using an ultrafast sequence as compared with conventional DCE MRI. • Ultrafast-derived maps provided further characterization and parametric contrast between PABC lesions and lactation-related BPE.
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Affiliation(s)
- Noam Nissan
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 St. Tel Hashomer, 5265601, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 St. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gazal Mahameed
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 St. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ethan Bauer
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Efi Efraim Moss Massasa
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 St. Tel Hashomer, 5265601, Ramat Gan, Israel
| | - Tehillah Menes
- Department of General Surgery, Sheba Medical Center, Ramat Gan, Israel
| | - Ravit Agassi
- Department of General Surgery, Soroka Medical Center, Beersheba, Israel
| | - Asia Brodsky
- Department of General Surgery, Bnei Zion Medical Center, Haifa, Israel
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Elisa Roccia
- MR Scientific Marketing, Siemens Healthcare GmbH, Erlangen, Germany
| | - Miri Sklair-Levy
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 St. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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9
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Goodburn R, Kousi E, Sanders C, Macdonald A, Scurr E, Bunce C, Khabra K, Reddy M, Wilkinson L, O'Flynn E, Allen S, Schmidt MA. Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status. Eur Radiol 2023; 33:6204-6212. [PMID: 37017702 PMCID: PMC10415521 DOI: 10.1007/s00330-023-09592-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 04/06/2023]
Abstract
OBJECTIVES To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. METHODS Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. RESULTS Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. CONCLUSIONS This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. KEY POINTS • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.
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Affiliation(s)
- Rosie Goodburn
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK.
- The Royal Marsden NHS Foundation Trust, Sutton, UK.
| | - Evanthia Kousi
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | | | | | - Erica Scurr
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Catey Bunce
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Komel Khabra
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Mamatha Reddy
- St Georges University Hospitals NHS Foundation Trust, London, UK
| | | | | | - Steven Allen
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Maria Angélica Schmidt
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
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10
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Nissan N, Massasa EEM, Bauer E, Halshtok-Neiman O, Shalmon A, Gotlieb M, Faermann R, Samoocha D, Yagil Y, Ziv-Baran T, Anaby D, Sklair-Levy M. MRI can accurately diagnose breast cancer during lactation. Eur Radiol 2023; 33:2935-2944. [PMID: 36348090 DOI: 10.1007/s00330-022-09234-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/27/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation. MATERIALS AND METHODS Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images. RESULTS For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001). CONCLUSION Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC. KEY POINTS • A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.
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Affiliation(s)
- Noam Nissan
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Efi Efraim Moss Massasa
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
| | - Ethan Bauer
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Osnat Halshtok-Neiman
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Shalmon
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michael Gotlieb
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Renata Faermann
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - David Samoocha
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yael Yagil
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tomer Ziv-Baran
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Miri Sklair-Levy
- Department of Radiology, Sheba Medical Center, Emek Ha-Ella 1 st. Tel Hashomer, 5265601, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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11
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Nissan N, Bauer E. Editorial for "4D Machine Learning Radiomics for the Prediction of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI". J Magn Reson Imaging 2023; 57:111-112. [PMID: 35652378 DOI: 10.1002/jmri.28277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 02/03/2023] Open
Affiliation(s)
- Noam Nissan
- Department of Radiology, Sheba Medical Center, Ramat Gan, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ethan Bauer
- Department of Radiology, Sheba Medical Center, Ramat Gan, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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12
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Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
- *Correspondence: Meredith A. Jones,
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
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13
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Nissan N, Bauer E, Moss Massasa EE, Sklair-Levy M. Breast MRI during pregnancy and lactation: clinical challenges and technical advances. Insights Imaging 2022; 13:71. [PMID: 35397082 PMCID: PMC8994812 DOI: 10.1186/s13244-022-01214-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022] Open
Abstract
The breast experiences substantial changes in morphology and function during pregnancy and lactation which affects its imaging properties and may reduce the visibility of a concurrent pathological process. The high incidence of benign gestational-related entities may further add complexity to the clinical and radiological evaluation of the breast during the period. Consequently, pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and carries a poor prognosis. This state-of-the-art pictorial review illustrates how despite currently being underutilized, technical advances and new clinical evidence support the use of unenhanced breast MRI during pregnancy and both unenhanced and dynamic-contrast enhanced (DCE) during lactation, to serve as effective supplementary modalities in the diagnostic work-up of PABC.
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Affiliation(s)
- Noam Nissan
- Radiology Department, Sheba Medical Center, 5265601, Tel Hashomer, Israel. .,Sackler Medicine School, Tel Aviv University, Tel Aviv, Israel.
| | - Ethan Bauer
- Sackler Medicine School, New-York Program, Tel Aviv University, Tel Aviv, Israel
| | - Efi Efraim Moss Massasa
- Joint Medicine School Program of Sheba Medical Center, St. George's, University of London and the University of Nicosia, Sheba Medical Center, Tel Hashomer, Israel
| | - Miri Sklair-Levy
- Radiology Department, Sheba Medical Center, 5265601, Tel Hashomer, Israel.,Sackler Medicine School, Tel Aviv University, Tel Aviv, Israel
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14
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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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15
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Nguyen AAT, Onishi N, Carmona-Bozo J, Li W, Kornak J, Newitt DC, Hylton NM. Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response. Tomography 2022; 8:891-904. [PMID: 35448706 PMCID: PMC9027600 DOI: 10.3390/tomography8020072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Background parenchymal enhancement (BPE) of breast fibroglandular tissue (FGT) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) has shown an association with response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Fully automated segmentation of FGT for BPE calculation is a challenge when image artifacts are present. Low spatial frequency intensity nonuniformity due to coil sensitivity variations is known as bias or inhomogeneity and can affect FGT segmentation and subsequent BPE measurement. In this study, we utilized the N4ITK algorithm for bias correction over a restricted bilateral breast volume and compared the contralateral FGT segmentations based on uncorrected and bias-corrected images in three MRI examinations at pre-treatment, early treatment and inter-regimen timepoints during NAC. A retrospective analysis of 2 cohorts was performed: one with 735 patients enrolled in the multi-center I-SPY 2 TRIAL and the sub-cohort of 340 patients meeting a high-quality benchmark for segmentation. Bias correction substantially increased the FGT segmentation quality for 6.3–8.0% of examinations, while it substantially decreased the quality for no examination. Our results showed improvement in segmentation quality and a small but statistically significant increase in the resulting BPE measurement after bias correction at all timepoints in both cohorts. Continuing studies are examining the effects on pCR prediction.
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Affiliation(s)
- Alex Anh-Tu Nguyen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Natsuko Onishi
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
- Correspondence: ; Tel.: +1-415-885-7511
| | - Julia Carmona-Bozo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA;
| | - David C. Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
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