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Hoffmann E, Masthoff M, Kunz WG, Seidensticker M, Bobe S, Gerwing M, Berdel WE, Schliemann C, Faber C, Wildgruber M. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol 2024; 21:428-448. [PMID: 38641651 DOI: 10.1038/s41571-024-00891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Bobe
- Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | | | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
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2
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Narongrit FW, Rispoli JV. Editorial for "MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning". J Magn Reson Imaging 2024; 59:162-163. [PMID: 37052873 DOI: 10.1002/jmri.28733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/14/2023] Open
Affiliation(s)
- Folk W Narongrit
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Joseph V Rispoli
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
- Purdue Institute for Cancer Research, West Lafayette, Indiana, USA
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
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3
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Liu J, Xu M, Ren J, Li Z, Xi L, Chen B. Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination. Front Oncol 2023; 12:1080580. [PMID: 36818669 PMCID: PMC9936239 DOI: 10.3389/fonc.2022.1080580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/14/2022] [Indexed: 02/05/2023] Open
Abstract
Objective To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions. Methods Clinical and MRI data of 158 patients with breast lesions who underwent dynamic contrast-enhanced MRI (DCE-MRI), syMRI, and MUSE sequences between September 2019 and December 2020 were retrospectively collected. The apparent diffusion coefficient (ADC) values of MUSE and quantitative relaxation parameters (longitudinal and transverse relaxation times [T1, T2], and proton density [PD] values) of syMRI were measured, and the parameter variation values and change in their ratios were calculated. The patients were randomly divided into training (n = 111) and validation (n = 47) groups at a ratio of 7:3. A nomogram was built based on univariate and multivariate logistic regression analyses in the training group and was verified in the validation group. The discriminatory and predictive capacities of the nomogram were assessed by the receiver operating characteristic curve and area under the curve (AUC). The AUC was compared by DeLong test. Results In the training group, univariate analysis showed that age, lesion diameter, menopausal status, ADC, T2pre, PDpre, PDGd, T2Delta, and T2ratio were significantly different between benign and malignant breast lesions (P < 0.05). Multivariate logistic regression analysis showed that ADC and T2pre were significant variables (all P < 0.05) in breast cancer diagnosis. The quantitative model (model A: ADC, T2pre), BI-RADS model (model B), and multi-parameter model (model C: ADC, T2pre, BI-RADS) were established by combining the above independent variables, among which model C had the highest diagnostic performance, with AUC of 0.965 and 0.986 in the training and validation groups, respectively. Conclusions The prediction model established based on syMRI, MUSE sequence, and BI-RADS is helpful for clinical differentiation of breast tumors and provides more accurate information for individualized diagnosis.
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Affiliation(s)
- Jinrui Liu
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Mengying Xu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE Healthcare, Beijing, China
| | - Zhihao Li
- Department of Pharmaceuticals Diagnostics, GE Healthcare, Xi’an, China
| | - Lu Xi
- Sales Department, GE Healthcare, Yinchuan, China
| | - Bing Chen
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China,*Correspondence: Bing Chen,
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Wang X, Li J, Wang X, Gao J, Jing H, Xing Y. Clinical Evaluation of High-Resolution MRI Combined With DWI in Identifying Vulnerable Carotid Plaque. Neurologist 2023; 28:5-10. [PMID: 35348493 DOI: 10.1097/nrl.0000000000000432] [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: 01/10/2023]
Abstract
BACKGROUND High-resolution magnetic resonance imaging combined with diffusion weighted imaging is used to identify vulnerable plaques (VP) and their characteristic components, and apparent diffusion coefficient (ADC) correlation analysis with serum inflammatory markers to assess plaque vulnerability. METHODS In this study, 60 eligible patients were included, including 29 patients in VP group and 31 patients in non-VP group (N group). The average ADC value, serum inflammatory marker levels (high-sensitivity C-reactive protein, myeloperoxidase, and erythrocyte sedimentation rate) of the 2 groups were measured, and the characteristics of different plaque components and ADC levels of vascular wall in VP group were compared, to evaluate the correlation between serum inflammatory markers and the mean value of plaque ADC. RESULTS The results showed that the ADC mean value of the plaques in the VP group was significantly lower than that in the N group, and the levels of hypersensitive C-reactive protein and myeloperoxidase were correlated with the ADC mean value of the plaques. CONCLUSION The ADC value of plaque measured by high-resolution magnetic resonance imaging combined with diffusion weighted imaging sequence can quantify the identification of VP and its characteristic components, reflect the inflammation of plaque to a certain extent, and thus prevent and treat stroke and other adverse outcomes more effectively.
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Affiliation(s)
- Xinyi Wang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, People's Republic of China
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5
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Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-Soto AE, Rakow-Penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022; 12:844790. [PMID: 35880168 PMCID: PMC9307963 DOI: 10.3389/fonc.2022.844790] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.
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Affiliation(s)
- Ashley M. Mendez
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Lauren K. Fang
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Claire H. Meriwether
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Stéphane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Rebecca A. Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA, United States,Department of Bioengineering, University of California San Diego, La Jolla, CA, United States,*Correspondence: Rebecca A. Rakow-Penner,
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6
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Thakran S, Gupta RK, Singh A. Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features. NMR IN BIOMEDICINE 2022; 35:e4665. [PMID: 34962326 DOI: 10.1002/nbm.4665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp-MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp-MRI features for the characterization of breast tumors (malignant vs. benign and low- vs. high-grade). This study included the breast mp-MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp-MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10-fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low- versus high-grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.
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Affiliation(s)
- Snekha Thakran
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department for Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer. Metabolites 2022; 12:metabo12050409. [PMID: 35629913 PMCID: PMC9145477 DOI: 10.3390/metabo12050409] [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: 03/25/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022] Open
Abstract
Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5−3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.
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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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Platt T, Ladd ME, Paech D. 7 Tesla and Beyond: Advanced Methods and Clinical Applications in Magnetic Resonance Imaging. Invest Radiol 2021; 56:705-725. [PMID: 34510098 PMCID: PMC8505159 DOI: 10.1097/rli.0000000000000820] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/07/2021] [Accepted: 08/07/2021] [Indexed: 12/15/2022]
Abstract
ABSTRACT Ultrahigh magnetic fields offer significantly higher signal-to-noise ratio, and several magnetic resonance applications additionally benefit from a higher contrast-to-noise ratio, with static magnetic field strengths of B0 ≥ 7 T currently being referred to as ultrahigh fields (UHFs). The advantages of UHF can be used to resolve structures more precisely or to visualize physiological/pathophysiological effects that would be difficult or even impossible to detect at lower field strengths. However, with these advantages also come challenges, such as inhomogeneities applying standard radiofrequency excitation techniques, higher energy deposition in the human body, and enhanced B0 field inhomogeneities. The advantages but also the challenges of UHF as well as promising advanced methodological developments and clinical applications that particularly benefit from UHF are discussed in this review article.
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Affiliation(s)
- Tanja Platt
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
| | - Mark E. Ladd
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
- Faculty of Physics and Astronomy
- Faculty of Medicine, University of Heidelberg, Heidelberg
- Erwin L. Hahn Institute for MRI, University of Duisburg-Essen, Essen
| | - Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg
- Clinic for Neuroradiology, University of Bonn, Bonn, Germany
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Zhu CR, Chen KY, Li P, Xia ZY, Wang B. Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis. Acta Radiol 2021; 62:1290-1297. [PMID: 33059458 DOI: 10.1177/0284185120963900] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions. PURPOSE To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions. MATERIAL AND METHODS A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve. RESULTS A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively. CONCLUSION The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.
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Affiliation(s)
- Chun-Rong Zhu
- North Sichuan Medical College, Nanchong, Sichuan, PR China
| | - Ke-Yu Chen
- North Sichuan Medical College, Nanchong, Sichuan, PR China
| | - Pan Li
- North Sichuan Medical College, Nanchong, Sichuan, PR China
| | - Zhi-Yang Xia
- North Sichuan Medical College, Nanchong, Sichuan, PR China
| | - Bin Wang
- Department of Breast and Thyroid Surgery, The Third People’s Hospital of Chengdu, Chengdu, Sichuan, PR China
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Yang ZL, Hu YQ, Huang J, Zhan CA, Zhou MX, Zhang XY, Zhang HT, Xia LM, Ai T. Detection and Classification of Breast Lesions With Readout-Segmented Diffusion-Weighted Imaging in a Large Chinese Cohort. Front Oncol 2021; 11:636471. [PMID: 33828984 PMCID: PMC8020903 DOI: 10.3389/fonc.2021.636471] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/08/2021] [Indexed: 01/22/2023] Open
Abstract
Objectives: To evaluate the performance of readout-segmented echo-planar imaging DWI (rs-EPI DWI) in detecting and characterizing breast cancers in a large Chinese cohort with comparison to dynamic contrast-enhanced MRI (DCE-MRI). Methods: The institutional review board approved this retrospective study with waived written informed consent. A total of 520 women (mean age, 43.1- ± 10.5-years) were included from July 2013 to October 2019. First, the ability of rs-EPI DWI in detecting breast lesions identified by DCE-MRI was evaluated. The lesion conspicuity of rs-EPI-DWI and DCE-MRI was compared using the Wilcoxon signed rank test. With pathology as a reference, the performance of rs-EPI DWI and DCE-MRI in distinguishing breast cancers was evaluated and compared using the Chi-square test. Results: Of 520 women, 327/520 (62.9%) patients had 423 lesions confirmed by pathology with 203 benign and 220 malignant lesions. The rs-EPI DWI can detect 90.8% (659/726) (reader 1) and 90.6% (663/732) (reader 2) of lesions identified by DCE-MRI. The lesion visibility was superior for DCE-MRI than rs-EPI-DWI (all p < 0.05). With pathology as a reference, the sensitivities and specificities of rs-EPI DWI in diagnosing breast cancers were 95.9% (211/220) and 85.7% (174/203) for reader 1 and 97.7% (215/220) and 86.2% (175/203) for reader 2. No significant differences were found for the performance of DCE-MRI and rs-EPI DWI in discriminating breast cancers (all p > 0.05). Conclusions: Although with an inferior lesion visibility, rs-EPI DWI can detect about 90% of breast lesions identified by DCE-MRI and has comparable diagnostic capacity to that of DCE-MRI in identifying breast cancer.
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Affiliation(s)
- Zhen Lu Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Qi Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jia Huang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Ao Zhan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Xiong Zhou
- College of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | | | | | - Li Ming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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12
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Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization. Cancers (Basel) 2020; 12:cancers12123763. [PMID: 33327532 PMCID: PMC7765071 DOI: 10.3390/cancers12123763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Confirming whether a breast lesion is benign or malignant usually involves an invasive tissue sample with an image-guided breast biopsy, which may cause substantial inconvenience to the patient. The purpose of this study was to investigate whether imaging biomarkers obtained from noninvasive dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can help differentiate benign from malignant lesions and characterize breast cancers to the same extent as a biopsy. In a sample of 37 patients with suspicious findings on mammography or ultrasound, we found that the radiologists’ diagnostic accuracy was improved when subjective Breast Imaging-Reporting and Data System (BI-RADS) evaluation was augmented with the use of pharmacokinetic markers. This study serves as a starting point for future collaborative research with the potential of providing valuable noninvasive tools for improved breast cancer diagnosis. Abstract The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.
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13
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A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI. Magn Reson Imaging 2020; 71:154-160. [DOI: 10.1016/j.mri.2020.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/06/2020] [Accepted: 04/12/2020] [Indexed: 12/30/2022]
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14
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Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K. Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 2019; 52:70-90. [PMID: 31520518 DOI: 10.1002/jmri.26908] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/12/2019] [Indexed: 12/30/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, New York, New York, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York, New York, USA
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Diffusion-Weighted Imaging With Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter: Comparison With Dynamic Contrast-Enhanced and Multiparametric Magnetic Resonance Imaging. Invest Radiol 2019; 53:587-595. [PMID: 29620604 DOI: 10.1097/rli.0000000000000465] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast. MATERIALS AND METHODS In this institutional review board-approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values. RESULTS There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P < 0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions. CONCLUSION Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.
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Role of diffusion weighted imaging and magnetic resonance spectroscopy in breast cancer patients with indeterminate dynamic contrast enhanced magnetic resonance imaging findings. Magn Reson Imaging 2019; 61:66-72. [PMID: 31128225 DOI: 10.1016/j.mri.2019.05.032] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 05/20/2019] [Accepted: 05/21/2019] [Indexed: 11/21/2022]
Abstract
PURPOSE Dynamic contrast enhanced MRI (DCEMRI), diffusion weighted imaging (DWI) and in vivo proton (1H) magnetic resonance spectroscopy (MRS) provides functional and molecular nature of breast cancer. This study evaluates the potential of the combination of three MR parameters [curve kinetics, apparent diffusion coefficient (ADC) and total choline (tCho) concentration] determined from these techniques in increasing the sensitivity of breast cancer detection. METHODS MR investigations were carried out at 1.5 T on 56 patients with cytologically/histologically confirmed breast carcinoma. Single-voxel MRS was used to determine the tCho concentration. 3D FLASH was used for DCEMRI while single shot EPI based DWI was used for ADC determination. RESULTS On DCEMRI, one patient showed type I curve, while 8 showed type II and 47 showed type III curve thus giving a sensitivity of 83.9% as detection rate of malignancy. tCho concentration was above cut-off value (2.54 mmol/kg) for 50/56 cases giving a sensitivity of 89.3%. Among 9 indeterminate DCEMRI cases, tCho showed malignancy in 6 cases with type II curve. DWI detected malignancy in 54/56 cases that included 9 cases that were false negative on DCEMRI, yielding a sensitivity of 96.4%. A total of 54 cases showed malignancy when any two of the three MR parameters was positive for malignancy yielding a sensitivity of 96.4% while it increased to 100% when any one parameters showed positive result. CONCLUSION DWI showed highest sensitivity of detection compared to DCEMRI and MRS. Multi-parametric approach yielded 96.4% and 100% sensitivity when any two or one of the three parameters was taken as positive for malignancy, respectively. Also the results demonstrated that addition of DWI and MRS play a significant role in establishing the final diagnosis of malignancy, especially in cases where DCEMRI is indeterminate.
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Potential of Noncontrast Magnetic Resonance Imaging With Diffusion-Weighted Imaging in Characterization of Breast Lesions: Intraindividual Comparison With Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Invest Radiol 2019; 53:229-235. [PMID: 29190227 DOI: 10.1097/rli.0000000000000433] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVES The aim of this study was to assess the potential of noncontrast magnetic resonance imaging (NC-MRI) with diffusion-weighted imaging (DWI) in characterization of breast lesions in comparison to dynamic contrast-enhanced MRI (DCE-MRI) at 3 T. MATERIALS AND METHODS Consecutive patients with conventional imaging (mammography, ultrasound) BI-RADS 4/5 findings were included in this institutional review board-approved single-center study. All underwent 3 T breast MRI including readout-segmented DWI, DCE, and T2-weighted sequences. Final diagnosis was defined by histopathology or follow-up (>24 months). Two experienced radiologists (R1, R2) independently assigned lesion conspicuity (0 = minimal to 3 = excellent) and BI-RADS scores to NC-MRI (readout-segmented DWI including apparent diffusion coefficient maps) and DCE-MRI (DCE and T2-weighted). Receiver operating characteristics, κ statistics, and visual grading characteristics analysis were applied. RESULTS Sixty-seven malignant and 56 benign lesions were identified in 113 patients (mean age, 54 ± 14 years). Areas under the receiver operating characteristics curves were similar: DCE-MRI: 0.901 (R1), 0.905 (R2); NC-MRI: 0.882 (R1), 0.854 (R2); P > 0.05, respectively. The κ agreement was 0.968 (DCE-MRI) and 0.893 (NC-MRI). Visual grading characteristics analysis revealed superior lesion conspicuity by DCE-MRI (0.661, P < 0.001). CONCLUSIONS Diagnostic performance and interreader agreement of both NC-MRI and DCE-MRI is high, indicating a potential use of NC-MRI as an alternative to DCE-MRI. However, inferior lesion conspicuity and lower interreader agreement of NC-MRI need to be considered.
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Zhang M, Horvat JV, Bernard-Davila B, Marino MA, Leithner D, Ochoa-Albiztegui RE, Helbich TH, Morris EA, Thakur S, Pinker K. Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy. J Magn Reson Imaging 2018; 49:864-874. [PMID: 30375702 PMCID: PMC6375760 DOI: 10.1002/jmri.26285] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 11/24/2022] Open
Abstract
Background The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T2‐weighted and dynamic contrast‐enhanced (DCE) MRI sequences. The addition of diffusion‐weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE‐MRI, DWI, and T2‐weighted imaging are most strongly associated with a breast cancer diagnosis. Purpose/Hypothesis To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI‐RADS recommended descriptors for breast MRI with DCE, T2‐weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping. Study Type Retrospective. Subjects In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014. Field Strength/Sequence IR inversion recovert DCE‐MRI dynamic contrast‐enhanced magnetic resonance imaging VIBE Volume‐Interpolated‐Breathhold‐Examination FLASH turbo fast‐low‐angle‐shot TWIST Time‐resolved angiography with stochastic Trajectories. Assessment Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T2‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10−3 mm2/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference. Statistical Tests χ2 test, Fisher's exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer–Lemeshow test of goodness‐of‐fit, receiver operating characteristics analysis. Results In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2‐weighted imaging variables were not included in the final models. Data Conclusion mpMRI with DCE‐MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE‐MRI and DWI identifies breast cancer with a high diagnostic accuracy. T2‐weighted imaging does not significantly contribute to breast cancer diagnosis. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:864–874.
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Affiliation(s)
- Michelle Zhang
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Joao V Horvat
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Blanca Bernard-Davila
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Maria Adele Marino
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA.,Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria
| | - Doris Leithner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA.,University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Frankfurt, Germany
| | - R Elena Ochoa-Albiztegui
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Thomas H Helbich
- Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Elizabeth A Morris
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Sunitha Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, NY, New York, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
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Leithner D, Wengert GJ, Helbich TH, Thakur S, Ochoa-Albiztegui RE, Morris EA, Pinker K. Clinical role of breast MRI now and going forward. Clin Radiol 2018; 73:700-714. [PMID: 29229179 PMCID: PMC6788454 DOI: 10.1016/j.crad.2017.10.021] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 10/31/2017] [Indexed: 02/08/2023]
Abstract
Magnetic resonance imaging (MRI) is a well-established method in breast imaging, with manifold clinical applications, including the non-invasive differentiation between benign and malignant breast lesions, preoperative staging, detection of scar versus recurrence, implant assessment, and the evaluation of high-risk patients. At present, dynamic contrast-enhanced MRI is the most sensitive imaging technique for breast cancer diagnosis, and provides excellent morphological and to some extent also functional information. To compensate for the limited functional information, and to increase the specificity of MRI while preserving its sensitivity, additional functional parameters such as diffusion-weighted imaging and apparent diffusion coefficient mapping, and MR spectroscopic imaging have been investigated and implemented into the clinical routine. Several additional MRI parameters to capture breast cancer biology are still under investigation. MRI at high and ultra-high field strength and advances in hard- and software may also further improve this imaging technique. This article will review the current clinical role of breast MRI, including multiparametric MRI and abbreviated protocols, and provide an outlook on the future of this technique. In addition, the predictive and prognostic value of MRI as well as the evolving field of radiogenomics will be discussed.
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Affiliation(s)
- D Leithner
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Frankfurt, Germany; Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - G J Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - T H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - S Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - R E Ochoa-Albiztegui
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - E A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - K Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) of the breast is an indispensable tool in breast imaging for many indications. Several functional parameters with MRI and positron emission tomography (PET) have been assessed for imaging of breast tumors and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity. STANDARD RADIOLOGICAL METHODS Multiparametric and molecular imaging of the breast comprises established MRI parameters, such as dynamic contrast-enhanced MRI, diffusion-weighted imaging (DWI), MR proton spectroscopy ((1)H-MRSI) as well as combinations of radiological and MRI techniques (e. g. PET/CT and PET/MRI) using radiotracers, such as fluorodeoxyglucose (FDG). METHODICAL INNOVATIONS Multiparametric and molecular imaging of the breast can be performed at different field-strengths (range 1.5-7 T). Emerging parameters comprise novel promising techniques, such as sodium imaging ((23)Na MRI), phosphorus spectroscopy ((31)P-MRSI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level-dependent (BOLD) and hyperpolarized MRI as well as various specific radiotracers. ACHIEVEMENTS Multiparametric and molecular imaging has multiple applications in breast imaging. Multiparametric and molecular imaging of the breast is an evolving field that will enable improved detection, characterization, staging and monitoring for personalized medicine in breast cancer.
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Liu HL, Zong M, Wei H, Lou JJ, Wang SQ, Zou QG, Shi HB, Jiang YN. Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level. Br J Radiol 2017; 90:20170394. [PMID: 28876982 DOI: 10.1259/bjr.20170394] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy. METHODS This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values. RESULTS The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC10 were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC10 < 1.047 ×10-3 mm2 s-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%). CONCLUSION Irregular morphology, TIC Type II/III and ADC10 are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC10 as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC10 and ADCmean was statistically significant. The irregular morphology, TIC Type II/III and ADC10 were significant predictors for malignant lesions.
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Affiliation(s)
- Hong-Li Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Han Wei
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jian-Juan Lou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Si-Qi Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qi-Gui Zou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yan-Ni Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Marino MA, Helbich T, Baltzer P, Pinker-Domenig K. Multiparametric MRI of the breast: A review. J Magn Reson Imaging 2017. [DOI: 10.1002/jmri.25790] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino; University of Messina; Messina Italy
| | - Thomas Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
| | - Katja Pinker-Domenig
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging; Medical University of Vienna; Austria
- Department of Radiology; Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center; New York New York USA
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DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic. Top Magn Reson Imaging 2017; 25:245-254. [PMID: 27748710 PMCID: PMC5081190 DOI: 10.1097/rmr.0000000000000103] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.
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Kraff O, Quick HH. 7T: Physics, safety, and potential clinical applications. J Magn Reson Imaging 2017; 46:1573-1589. [DOI: 10.1002/jmri.25723] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 03/17/2017] [Indexed: 12/19/2022] Open
Affiliation(s)
- Oliver Kraff
- Erwin L. Hahn Institute for MR Imaging; University of Duisburg-Essen; Essen Germany
| | - Harald H. Quick
- Erwin L. Hahn Institute for MR Imaging; University of Duisburg-Essen; Essen Germany
- High Field and Hybrid MR Imaging; University Hospital Essen; Essen Germany
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Dramatic increase in the utilization of multiparametric magnetic resonance imaging for detection and management of prostate cancer. Abdom Radiol (NY) 2017; 42:1255-1258. [PMID: 27858090 DOI: 10.1007/s00261-016-0975-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
PURPOSE Multiparametric MRI (mpMRI) of the prostate is an evolving technology that provides functional information of the prostate that helps distinguish benign from malignant lesions. We hypothesized that mpMRI is rapidly adopted in the US to fill the unmet need for a non-invasive, accurate screening tool. The aim of this study is to assess the increasing utilization of mpMRI for the diagnosis and management of prostate cancer. METHODS We conducted a retrospective review of an institutional clinical data repository of four million patients. Clinical information from all men undergoing mpMRI from October 2013 to December 2015 was collected in a prospectively designed database. Individual chart reviews were performed for each patient. RESULTS 1521 mpMRI of the prostate were performed with an increase in the use of 486% over 26 months. The most common indication for mpMRI was abnormal screening (64%) and 47% of these men went on to prostate biopsy, either by cognitive mapping (65%) or MRI-US fusion targeting (35%). 261 men elected to defer prostate needle biopsy after informative decision-making with their urologist. 12.7% of mpMRI were performed for active surveillance, 7.5% for clinical staging, and 3.2% by radiation oncologists planning radiotherapy. 7% of mpMRI were performed to evaluate the pelvis for biochemical recurrence, a third of which identified a region of suspicion for targeted. CONCLUSION Prostate mpMRI is increasingly performed for both the diagnosis and management of prostate cancer. As clinical utilization increases along with the diffusion of technology and radiologic expertise, MpMRI has the potential to influence clinical decision-making and fulfill the need for a non-invasive, accurate tool for the diagnosis and management of prostate cancer.
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Spick C, Bickel H, Pinker K, Bernathova M, Kapetas P, Woitek R, Clauser P, Polanec SH, Rudas M, Bartsch R, Helbich TH, Baltzer PA. Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy. NMR IN BIOMEDICINE 2016; 29:1445-1453. [PMID: 27553252 DOI: 10.1002/nbm.3596] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/08/2016] [Accepted: 07/08/2016] [Indexed: 06/06/2023]
Abstract
Diffusion-weighted MRI (DWI) provides insights into tissue microstructure by visualization and quantification of water diffusivity. Quantitative evaluation of the apparent diffusion coefficient (ADC) obtained from DWI has been proven helpful for differentiating between malignant and benign breast lesions, for cancer subtyping in breast cancer patients, and for prediction of response to neoadjuvant chemotherapy. However, to further establish DWI of breast lesions it is important to evaluate the quantitative imaging biomarker (QIB) characteristics of reproducibility, repeatability, and diagnostic accuracy. In this intra-individual prospective clinical study 40 consecutive patients with suspicious findings, scheduled for biopsy, underwent an identical 3T breast MRI protocol of the breast on two consecutive days (>24 h). Mean ADC of target lesions was assessed (two independent readers) in four separate sessions. Reproducibility, repeatability, and diagnostic accuracy between examinations (E1, E2), readers (R1, R2), and measurements (M1, M2) were assessed with intraclass correlation coefficients (ICCs), coefficients of variation (CVs), Bland-Altman plots, and receiver operating characteristic (ROC) analysis with calculation of the area under the ROC curve (AUC). The standard of reference was either histopathology (n = 38) or imaging follow-up of up to 24 months (n = 2). Eighty breast MRI examinations (median E1-E2, 2 ± 1.7 days, 95% confidence interval (CI) 1-2 days, range 1-11 days) in 40 patients (mean age 56, standard deviation (SD) ±14) were evaluated. In 55 target lesions (mean size 25.2 ± 20.8 (SD) mm, range 6-106 mm), mean ADC values were significantly (P < 0.0001) higher in benign (1.38, 95% CI 1.27-1.49 × 10(-3) mm(2) /s) compared with malignant (0.86, 95% CI 0.81-0.91 × 10(-) (3) mm(2) /s) lesions. Reproducibility and repeatability showed high agreement for repeated examinations, readers, and measurements (all ICCs >0.9, CVs 3.2-8%), indicating little variation. Bland-Altman plots demonstrated no systematic differences, and diagnostic accuracy was not significantly different in the two repeated examinations (all ROC curves >0.91, P > 0.05). High reproducibility, repeatability, and diagnostic accuracy of DWI provide reliable characteristics for its use as a potential QIB, to further improve breast lesion detection, characterization, and treatment monitoring of breast lesions.
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Affiliation(s)
- Claudio Spick
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Hubert Bickel
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Ramona Woitek
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Stephan H Polanec
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Margaretha Rudas
- Clinical Institute of Pathology, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Rupert Bartsch
- Department of Internal Medicine, Division of Oncology, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria
| | - Pascal A Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, A-1090, Vienna, Austria.
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Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2016; 45:337-355. [PMID: 27690173 DOI: 10.1002/jmri.25479] [Citation(s) in RCA: 215] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 08/29/2016] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.
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Affiliation(s)
- Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Noam Nissan
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Habib Rahbar
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Averi E Kitsch
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Eric E Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Abstract
Breast MR imaging has increased in popularity over the past 2 decades due to evidence of its high sensitivity for cancer detection. Current clinical MR imaging approaches rely on the use of a dynamic contrast-enhanced acquisition that facilitates morphologic and semiquantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters holds promise to broaden the utility of MR imaging and improve its specificity. Because of wide variations in approaches for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use are not yet available, limiting current applications of many of these tools to research purposes.
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Affiliation(s)
- Habib Rahbar
- Breast Imaging Section, Department of Radiology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Avenue East, PO Box 19023, Seattle, WA 98109-1023, USA
| | - Savannah C Partridge
- Breast Imaging Section, Department of Radiology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Avenue East, PO Box 19023, Seattle, WA 98109-1023, USA.
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Teruel JR, Cho GY, Moccaldi Rt M, Goa PE, Bathen TF, Feiweier T, Kim SG, Moy L, Sigmund EE. Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue. J Magn Reson Imaging 2016; 45:84-93. [PMID: 27441890 DOI: 10.1002/jmri.25376] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 06/21/2016] [Indexed: 11/06/2022] Open
Abstract
PURPOSE To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated-echo acquisition mode (STEAM) with variable diffusion times. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act-compliant study, approved by the local institutional review board, eight patients and six healthy volunteers underwent an MRI examination at 3 Tesla including STEAM-DTI with several diffusion times ranging from 68.5 to 902.5 ms. A DTI model was fitted to the data for each diffusion time, and parametric maps of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for healthy fibroglandular tissue (FGT) and lesions. The median value of radial diffusivity for FGT was fitted to a linear decay to obtain an estimation of the surface-to-volume ratio, from which the radial diameter was calculated. RESULTS For healthy FGT, radial diffusivity presented a linear decay with the square root of the diffusion time resulting in a range of estimated radial diameters from 202 to 496 µm, while axial diffusivity presented a nearly time-independent diffusion. Residual fat signal was reduced at longer diffusion times due to the shorter T1 of fat. Residual fat signal to the overall signal in the healthy volunteers' FGT was found to range from 2.39% to 2.55% (shortest mixing time), and from 0.40% to 0.51% (longest mixing time) for the b500 images. CONCLUSION The use of variable diffusion times may provide an in vivo noninvasive tool to probe diffusion lengths in breast tissue and breast pathology, and might aid by improving fat suppression at longer diffusion times. LEVEL OF EVIDENCE 2 J. Magn. Reson. Imaging 2017;45:84-93.
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Affiliation(s)
- Jose R Teruel
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Radiology, University of California San Diego (UCSD), La Jolla, California, USA
| | - Gene Y Cho
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Melanie Moccaldi Rt
- Cancer Institute, New York University School of Medicine, New York, New York, USA
| | - Pål E Goa
- Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Sungheon G Kim
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Eric E Sigmund
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Hancu I, Lee SK, Hulsey K, Lenkinski R, Holland D, Sperl JI, Tan ET. Distortion correction in diffusion-weighted imaging of the breast: Performance assessment of prospective, retrospective, and combined (prospective + retrospective) approaches. Magn Reson Med 2016; 78:247-253. [PMID: 27403765 DOI: 10.1002/mrm.26328] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 05/27/2016] [Accepted: 06/10/2016] [Indexed: 01/11/2023]
Abstract
PURPOSE To compare the effectiveness of prospective, retrospective, and combined (prospective + retrospective) EPI distortion correction methods in bilateral breast diffusion-weighted imaging (DWI) scans. METHODS Five healthy female subjects underwent an axial bilateral breast DWI exam with and without prospective B0 inhomogeneity correction using slice-by-slice linear shimming. In each case, an additional b=0 DWI scan was performed with the polarity of the phase-encoding gradient reversed, to generate an estimated B0 map; this map or a separately acquired B0 map was used for retrospective correction, either alone or in combination with the prospective correction. The alignment between an undistorted, anatomical reference scan with similar contrast and the corrected b=0 DWI images with different correction schemes was assessed. RESULTS The average cross-correlation coefficient between the DWI images and the anatomical reference scan was increased from 0.82 to 0.92 over the five volunteers when combined prospective and retrospective distortion correction was applied. Furthermore, such correction substantially reduced patient-to-patient variation of the image alignment and the variability of the average apparent diffusion coefficient in normal glandular tissue. CONCLUSION Combined prospective and retrospective distortion correction can provide an efficient way to reduce susceptibility-induced image distortions and enhance the reliability of breast DWI exams. Magn Reson Med 78:247-253, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Seung-Kyun Lee
- CNIR, IBS and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Keith Hulsey
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Robert Lenkinski
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | | | - Ek T Tan
- GE Global Research Center, Niskayuna, NY, USA
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Magnetic Resonance Imaging: Advanced Applications in Breast Cancer. CURRENT RADIOLOGY REPORTS 2016. [DOI: 10.1007/s40134-016-0142-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Aribal E, Asadov R, Ramazan A, Ugurlu MÜ, Kaya H. Multiparametric breast MRI with 3T: Effectivity of combination of contrast enhanced MRI, DWI and 1H single voxel spectroscopy in differentiation of Breast tumors. Eur J Radiol 2016; 85:979-86. [PMID: 27130059 DOI: 10.1016/j.ejrad.2016.02.022] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 02/20/2016] [Accepted: 02/22/2016] [Indexed: 01/09/2023]
Abstract
OBJECTIVES To evaluate the diagnostic accuracy of dynamic contrast enhanced breast MRI (DCE-MRI) combined with diffusion weighted imaging (DWI) and 1H single-voxel magnetic resonance spectroscopy (1HMRS) in differentiating malignant from benign breast lesions. METHODS One hundred twenty-nine patients with 138 lesions were included in the study. Multiparametric MRI of the breast was performed with a 3T unit. A DWI is followed by DCE-MRI and 1HMRS. All lesions were biopsied within one week after MRI. Histopathologic findings were accepted as the standard of reference. Probability of malignancy was assessed according to BI-RADS for DCE-MRI. ADC values were measured for DWI and choline peaks were assessed using a semi-quantitative method in 1HMRS. Two blinded radiologists evaluated findings in consensus. Diagnostic performance of DCE-MRI, DWI and 1HMRS alone or in combination for multiparametric imaging were statistically evaluated. RESULTS Histopathology revealed malignancy in 54.4% of lesions (75/138). DCE-MRI showed the highest AUC (0.978), sensitivity (97.33%) and specificity (88.89%) compared to DWI and 1HMRS. Sensitivity was 100% when a positive result from any one of three techniques was accepted as malignancy, albeit with a trade-off for 65.1% specificity. Highest specificity (98.4%) was attained when all three techniques were required to be positive, though with a lower sensitivity (82.7%) as trade-off. Logistic regression analysis confirmed significant association with DCE-MRI (p<0.001) and 1H MRS (p=0.009) but not with DWI (p=0.127). There was one case of fat necrosis which was false positive in all three techniques. CONCLUSIONS Multiparametric imaging with combination of DCE-MRI, DWI and 1HMRS does not improve, and may even reduce the diagnostic accuracy of breast MRI. Although, the specificity may be improved with a trade-off for lower sensitivity, we have not set a convenient algorithm for the combined use of these techniques.
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Affiliation(s)
- Erkin Aribal
- Marmara University, Faculty of Medicine, Department of Radiology, Mimar Sinan cad 41, Pendik, 34890 Istanbul, Turkey.
| | - Ruslan Asadov
- Marmara University, Faculty of Medicine, Department of Radiology, Mimar Sinan cad 41, Pendik, 34890 Istanbul, Turkey.
| | - Abdullah Ramazan
- TC SB Marmara University Pendik Training and Research Hospital, Radiology Department, Mimar Sinan cad 41, Pendik, 34890 Istanbul, Turkey.
| | - Mustafa Ümit Ugurlu
- Marmara University, Faculty of Medicine, Department of General Surgery, Mimar Sinan cad 41, Pendik, 34890 Istanbul, Turkey.
| | - Handan Kaya
- Marmara University, Faculty of Medicine, Department of Pathology, Mimar Sinan cad 41, Pendik, 34890 Istanbul, Turkey.
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Zaric O, Pinker K, Zbyn S, Strasser B, Robinson S, Minarikova L, Gruber S, Farr A, Singer C, Helbich TH, Trattnig S, Bogner W. Quantitative Sodium MR Imaging at 7 T: Initial Results and Comparison with Diffusion-weighted Imaging in Patients with Breast Tumors. Radiology 2016; 280:39-48. [PMID: 27007803 DOI: 10.1148/radiol.2016151304] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To investigate the clinical feasibility of a quantitative sodium 23 ((23)Na) magnetic resonance (MR) imaging protocol developed for breast tumor assessment and to compare it with 7-T diffusion-weighted imaging (DWI). Materials and Methods Written informed consent in this institutional review board-approved study was obtained from eight healthy volunteers and 17 patients with 20 breast tumors (five benign, 15 malignant). To achieve the best image quality and reproducibility, the (23)Na sequence was optimized and tested on phantoms and healthy volunteers. For in vivo quantification of absolute tissue sodium concentration (TSC), an external phantom was used. Static magnetic field, or B0, and combined transmit and receive radiofrequency field, or B1, maps were acquired, and image quality, measurement reproducibility, and accuracy testing were performed. Bilateral (23)Na and DWI sequences were performed before contrast material-enhanced MR imaging in patients with breast tumors. TSC and apparent diffusion coefficient (ADC) were calculated and correlated for healthy glandular tissue and benign and malignant lesions. Results The (23)Na MR imaging protocol is feasible, with 1.5-mm in-plane resolution and 16-minute imaging time. Good image quality was achieved, with high reproducibility (mean TSC values ± standard deviation for the test, 36 mmol per kilogram of wet weight ± 2 [range, 34-37 mmol/kg]; for the retest, 37 mmol/kg ± 1 [range, 35-39 mmol/kg]; P = .610) and accuracy (r = 0.998, P < .001). TSC values in normal glandular and adipose breast tissue were 35 mmol/kg ± 3 and 18 mmol/kg ± 3, respectively. In malignant lesions (mean size, 31 mm ± 24; range, 6-92 mm), the TSC of 69 mmol/kg ± 10 was, on average, 49% higher than that in benign lesions (mean size, 14 mm ± 12; range, 6-35 mm), with a TSC of 47 mmol/kg ± 8 (P = .002). There were similar ADC differences between benign ([1.78 ± 0.23] × 10(-3) mm(2)/sec) and malignant ([1.03 ± 0.23] × 10(-3) mm(2)/sec) tumors (P = .002). ADC and TSC were inversely correlated (r = -0.881, P < .001). Conclusion Quantitative (23)Na MR imaging is clinically feasible, may provide good differentiation between malignant and benign breast lesions, and demonstrates an inverse correlation with ADC. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Olgica Zaric
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Katja Pinker
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Stefan Zbyn
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Bernhard Strasser
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Simon Robinson
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Lenka Minarikova
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Stephan Gruber
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Alex Farr
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Christian Singer
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Thomas H Helbich
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Siegfried Trattnig
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
| | - Wolfgang Bogner
- From the MR Center of Excellence (MRCE), Department of Biomedical Imaging and Image-guided Therapy (O.Z., S.Z., B.S., S.R., L.M., S.G., S.T., W.B.), Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy (K.P., T.H.H.), and Department of Obstetrics and Gynecology (A.F., C.S.), Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; and Christian Doppler Laboratory for Clinical Molecular MRI, Christian Doppler Forschungsgesellschaft, Vienna, Austria (S.T.)
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Teruel JR, Goa PE, Sjøbakk TE, Østlie A, Fjøsne HE, Bathen TF. Diffusion weighted imaging for the differentiation of breast tumors: From apparent diffusion coefficient to high order diffusion tensor imaging. J Magn Reson Imaging 2015; 43:1111-21. [DOI: 10.1002/jmri.25067] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 09/21/2015] [Accepted: 09/22/2015] [Indexed: 11/06/2022] Open
Affiliation(s)
- Jose R. Teruel
- Department of Circulation and Medical Imaging; Norwegian University of Science and Technology (NTNU); Trondheim Norway
- St. Olavs University Hospital; Trondheim Norway
| | - Pål E. Goa
- Department of Physics; Norwegian University of Science and Technology (NTNU); Trondheim Norway
| | - Torill E. Sjøbakk
- Department of Circulation and Medical Imaging; Norwegian University of Science and Technology (NTNU); Trondheim Norway
| | - Agnes Østlie
- Clinic of Radiology and Nuclear Medicine; St. Olavs University Hospital; Trondheim Norway
| | - Hans E. Fjøsne
- Department of Cancer Research and Molecular Medicine; Norwegian University of Science and Technology (NTNU); Trondheim Norway
- Department of Surgery; St. Olavs University Hospital; Trondheim Norway
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging; Norwegian University of Science and Technology (NTNU); Trondheim Norway
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