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Wielema M, Sijens PE, Pijnappel RM, De Bock GH, Zorgdrager M, Kok MGJ, Rainer E, Varga R, Clauser P, Oudkerk M, Dorrius MD, Baltzer PAT. Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening. Eur Radiol 2024; 34:4730-4737. [PMID: 38008743 PMCID: PMC11213722 DOI: 10.1007/s00330-023-10321-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 11/28/2023]
Abstract
OBJECTIVES To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. METHODS Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). RESULTS Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). CONCLUSIONS Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. CLINICAL RELEVANCE STATEMENT Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. KEY POINTS • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.
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Affiliation(s)
- Mirjam Wielema
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Paul E Sijens
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, Utrecht University, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geertruida H De Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marcel Zorgdrager
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marius G J Kok
- Department of Radiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Eva Rainer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Raoul Varga
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Monique D Dorrius
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
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Pötsch N, Sodano C, Baltzer PAT. Performance of Diffusion-weighted Imaging-based Noncontrast MRI Protocols for Diagnosis of Breast Cancer: A Systematic Review and Meta-Analysis. Radiology 2024; 311:e232508. [PMID: 38771179 DOI: 10.1148/radiol.232508] [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: 05/22/2024]
Abstract
Background Diffusion-weighted imaging (DWI) is increasingly recognized as a powerful diagnostic tool and tested alternative to contrast-enhanced (CE) breast MRI. Purpose To perform a systematic review and meta-analysis that assesses the diagnostic performance of DWI-based noncontrast MRI protocols (ncDWI) for the diagnosis of breast cancer. Materials and Methods A systematic literature search in PubMed for articles published from January 1985 to September 2023 was performed. Studies were excluded if they investigated malignant lesions or selected patients and/or lesions only, used DWI as an adjunct technique to CE MRI, or were technical studies. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. Additional subgroup comparisons of ncDWI to CE MRI and standard mammography were performed. Results A total of 28 studies were included, with 4406 lesions (1676 malignant, 2730 benign) in 3787 patients. The pooled sensitivity and specificity of ncDWI were 86.5% (95% CI: 81.4, 90.4) and 83.5% (95% CI: 76.9, 88.6), and both measures presented with high between-study heterogeneity (I 2 = 81.6% and 91.6%, respectively; P < .001). CE MRI (18 studies) had higher sensitivity than ncDWI (95.1% [95% CI: 92.9, 96.7] vs 88.9% [95% CI: 82.4, 93.1], P = .004) at similar specificity (82.2% [95% CI: 75.0, 87.7] vs 82.0% [95% CI: 74.8, 87.5], P = .97). Compared with ncDWI, mammography (five studies) showed no evidence of a statistical difference for sensitivity (80.3% [95% CI: 56.3, 93.3] vs 56.7%; [95% CI: 41.9, 70.4], respectively; P = .09) or specificity (89.9% [95% CI: 85.5, 93.1] vs 90% [95% CI: 61.3, 98.1], respectively; P = .62), but ncDWI had a higher area under the summary receiver operating characteristic curve (0.93 [95% CI: 0.91, 0.95] vs 0.78 [95% CI: 0.74, 0.81], P < .001). Conclusion A direct comparison with CE MRI showed a modestly lower sensitivity at similar specificity for ncDWI, and higher diagnostic performance indexes for ncDWI than standard mammography. Heterogeneity was high, thus these results must be interpreted with caution. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kataoka and Iima in this issue.
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Affiliation(s)
- Nina Pötsch
- From the Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Claudia Sodano
- From the Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Pascal A T Baltzer
- From the Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090 Vienna, Austria
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Gullo RL, Partridge SC, Shin HJ, Thakur SB, Pinker K. Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring. AJR Am J Roentgenol 2024; 222:e2329933. [PMID: 37850579 PMCID: PMC11196747 DOI: 10.2214/ajr.23.29933] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA, USA 98109, USA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Hu Y, Hu Q, Zhan C, Yin T, Ai T. Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging. J Magn Reson Imaging 2023; 58:1725-1736. [PMID: 36807457 DOI: 10.1002/jmri.28655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND The recommended technique for breast diffusion-weighted imaging (DWI) acquisitions is not sufficiently standardized in clinical practice. PURPOSE To investigate the intraobserver and interobserver reproducibility of DWI measurements, diffusion-kurtosis imaging (DKI) parameters, and image quality evaluation in breast lesions between single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI). STUDY TYPE Prospective. POPULATION A total of 295 women with 209 malignant and 86 benign breast lesions. FIELD STRENGTH/SEQUENCE A 3-T; fat-saturated T2-weighted MR imaging (T2WI); multi-b-value DWI with both ss-EPI and rs-EPI readouts; T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). ASSESSMENT Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were measured for each lesion on ss-EPI and rs-EPI, respectively. Image quality was visually evaluated regarding image sharpness, geometric distortion, lesion conspicuity, visualization of anatomic structures, and overall quality. Quantitative and qualitative analyses were performed twice with a time interval of 2 weeks. STATISTICAL TESTS Intraobserver and interobserver reproducibility were evaluated using intra-class correlation coefficients (ICC), within-subject coefficient of variation (wCV), and Bland-Altman plots. RESULTS MK, MD, and ADC quantitative parameters for breast lesions showed excellent intraobserver and interobserver reproducibility, with ICCs >0.75 and wCV values ranging from 2.51% to 7.08% for both sequences. The wCV values in both intraobserver and interobserver measurements were higher in the ss-EPI sequence (3.63%-7.08%) than that of the rs-EPI sequence (2.51%-3.62%). The wCV values differed in subgroups with different histopathological types of lesions, breast density, lesion morphology, and lesion sizes, respectively. Furthermore, rs-EPI (ICCs, 0.76-0.97; wCV values, 2.41%-6.04%) had better intraobserver and interobserver reproducibility than ss-EPI (ICCs, 0.54-0.90; wCV values, 6.18%-13.69%) with regard to image quality. DATA CONCLUSION Compared to the ss-EPI, the rs-EPI sequence showed higher intraobserver and interobserver reproducibility for quantitative diffusion-related parameters and image quality assessments measured in breast DWI and DKI. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yiqi Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chenao Zhan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Shanghai, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Luo HC, Cheng WQ, Ding H, He L. Diagnostic performance of diffusion-weighted imaging and intravoxel incoherent motion for renal lesions: a meta-analysis. Clin Radiol 2023; 78:935-946. [PMID: 37652795 DOI: 10.1016/j.crad.2023.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 07/06/2023] [Accepted: 07/30/2023] [Indexed: 09/02/2023]
Abstract
AIM To compare the diagnostic performance of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) parameters, specifically true diffusion coefficient (D), pseudo diffusion coefficient (D∗), and perfusion fraction (f) for quantitatively differentiating benign and malignant renal lesions. MATERIALS AND METHODS A comprehensive search was conducted in the EMBASE and PubMed databases before September 2022 to identify studies in English investigating the diagnostic accuracy of DWI and IVIM in renal lesions. The quality of the included studies was assessed using the QUADAS-2 tool. Pooled sensitivity, specificity, and area under the curve (AUC) values were estimated for each parameter. RESULTS A total of 19 studies involving 1,860 renal lesions (1,160 malignant and 700 benign), met the inclusion criteria. Among these studies, 15 assessed the apparent diffusion coefficient (ADC), four assessed IVIM, and three evaluated both ADC and IVIM. The pooled sensitivity, specificity, and AUC for ADC were 0.84 (95% confidence interval [Cl], 0.79-0.88), 0.82 (95% Cl, 0.72-0.89), and 0.89 (95% Cl, 0.86-0.92), respectively. The IVIM parameter with the highest diagnostic accuracy was D, with a pooled sensitivity, specificity, and AUC of 0.89 (95% Cl, 0.74-0.96), 0.96 (95% Cl, 0.85-0.99), and 0.98 (95% Cl, 0.96-0.99), respectively. The pooled sensitivity, specificity and AUC for f were 0.67 (95% Cl, 0.55-0.77), 0.81 (95% Cl, 0.30-0.98), and 0.73 (95% Cl, 0.69-0.77), respectively. The pooled sensitivity, specificity, and AUC for D∗ were 0.87 (95% Cl, 0.81-0.91), 0.59 (95% Cl, 0.48-0.70), and 0.82 (95% Cl, 0.78-0.85), respectively. CONCLUSION This meta-analysis indicated that both IVIM and DWI had moderate to high diagnostic accuracy for differentiating benign and malignant renal lesions. Among the IVIM parameter, D exhibited the highest diagnostic accuracy, demonstrating higher sensitivity and specificity than ADC, D∗, and f.
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Affiliation(s)
- H C Luo
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - W Q Cheng
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - H Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - L He
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China.
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van der Hoogt KJJ, Schipper RJ, Wessels R, Ter Beek LC, Beets-Tan RGH, Mann RM. Breast DWI Analyzed Before and After Gadolinium Contrast Administration-An Intrapatient Analysis on 1.5 T and 3.0 T. Invest Radiol 2023; 58:832-841. [PMID: 37389456 DOI: 10.1097/rli.0000000000000999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
OBJECTIVES Diffusion-weighted magnetic resonance imaging (MRI) is gaining popularity as an addition to standard dynamic contrast-enhanced breast MRI. Although adding diffusion-weighted imaging (DWI) to the standard protocol design would require increased scanning-time, implementation during the contrast-enhanced phase could offer a multiparametric MRI protocol without any additional scanning time. However, gadolinium within a region of interest (ROI) might affect assessments of DWI. This study aims to determine if acquiring DWI postcontrast, incorporated in an abbreviated MRI protocol, would statistically significantly affect lesion classification. In addition, the effect of postcontrast DWI on breast parenchyma was studied. MATERIALS AND METHODS Screening or preoperative MRIs (1.5 T/3 T) were included for this study. Diffusion-weighted imaging was acquired with single-shot spin echo-echo planar imaging before and at approximately 2 minutes after gadoterate meglumine injection. Apparent diffusion coefficients (ADCs) based on 2-dimensional ROIs of fibroglandular tissue, as well as benign and malignant lesions at 1.5 T/3.0 T, were compared with a Wilcoxon signed rank test. Diffusivity levels were compared between precontrast and postcontrast DWI with weighted κ. An overall P ≤ 0.05 was considered statistically significant. RESULTS No significant changes were observed in ADC mean after contrast administration in 21 patients with 37 ROI of healthy fibroglandular tissue and in the 93 patients with 93 (malignant and benign) lesions. This effect remained after stratification on B 0 . In 18% of all lesions, a diffusion level shift was observed, with an overall weighted κ of 0.75. CONCLUSIONS This study supports incorporating DWI at 2 minutes postcontrast when ADC is calculated based on b150-b800 with 15 mL 0.5 M gadoterate meglumine in an abbreviated multiparametric MRI protocol without requiring extra scan time.
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Affiliation(s)
- Kay J J van der Hoogt
- From the Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands (K.J.J.H., R.-J.S., R.W., R.G.H.B., R.M.M.); GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (K.J.J.H., R.G.H.B.); Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, the Netherlands (R.-J.S.); Department of Medical Physics, the Netherlands Cancer Institute, Amsterdam, the Netherlands (L.C.B.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (R.M.M.); and Danish Colorectal Cancer Unit South, Vejle University Hospital, Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark (R.G.H.B.)
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Billy CA, Darmiati S, Prihartono J. Diagnostic accuracy of diffusion weighted imaging compared to magnetic resonance spectroscopy in differentiation of benign and malignant breast lesions: A systematic review and meta-analysis. Eur J Radiol 2023; 168:111124. [PMID: 37820523 DOI: 10.1016/j.ejrad.2023.111124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 07/12/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVE To compare the sensitivity and specificity of diffusion weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in the differentiation of benign and malignant breast lesions. METHODS Scopus, PubMed, and other registries were searched up to April 2023. We included diagnostic studies with DWI and MRS as index tests and histopathologic examination as the reference standard for differentiating benign and malignant breast lesions in adult females. We excluded studies involving healthy women, only breast cancer patients, and non-comparative diagnostic accuracy studies on either index test. The sensitivity and specificity of DWI and MRS were investigated and pooled using random-effect bivariate meta-analysis. Risk of bias was assessed using QUADAS-2. Evidence quality was summarized using GRADE. RESULTS Eight eligible studies involving 632 females and 687 breast lesions were identified. The pooled sensitivity and specificity of DWI were 92% (CI 85-96%) and 88% (CI 75-94%), respectively. The pooled sensitivity and specificity of MRS were 85% (CI 66-94%) and 85% (CI 77-91%), respectively. No significant difference was noted in the sensitivity (7%, CI -8-22%) and specificity (3%, CI -9-14%) between DWI and MRS. CONCLUSIONS In low to moderate quality evidence, DWI and MRS show comparable sensitivity and specificity in differentiating benign and malignant breast lesions.
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Affiliation(s)
- Christy Amanda Billy
- Department of Radiology, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine, University of Indonesia, Jakarta 10430, Indonesia.
| | - Sawitri Darmiati
- Department of Radiology, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine, University of Indonesia, Jakarta 10430, Indonesia
| | - Joedo Prihartono
- Department of Community Medicine, Faculty of Medicine, University of Indonesia, Jakarta 10310, Indonesia
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Kazama T, Takahara T, Endo J, Yamamuro H, Sekiguchi T, Niwa T, Niikura N, Okamura T, Kumaki N, Hashimoto J. Computed diffusion-weighted imaging with a low-apparent diffusion coefficient-pixel cut-off technique for breast cancer detection. Br J Radiol 2023; 96:20220951. [PMID: 37393536 PMCID: PMC10607411 DOI: 10.1259/bjr.20220951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 06/15/2023] [Accepted: 06/24/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE This study aimed to compare the image quality and diagnostic performance of computed diffusion-weighted imaging (DWI) with low-apparent diffusion coefficient (ADC)-pixel cut-off technique (cDWI cut-off) and actual measured DWI (mDWI). METHODS Eighty-seven consecutive patients with malignant breast lesions and 72 with negative breast lesions who underwent breast MRI were retrospectively evaluated. Computed DWI with high b-values of 800, 1200, and 1500 s/mm2 and ADC cut-off thresholds of none, 0, 0.3, and 0.6 (×10-3 mm2/s) were generated from DWI with two b-values (0 and 800 s/mm2). To identify the optimal conditions, two radiologists evaluated the fat suppression and lesion reduction failure using a cut-off technique. The contrast between breast cancer and glandular tissue was evaluated using region of interest analysis. Three other board-certified radiologists independently assessed the optimised cDWI cut-off and mDWI data sets. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. RESULTS When an ADC cut-off threshold of 0.3 or 0.6 (× 10-3 mm2/s) was applied, fat suppression improved significantly (p < .05). The contrast of the cDWI cut-off with a b-value of 1200 or 1500 s/mm2 was better than the mDWI (p < .01). The ROC area under the curve for breast cancer detection was 0.837 for the mDWI and 0.909 for the cDWI cut-off (p < .01). CONCLUSION The cDWI cut-off provided better diagnostic performance than mDWI for breast cancer detection. ADVANCES IN KNOWLEDGE Using the low-ADC-pixel cut-off technique, computed DWI can improve diagnostic performance by increasing contrast and eliminating un-suppressed fat signals.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka, Japan
| | - Jun Endo
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
| | - Hiroshi Yamamuro
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
| | - Tatsuya Sekiguchi
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
| | - Tetsu Niwa
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
| | - Naoki Niikura
- Department of Breast Oncology, Tokai University School of Medicine, Isehara, Japan
| | - Takuho Okamura
- Department of Breast Oncology, Tokai University School of Medicine, Isehara, Japan
| | - Nobue Kumaki
- Department of Pathology, Tokai University School of Medicine, Isehara, Japan
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara, Japan
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Saccenti L, Mellon CDM, Scholer M, Jolibois Z, Stemmer A, Weiland E, de Bazelaire C. Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI. Diagn Interv Imaging 2023; 104:410-418. [PMID: 37208291 DOI: 10.1016/j.diii.2023.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/21/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions. MATERIALS AND METHODS This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis. RESULTS A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001). CONCLUSION Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.
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Affiliation(s)
- Laetitia Saccenti
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France.
| | - Constance de Margerie Mellon
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - Margaux Scholer
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Zoe Jolibois
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Alto Stemmer
- Siemens Healthineers GMBH, 91052 Erlanger, Germany
| | | | - Cedric de Bazelaire
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
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Honda M, Iima M. Is the breast ADC category system a useful addition to BI-RADS? Eur Radiol 2023; 33:5398-5399. [PMID: 37219620 DOI: 10.1007/s00330-023-09752-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 03/11/2023] [Accepted: 04/10/2023] [Indexed: 05/24/2023]
Affiliation(s)
- Maya Honda
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan.
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
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11
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Yao FF, Zhang Y. A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker. Clin Imaging 2023; 98:36-58. [PMID: 36996598 DOI: 10.1016/j.clinimag.2023.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/03/2023] [Accepted: 03/21/2023] [Indexed: 04/01/2023]
Abstract
Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.
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Affiliation(s)
- Fei-Fei Yao
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China.
| | - Yan Zhang
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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12
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Zhou Z, Chen Y, Zhao F, Sun Z, Zhu L, Yu H, Wang W. Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study. Acta Radiol 2023; 64:951-961. [PMID: 35765225 DOI: 10.1177/02841851221107626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice. PURPOSE To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer. MATERIAL AND METHODS A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed. RESULTS The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004). CONCLUSION IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.
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Affiliation(s)
- Zhe Zhou
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Yueqin Chen
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Fan Zhao
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Zhanguo Sun
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Laimin Zhu
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Hao Yu
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Weiwei Wang
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
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13
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Milon A, Flament V, Gueniche Y, Kermarrec E, Chabbert-Buffet N, Darai É, Touboul C, Razakamanantsoa L, Thomassin-Naggara I. How to optimize MRI breast protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences. Diagn Interv Imaging 2023; 104:284-291. [PMID: 36801096 DOI: 10.1016/j.diii.2023.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
PURPOSE The purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI. MATERIAL AND METHODS Women who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10-3 mm2/s to classify the lesions based on morphology and these two functional criteria only. RESULTS Two hundred fifty-seven women (median age: 51 years; range: 16-92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10-3 mm2/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively. CONCLUSION BI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.
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Affiliation(s)
- Audrey Milon
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France.
| | - Vincent Flament
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Yoram Gueniche
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Edith Kermarrec
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Nathalie Chabbert-Buffet
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Émile Darai
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Cyril Touboul
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Leo Razakamanantsoa
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Isabelle Thomassin-Naggara
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France
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14
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Romeo V, Helbich TH, Pinker K. Breast PET/MRI Hybrid Imaging and Targeted Tracers. J Magn Reson Imaging 2023; 57:370-386. [PMID: 36165348 PMCID: PMC10074861 DOI: 10.1002/jmri.28431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (18 F-FDG, 18 F-NaF, choline, 18 F-FES, 18 F-FES, 89 Zr-trastuzumab, choline derivatives, 18 F-FLT, and 68 Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria
| | - Katja Pinker
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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15
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Shahbazi-Gahrouei D, Aminolroayaei F, Nematollahi H, Ghaderian M, Gahrouei SS. Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. Diagnostics (Basel) 2022; 12:2741. [PMID: 36359584 PMCID: PMC9689118 DOI: 10.3390/diagnostics12112741] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/26/2022] [Accepted: 11/07/2022] [Indexed: 08/28/2023] Open
Abstract
Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.
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Affiliation(s)
- Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Fahimeh Aminolroayaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hamide Nematollahi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Mohammad Ghaderian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Sogand Shahbazi Gahrouei
- Department of Management, School of Humanities, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
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16
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Marino MA, Avendano D, Sevilimedu V, Thakur S, Martinez D, Lo Gullo R, Horvat JV, Helbich TH, Baltzer PAT, Pinker K. Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors. Eur J Radiol 2022; 156:110523. [PMID: 36122521 PMCID: PMC10014485 DOI: 10.1016/j.ejrad.2022.110523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/14/2022] [Accepted: 09/09/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors. METHOD Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader. RESULTS The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %). CONCLUSION mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.
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Affiliation(s)
- Maria Adele Marino
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Nuevo Leon, Mexico
| | - Varadan Sevilimedu
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, NY, USA
| | - Sunitha Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Danny Martinez
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Joao V Horvat
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA.
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17
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Lo Gullo R, Sevilimedu V, Baltzer P, Le Bihan D, Camps-Herrero J, Clauser P, Gilbert FJ, Iima M, Mann RM, Partridge SC, Patterson A, Sigmund EE, Thakur S, Thibault FE, Martincich L, Pinker K. A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice. Eur Radiol 2022; 32:6588-6597. [PMID: 35507050 PMCID: PMC9064723 DOI: 10.1007/s00330-022-08833-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI. METHODS An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test. RESULTS Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10-3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values. CONCLUSION The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform. KEY POINTS • Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, NY, New York, 10017, USA
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Denis Le Bihan
- NeuroSpin/Joliot, CEA-Saclay Center, Paris-Saclay University, Gif-sur-Yvette, France
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- National Institute for Physiological Sciences, Okazaki, Japan
| | | | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
| | - Ritse M Mann
- Department of Diagnostic Imaging, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, 6, 60 1st Avenue, New York, NY, 10016, USA
| | - Sunitha Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Fabienne E Thibault
- Department of Medical Imaging, Institut Curie, 26 Rue d'Ulm, F-75005, Paris, France
| | - Laura Martincich
- Unit of Radiodiagnostics, Ospedale Cardinal G. Massaia -ASL AT, Via Conte Verde 125, 14100, Asti, Italy
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria.
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18
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Wang W, Lv S, Xun J, Wang L, Zhao F, Wang J, Zhou Z, Chen Y, Sun Z, Zhu L. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer. Eur J Radiol 2022; 154:110392. [DOI: 10.1016/j.ejrad.2022.110392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022]
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19
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Can DWI provide additional value to Kaiser score in evaluation of breast lesions. Eur Radiol 2022; 32:5964-5973. [PMID: 35357535 DOI: 10.1007/s00330-022-08674-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). METHODS Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10-3mm2/s, 1.4 × 10-3mm2/s, 1.53 × 10-3mm2/s, and 1.6 × 10-3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. RESULTS The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). CONCLUSION KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. KEY POINTS • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.
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20
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Zhang R, Xu M, Zhou C, Ding X, Lu H, Ge M, Du L, Bu Y. The value of noncontrast MRI in evaluating breast imaging reporting and data system category 0 lesions on digital mammograms. Quant Imaging Med Surg 2022; 12:4069-4080. [PMID: 35919041 PMCID: PMC9338372 DOI: 10.21037/qims-21-968] [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: 10/01/2021] [Accepted: 05/23/2022] [Indexed: 11/06/2022]
Abstract
Background Benign and malignant diagnosis of nonpalpable breast imaging reporting and data system (BI-RADS) category 0 lesions on digital mammograms (DMs) is very important. We compared the diagnostic performance of non-contrast-enhanced magnetic resonance imaging (MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for them. We sought to evaluate BI-RADS category 0 lesions using 3 MRI sequences: short tau inversion recovery (STIR), STIR combined with high b value diffusion-weighted imaging (STIR-DWI), and DCE-MRI. Methods We retrospectively reviewed 114 breast DMs rated as nonpalpable BI-RADS category 0 lesions in 112 patients from January 2014 to June 2019. STIR, high b value DWI, and DCE-MRI were performed for all patients. Two breast radiologists read individual sequences (STIR, DWI, DCE-MRI) and pairs of sequences (STIR-DWI) to detect BI-RADS category 0 lesions in DMs. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance according to a best valuable comparator that combined MRI imaging, clinical, and pathological data. Results Among of 114 lesions (the median age of patients was 47 years; the median size of the lesion was 19 mm), 32 (48.5%) malignant lesions were missed by STIR, 9 (13.6%) malignant lesions were missed by STIR-DWI, and 3 (4.5%) malignant lesions were missed by DCE-MRI. The principal finding of our study was that STIR-DWI and DCE-MRI showed higher diagnostic accuracy than did STIR (P<0.01). STIR-DWI showed higher accuracy [area under the curve (AUC) =0.858; sensitivity =87.8%] for BI-RADS category 0 lesions in DMs than did STIR (AUC =0.754; sensitivity =51.5%), while the performance was comparable to that of DCE-MRI (AUC =0.884; sensitivity =95.5%). Conclusions Using pairs of sequences (STIR-DWI) is a non-contrast-enhanced MRI technique and had an equal diagnostic performance in distinguishing benign from malignant lesions among nonpalpable BI-RADS category 0 lesions to that of DCE-MRI. As a result, STIR-DWI as having the potential to improve the safety and efficacy in of breast cancer screening, especially in nonpalpable BI-RADS category 0 lesions at in DMs.
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Affiliation(s)
- Ruixin Zhang
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Changyu Zhou
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuewei Ding
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Huan Lu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Min Ge
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Liang Du
- Department of Radiology, Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Yangyang Bu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.,The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
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21
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Egnell L, Jerome NP, Andreassen MMS, Bathen TF, Goa PE. Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T. NMR IN BIOMEDICINE 2022; 35:e4654. [PMID: 34967468 DOI: 10.1002/nbm.4654] [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: 03/02/2021] [Revised: 09/21/2021] [Accepted: 10/10/2021] [Indexed: 06/14/2023]
Abstract
PURPOSE The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value. MATERIALS AND METHODS Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm2 ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm2 ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times. RESULTS Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2p = 100 ms) and 60 % (T2p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm2 was around 8% lower than at b = 0 s/mm2 (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01). CONCLUSION The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.
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Affiliation(s)
- Liv Egnell
- Department of Physics, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Neil P Jerome
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Maren M S Andreassen
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F Bathen
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
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22
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Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial. Tomography 2022; 8:701-717. [PMID: 35314635 PMCID: PMC8938828 DOI: 10.3390/tomography8020058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022] Open
Abstract
In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.
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23
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Meyer HJ, Wienke A, Surov A. Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis. Breast Care (Basel) 2022; 17:47-54. [PMID: 35355697 PMCID: PMC8914237 DOI: 10.1159/000514407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/08/2021] [Indexed: 02/03/2023] Open
Abstract
Background Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Objectives This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients. Method The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included. Results The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07). Conclusions ADC values cannot be used to discriminate between molecular subtypes of BC.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
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24
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Wang W, Zhang X, Zhu L, Chen Y, Dou W, Zhao F, Zhou Z, Sun Z. Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging. Front Oncol 2022; 12:825264. [PMID: 35174093 PMCID: PMC8841854 DOI: 10.3389/fonc.2022.825264] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/07/2022] [Indexed: 01/31/2023] Open
Abstract
Purpose To explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer. Materials and Methods A total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results. Results D* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10−3 mm2/s and MK ≥ 0.69, the NPI tended to be high grade (with areas under the curve (AUCs) of 0.712 and 0.647, respectively). The combination of D* and MK demonstrated the highest AUC of 0.734 in grading NPI with sensitivity and accuracy of 71.7% and 77.1%, respectively. Additionally, higher D*, f, and MK and lower ADC and D values were observed in the high Ki-67 than low Ki-67 expression groups (p < 0.05). The AUC of the combined model (D + D* + f + MK) was 0.755, being significantly higher than that of single parameters (Z = 2.770~3.244, p = 0.001~0.006) in distinguishing high from low Ki-67 expression. D* and f values in the Luminal A subtype were significantly lower than in other subtypes (p < 0.05). Luminal B showed decreased D value compared with other subtypes (p < 0.05). The HER-2-positive subtype demonstrated increased ADC values compared with the Luminal B subtype (p < 0.05). Luminal A/B showed significantly lower D, D*, MD, and MK than the non-Luminal subtypes (p < 0.05). The combined model (D + D* + MD + MK) showed an AUC of 0.830 in diagnosing the Luminal and non-Luminal subtypes, which is significantly higher than that of a single parameter (Z = 3.273~4.440, p < 0.01). f ≥ 54.30% [odds ratio (OR) = 1.038, p < 0.001] and MK ≥ 0.68 (OR = 24.745, p = 0.012) were found to be significant predictors of triple-negative subtypes. The combination of f and MK values demonstrated superior diagnostic performance with AUC, sensitivity, specificity, and accuracy of 0.756, 67.5%, 77.5%, and 82.4%, respectively. Moreover, as shown in the calibration curve, strong agreements were observed between nomogram prediction probability and actual findings in the prediction of genotypes (p = 0.22, 0.74). Conclusion DWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment.
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Affiliation(s)
- Weiwei Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xindong Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Laimin Zhu
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | | | - Fan Zhao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhe Zhou
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhanguo Sun
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
- *Correspondence: Zhanguo Sun,
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25
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Jeong S, Kim TH. Diffusion-weighted imaging of breast invasive lobular carcinoma: comparison with invasive carcinoma of no special type using a histogram analysis. Quant Imaging Med Surg 2022; 12:95-105. [PMID: 34993063 DOI: 10.21037/qims-21-355] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/03/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND To investigate the imaging findings and visibility of breast invasive lobular carcinoma (ILC) on diffusion-weighted imaging (DWI) and compare quantitative apparent diffusion coefficient (ADC) metrics of ILC and invasive carcinoma of no special type (NST) using a histogram analysis. METHODS We performed an observational retrospective study of 629 consecutive women with pathologically proven ILC and invasive ductal carcinoma of NST, who underwent 3-T MRI including DWI, between January 2017 and August 2020. RESULTS After propensity score matching, 71 women were allocated to each group. On DWI, 9 (12.7%) lesions of ILC and 4 (5.6%) invasive carcinomas of the NST were not visualized. For the tumor visibility on DWI, tumor size, tumor ADC value, and background diffusion grade were significantly associated with the visibility score in both groups (all P<0.05), whereas the mean background ADC value was not significant (P>0.05). The mean ADC (1.226×10-3 vs. 1.052×10-3 mm2/s, P<0.001), median ADC (1.222×10-3 vs. 1.051×10-3 mm2/s, P=0.002), maximum ADC (1.758×10-3 vs. 1.504×10-3 mm2/s, P<0.001), minimum ADC (0.717×10-3 vs. 0.649×10-3 mm2/s, P=0.003), 90th percentile ADC (1.506×10-3 vs. 1.292×10-3 mm2/s, P<0.001) and 10th percentile ADC (0.956×10-3 vs. 0.818×10-3 mm2/s, P=0.008) were higher in ILC than in invasive carcinoma of NST. Additionally, the ADC difference value of the ILC was higher than that of invasive carcinoma of NST (1.04×10-3 vs. 0.855×10-3 mm2/s, P=0.027). CONCLUSIONS On DWI, the visibility of ILC was lower compared to invasive carcinoma of NST. ILC showed higher quantitative ADC values and higher ADC difference values.
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Affiliation(s)
- Seongkyun Jeong
- Department of Human Intelligence Robot Engineering, Sangmyung University, Cheonan, Republic of Korea
| | - Tae Hee Kim
- Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Suwon, Republic of Korea
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26
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Sun SY, Ding Y, Li Z, Nie L, Liao C, Liu Y, Zhang J, Zhang D. Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions. Front Oncol 2021; 11:699127. [PMID: 34722246 PMCID: PMC8554332 DOI: 10.3389/fonc.2021.699127] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/27/2021] [Indexed: 11/22/2022] Open
Abstract
Objectives To evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy. Methods A total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%. Results ΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1. Conclusions The mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.
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Affiliation(s)
- Shi Yun Sun
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Yingying Ding
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Zhuolin Li
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Lisha Nie
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, China
| | - Chengde Liao
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Yifan Liu
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Jia Zhang
- Department of Radiology, Third People's Hospital of Yunnan Province, Kunming, China
| | - Dongxue Zhang
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
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27
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Girometti R, Sardanelli F, Marconi V, Bondini F, De Serio I, Bracciani A, Londero V, Zuiani C. Diagnostic Performance of Digital Breast Tomosynthesis, Unenhanced MRI, and Their Combination in the Preoperative Assessment of Breast Cancer: A Multi-reader Study. Acad Radiol 2021; 28:1339-1351. [PMID: 32307272 DOI: 10.1016/j.acra.2020.03.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 02/14/2020] [Accepted: 03/10/2020] [Indexed: 01/31/2023]
Abstract
RATIONALE AND OBJECTIVES To compare the diagnostic performance of digital breast tomosynthesis (DBT) and unenhanced magnetic resonance imaging (UMRI) in the preoperative assessment of breast cancer. MATERIALS AND METHODS We retrospectively included 59 patients with 74 pathology-proven cancers who underwent DBT and preoperative 1.5 T magnetic resonance imaging between January 2016 and February 2017. Four residents with 2-3 years of experience, blinded to pathology, independently reviewed DBT and UMRI (diffusion-weighted and unenhanced T1-weighted sequences), using the breast imaging reporting and data system (BI-RADS) and a 0-5 Likert score, respectively. We calculated per-lesion sensitivity and positive predictive value of DBT, UMRI, and combined DBT+UMRI, as well as the agreement between DBT and UMRI vs. pathology in assessing cancer size (Bland-Altman analysis). Logistic regression was performed to assess clinical features predictive of missing cancer. RESULTS Of 74 lesions, 84% were invasive ductal carcinoma, 27% of which with an in situ component; 31% of cancers were ≤10 mm large. Sensitivity of UMRI (74-85%) was equal or higher than that of DBT (68-82%), with similar positive predictive value (93-97% vs. 98-100%, respectively). DBT+UMRI increased the sensitivity up to 88%. UMRI showed closer limits of agreement with pathological size than DBT. Missing cancer was independently predicted by size ≤10 mm on DBT, UMRI, and DBT+UMRI (odds ratio 18.7, 5.1, and 13.3, respectively), and by increased breast density on DBT alone (odds ratio 3.50). CONCLUSION UMRI was equal or better than DBT in the preoperative assessment of breast cancer. Combined imaging achieved up to 88% per-lesion sensitivity, suggesting potential use in clinical practice.
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28
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Sanderink WBG, Teuwen J, Appelman L, Moy L, Heacock L, Weiland E, Sechopoulos I, Mann RM. Diffusion weighted imaging for evaluation of breast lesions: Comparison between high b-value single-shot and routine readout-segmented sequences at 3 T. Magn Reson Imaging 2021; 84:35-40. [PMID: 34560230 DOI: 10.1016/j.mri.2021.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/10/2021] [Accepted: 09/16/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE In this study, we compare readout-segmented echo-planar imaging (rs-EPI) Diffusion Weighted Imaging (DWI) to a work-in-progress single-shot EPI with modified Inversion Recovery Background Suppression (ss-EPI-mIRBS) sequence at 3 T using a b-value of 2000 s/mm2 on image quality, lesion visibility and evaluation time. METHOD From September 2017 to December 2018, 23 women (one case used for training) with known breast cancer were included in this study, after providing signed informed consent. Women were scanned with the conventional rs-EPI sequence and the work-in-progress ss-EPI-mIRBS during the same examination. Four breast radiologists (4-13 years of experience) independently scored both series for overall image quality (1: extremely poor to 9: excellent). All lesions (47 in total, 36 malignant, and 11 benign and high-risk) were evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible) and probability of malignancy (BI-RADS 1 to 5). ADC values were determined by measuring signal intensity in the lesions using dynamic contrast-enhanced (DCE) images for reference. Evaluation times for all assessments were automatically recorded. Results were analyzed using the visual grading characteristics (VGC) and the resulting area under the curve (AUCVGC) method. Statistical analysis was performed in SPSS, with McNemar tests, and paired t-tests used for comparison. RESULTS No significant differences were detected between the two sequences in image quality (AUCVGC: 0.398, p = 0.087) and lesion visibility (AUCVGC: 0.534, p = 0.336) scores. Lesion characteristics (e.g benign and high-risk, versus malignant; small (≤10 mm) vs. larger (>10 mm)) did not result in different image quality or lesion visibility between sequences. Sensitivity (rs-EPI: 72.2% vs. ss-EPImIRBS: 78.5%, p = 0.108) and specificity (70.5% vs. 56.8%, p = 0.210, respectively) were comparable. In both sequences the mean ADC value was higher for benign and high-risk lesions than for malignant lesions (ss-EPI-mIRBS: p = 0.022 and rs-EPI: p = 0.055). On average, ss-EPI-mIRBS resulted in decreased overall reading time by 7.7 s/case (p = 0.067); a reduction of 17%. For malignant lesions, average reading time was significantly shorter using ss-EPI-mIRBS compared to rs-EPI (64.0 s/lesion vs. 75.9 s/lesion, respectively, p = 0.039). CONCLUSION Based on this study, the ss-EPI sequence using a b-value of 2000 s/mm2 enables for a mIRBS acquisition with quality and lesion conspicuity that is comparable to conventional rs-EPI, but with a decreased reading time.
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Affiliation(s)
- Wendelien B G Sanderink
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, the Netherlands.
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, the Netherlands; Department of Radiology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, the Netherlands
| | - Linda Appelman
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, the Netherlands
| | - Linda Moy
- Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 4(th) floor, New York, NY 10016, United States
| | - Laura Heacock
- Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 4(th) floor, New York, NY 10016, United States
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, the Netherlands
| | - Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525GA Nijmegen, the Netherlands; Department of Radiology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, the Netherlands
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Ma W, Mao J, Wang T, Huang Y, Zhao ZH. Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis. Eur J Radiol 2021; 141:109809. [PMID: 34116452 DOI: 10.1016/j.ejrad.2021.109809] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 05/27/2021] [Accepted: 05/31/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis. METHODS We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis. RESULTS We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I2>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*. CONCLUSIONS Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.
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Affiliation(s)
- Weili Ma
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing 312000, China
| | - Jiwei Mao
- Department of Radiation Oncology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing 312000, China
| | - Ting Wang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing 312000, China
| | - Yanan Huang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing 312000, China
| | - Zhen Hua Zhao
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing 312000, China.
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Mori M, Fujioka T, Katsuta L, Yashima Y, Nomura K, Yamaga E, Hosoya T, Oda G, Nakagawa T, Kubota K, Tateishi U. Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil. Clin Imaging 2021; 78:217-222. [PMID: 34051405 DOI: 10.1016/j.clinimag.2021.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 03/22/2021] [Accepted: 04/25/2021] [Indexed: 11/27/2022]
Abstract
We aimed to evaluate the usefulness of a fast protocol of diffusion-weighted imaging (DWI) with one excitation using 3T magnetic resonance imaging (MRI) and a 16-channel breast coil. We analyzed 30 lesions from 27 women between February 2020 and June 2020. The visibility score (from 1 = extremely poor to 5 = excellent) and apparent diffusion coefficient (ADC) value between one and four excitations were evaluated by two readers. The image acquisition time was 40 s for one excitation and 1 min 52 s for four excitations. The visibility scores were 4.630 ± 0.718 and 4.267 ± 1.015 for one excitation and 4.730 ± 0.691 and 4.200 ± 1.000 for four excitations by the two readers. There was no significant difference in the visibility (P = 0.184 and P = 0.423), mean ADC value (P = 0.918 and P = 0.417), and minimum ADC value (P = 0.936 and P = 0.443) between one and four excitations by the two readers. Despite the short acquisition time, the visibility score and ADC values of one-excitation DWI were comparable to that with four excitations. Our fast DWI protocol could provide reproducible visibility and ADC value, potentially helping radiologists to efficiently diagnose patients.
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Affiliation(s)
- Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan.
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Kyoko Nomura
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tokuko Hosoya
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Hospital, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
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Clauser P, Krug B, Bickel H, Dietzel M, Pinker K, Neuhaus VF, Marino MA, Moschetta M, Troiano N, Helbich TH, Baltzer PAT. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy. Clin Cancer Res 2021; 27:1941-1948. [PMID: 33446565 PMCID: PMC8406278 DOI: 10.1158/1078-0432.ccr-20-3037] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies. EXPERIMENTAL DESIGN This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10-3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis. RESULTS There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282). CONCLUSIONS An ADC cutoff of ≥1.5 × 10-3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Krug
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Hubert Bickel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, Friedrich-Alexander-University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor-Frederic Neuhaus
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy
| | - Marco Moschetta
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Nicoletta Troiano
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
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Li X, Jiang N, Zhang C, Luo X, Zhong P, Fang J. Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast. Cancer Imaging 2021; 21:29. [PMID: 33712070 PMCID: PMC7953576 DOI: 10.1186/s40644-021-00398-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 03/04/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast. METHODS The preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency. RESULTS For ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793-0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%. CONCLUSION Texture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.
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Affiliation(s)
- Xiaoguang Li
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Nianping Jiang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Chunlai Zhang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Xiangguo Luo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Peng Zhong
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Jingqin Fang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China.
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Sanderink WBG, Teuwen J, Appelman L, Moy L, Heacock L, Weiland E, Karssemeijer N, Baltzer PAT, Sechopoulos I, Mann RM. Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI. Eur J Radiol 2021; 138:109626. [PMID: 33711569 DOI: 10.1016/j.ejrad.2021.109626] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/15/2021] [Accepted: 03/01/2021] [Indexed: 01/09/2023]
Abstract
PURPOSE To compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition. METHOD From September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3 T during the same imaging examination. Four breast radiologists (4-13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n = 52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUCVGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate. RESULTS Overall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUCVGC: 0.698, P = 0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUCVGC: 0.660, P = 0.022 and AUCVGC: 0.700 P = 0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUCVGC: 0.427, P = 0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P = 0.035). Sensitivity and specificity were comparable between both sequences (P = 0.760 and P = 0.549, respectively). CONCLUSIONS Despite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.
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Affiliation(s)
- Wendelien B G Sanderink
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands.
| | - Jonas Teuwen
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands
| | - Linda Appelman
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands
| | - Linda Moy
- Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 4(th) Floor, New York, NY, 10016, United States
| | - Laura Heacock
- Department of Radiology, New York University Langone Medical Center, 660 First Avenue, 4(th) Floor, New York, NY, 10016, United States
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare, Allee am Roethelheimpark 2, Erlangen, 91052, Germany
| | - Nico Karssemeijer
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital, Währinger Gürtel 18-20, Vienna, 1090, Austria
| | - Ioannis Sechopoulos
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands
| | - Ritse M Mann
- Medical Imaging, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525GA, the Netherlands
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Wielema M, Sijens PE, Dijkstra H, De Bock GH, van Bruggen IG, Siegersma JE, Langius E, Pijnappel RM, Dorrius MD, Oudkerk M. Diffusion weighted imaging of the breast: Performance of standardized breast tumor tissue selection methods in clinical decision making. PLoS One 2021; 16:e0245930. [PMID: 33493230 PMCID: PMC7833148 DOI: 10.1371/journal.pone.0245930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/08/2021] [Indexed: 12/05/2022] Open
Abstract
Objectives In breast diffusion weighted imaging (DWI) protocol standardization, it is recently shown that no breast tumor tissue selection (BTTS) method outperformed the others. The purpose of this study is to analyze the feasibility of three fixed-size breast tumor tissue selection (BTTS) methods based on the reproducibility, accuracy and time-measurement in comparison to the largest oval and manual delineation in breast diffusion weighted imaging data. Methods This study is performed with a consecutive dataset of 116 breast lesions (98 malignant) of at least 1.0 cm, scanned in accordance with the EUSOBI breast DWI working group recommendations. Reproducibility of the maximum size manual (BTTS1) and of the maximal size round/oval (BTTS2) methods were compared with three smaller fixed-size circular BTTS methods in the middle of each lesion (BTTS3, 0.12 cm3 volume) and at lowest apparent diffusion coefficient (ADC) (BTTS4, 0.12 cm3; BTTS5, 0.24 cm3). Mean ADC values, intraclass-correlation-coefficients (ICCs), area under the curve (AUC) and measurement times (sec) of the 5 BTTS methods were assessed by two observers. Results Excellent inter- and intra-observer agreement was found for any BTTS (with ICC 0.88–0.92 and 0.92–0.94, respectively). Significant difference in ADCmean between any pair of BTTS methods was shown (p = <0.001–0.009), except for BTTS2 vs. BTTS3 for observer 1 (p = 0.10). AUCs were comparable between BTTS methods, with highest AUC for BTTS2 (0.89–0.91) and lowest for BTTS4 (0.76–0.85). However, as an indicator of clinical feasibility, BTTS2-3 showed shortest measurement times (10–15 sec) compared to BTTS1, 4–5 (19–39 sec). Conclusion The performance of fixed-size BTTS methods, as a potential tool for clinical decision making, shows equal AUC but shorter ADC measurement time compared to manual or oval whole lesion measurements. The advantage of a fixed size BTTS method is the excellent reproducibility. A central fixed breast tumor tissue volume of 0.12 cm3 is the most feasible method for use in clinical practice.
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Affiliation(s)
- M. Wielema
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- * E-mail:
| | - P. E. Sijens
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - H. Dijkstra
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - G. H. De Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - I. G. van Bruggen
- Department of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - J. E. Siegersma
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - E. Langius
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands
| | - R. M. Pijnappel
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - M. D. Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - M. Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, the Netherlands
- Institute of Diagnostic Accuracy, Groningen, the Netherlands
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Choi BH, Baek HJ, Ha JY, Ryu KH, Moon JI, Park SE, Bae K, Jeon KN, Jung EJ. Feasibility Study of Synthetic Diffusion-Weighted MRI in Patients with Breast Cancer in Comparison with Conventional Diffusion-Weighted MRI. Korean J Radiol 2020; 21:1036-1044. [PMID: 32691539 PMCID: PMC7371621 DOI: 10.3348/kjr.2019.0568] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 02/20/2020] [Accepted: 03/17/2020] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To investigate the clinical feasibility of synthetic diffusion-weighted imaging (sDWI) at different b-values in patients with breast cancer by assessing the diagnostic image quality and the quantitative measurements compared with conventional diffusion-weighted imaging (cDWI). MATERIALS AND METHODS Fifty patients with breast cancer were assessed using cDWI at b-values of 800 and 1500 s/mm² (cDWI800 and cDWI1500) and sDWI at b-values of 1000 and 1500 s/mm² (sDWI1000 and sDWI1500). Qualitative analysis (normal glandular tissue suppression, overall image quality, and lesion conspicuity) was performed using a 4-point Likert-scale for all DWI sets and the cancer detection rate (CDR) was calculated. We also evaluated cancer-to-parenchyma contrast ratios for each DWI set in 45 patients with the lesion identified on any of the DWI sets. Statistical comparisons were performed using Friedman test, one-way analysis of variance, and Cochran's Q test. RESULTS All parameters of qualitative analysis, cancer-to-parenchyma contrast ratios, and CDR increased with increasing b-values, regardless of the type of imaging (synthetic or conventional) (p < 0.001). Additionally, sDWI1500 provided better lesion conspicuity than cDWI1500 (3.52 ± 0.92 vs. 3.39 ± 0.90, p < 0.05). Although cDWI1500 showed better normal glandular tissue suppression and overall image quality than sDWI1500 (3.66 ± 0.78 and 3.73 ± 0.62 vs. 3.32 ± 0.90 and 3.35 ± 0.81, respectively; p < 0.05), there was no significant difference in their CDR (90.0%). Cancer-to-parenchyma contrast ratios were greater in sDWI1500 than in cDWI1500 (0.63 ± 0.17 vs. 0.55 ± 0.18, p < 0.001). CONCLUSION sDWI1500 can be feasible for evaluating breast cancers in clinical practice. It provides higher tumor conspicuity, better cancer-to-parenchyma contrast ratio, and comparable CDR when compared with cDWI1500.
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Affiliation(s)
- Bo Hwa Choi
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea.,Department of Radiology, National Cancer Center, Goyang, Korea
| | - Hye Jin Baek
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea.
| | - Ji Young Ha
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Kyeong Hwa Ryu
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Jin Il Moon
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Sung Eun Park
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Kyungsoo Bae
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Kyung Nyeo Jeon
- Department of Radiology Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Eun Jung Jung
- Department of Surgery, Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Korea
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McDonald ES, Romanoff J, Rahbar H, Kitsch AE, Harvey SM, Whisenant JG, Yankeelov TE, Moy L, DeMartini WB, Dogan BE, Yang WT, Wang LC, Joe BN, Wilmes LJ, Hylton NM, Oh KY, Tudorica LA, Neal CH, Malyarenko DI, Comstock CE, Schnall MD, Chenevert TL, Partridge SC. Mean Apparent Diffusion Coefficient Is a Sufficient Conventional Diffusion-weighted MRI Metric to Improve Breast MRI Diagnostic Performance: Results from the ECOG-ACRIN Cancer Research Group A6702 Diffusion Imaging Trial. Radiology 2020; 298:60-70. [PMID: 33201788 DOI: 10.1148/radiol.2020202465] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. Purpose To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. Materials and Methods This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm2). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. Results Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41-60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59-0.79 vs AUC, 0.75; P = .02-.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm2) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm2; AUC, 0.73-0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17-.87). Conclusion Mean apparent diffusion coefficient calculated with a two-b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Elizabeth S McDonald
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Justin Romanoff
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Habib Rahbar
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Averi E Kitsch
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Sara M Harvey
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Jennifer G Whisenant
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Thomas E Yankeelov
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Wendy B DeMartini
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Basak E Dogan
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Wei T Yang
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Lilian C Wang
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Bonnie N Joe
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Lisa J Wilmes
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Nola M Hylton
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Karen Y Oh
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Luminita A Tudorica
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Colleen H Neal
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Dariya I Malyarenko
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Christopher E Comstock
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Mitchell D Schnall
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Thomas L Chenevert
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
| | - Savannah C Partridge
- From the Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (E.S.M., M.D.S.); Center for Statistical Sciences, Brown University, Providence, RI (J.R.); Department of Radiology, University of Washington, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109 (H.R., A.E.K., S.C.P.); Departments of Radiology and Radiological Sciences (S.M.H.) and Medicine (J.G.W.), Vanderbilt University Medical Center, Nashville, Tenn; Vanderbilt-Ingram Cancer Center, Nashville, Tenn (J.G.W.); Department of Biomedical Engineering, University of Texas, Austin, Tex (T.E.Y.); Department of Radiology, New York University School of Medicine, New York, NY (L.M.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.); Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (B.E.D.); Department of Breast Imaging, MD Anderson Cancer Center, Houston, Tex (W.T.Y.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (L.C.W.); Department of Radiology and Biomedical Imaging, University of California, San Francisco School of Medicine, San Francisco, Calif (B.N.J., L.J.W., N.M.H.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.Y.O., L.A.T.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (C.H.N., D.I.M., T.L.C.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (C.E.C.)
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Gao W, Zhang S, Guo J, Wei X, Li X, Diao Y, Huang W, Yao Y, Shang A, Zhang Y, Yang Q, Chen X. Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS. J Magn Reson Imaging 2020; 53:1118-1127. [PMID: 33179809 DOI: 10.1002/jmri.27435] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis. PURPOSE To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS). STUDY TYPE Retrospective. SUBJECTS Eighty patients (56 with malignant lesions and 24 with benign lesions). FIELD STRENGTH/SEQUENCE Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T. ASSESSMENT T1 relaxation time (T1 ), T2 relaxation time (T2 ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS. STATISTICAL TESTS The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC). RESULTS T2 , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P < 0.05). The combined T2 , PD, and ADC model had the best performance for differentiating malignant and benign lesions with AUC, sensitivity, specificity, positive predictive value, and negative predictive values of 0.904, 94.6%, 87.5%, 94.6%, and 87.5%, respectively. The corresponding results for BI-RADS were no AUC, 94.6%, 75.0%, 89.8%, and 85.7%, respectively. DATA CONCLUSION The approach that combined synthetic MRI and DWI outperformed BI-RADS in the differential diagnosis of malignant and benign breast lesions and was achieved without contrast agents. This approach may serve as an alternative and effective strategy for the improvement of breast lesion differentiation. LEVEL OF EVIDENCE 3. TECHNICAL EFFICACY STAGE 3.
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Affiliation(s)
- Weibo Gao
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shuqun Zhang
- Department of Oncology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jinxia Guo
- GE Healthcare, MR Research, Beijing, China
| | | | - Xiaohui Li
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yan Diao
- Department of Oncology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Wei Huang
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yue Yao
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ali Shang
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yanyan Zhang
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Quanxin Yang
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xin Chen
- Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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MRI-guided breast biopsy based on diffusion-weighted imaging: a feasibility study. Eur Radiol 2020; 31:2645-2656. [PMID: 33128183 PMCID: PMC8043934 DOI: 10.1007/s00330-020-07396-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/13/2020] [Accepted: 10/08/2020] [Indexed: 11/24/2022]
Abstract
Objectives This study evaluated the feasibility of DWI for lesion targeting in MRI-guided breast biopsies. Furthermore, it assessed device positioning on DWI during biopsy procedures. Methods A total of 87 biopsy procedures (5/87 bilateral) consecutively performed between March 2019 and June 2020 were retrospectively reviewed: in these procedures, a preliminary DWI sequence (b = 1300 s/mm2) was acquired to assess lesion detectability. We included 64/87 procedures on lesions detectable at DWI; DWI sequences were added to the standard protocol to localize lesion and biopsy device and to assess the site marker correct positioning. Results Mass lesions ranged from 5 to 48 mm, with a mean size of 10.7 mm and a median size of 8 mm. Non-mass lesions ranged from 7 to 90 mm, with a mean size of 33.9 mm and a median size of 31 mm. Positioning of the coaxial system was confirmed on both T1-weighted and DWI sequences. At DWI, the biopsy needle was detectable in 62/64 (96.9%) cases; it was not visible in 2/64 (3.1%) cases. The site marker was always identified using T1-weighted imaging; a final DWI sequence was acquired in 44/64 cases (68.8%). In 42/44 cases (95.5%), the marker was recognizable at DWI. Conclusions DWI can be used as a cost-effective, highly reliable technique for targeting both mass and non-mass lesions, with a minimum size of 5 mm, detectable at pre-procedural DWI. DWI is also a feasible technique to localize the biopsy device and to confirm the deployment of the site marker. Key Points • MRI-guided breast biopsy is performed in referral centers by an expert dedicated staff, based on prior MR imaging; contrast agent administration is usually needed for lesion targeting. • DWI represents a feasible, highly reliable technique for lesion targeting, avoiding contrast agent administration. • DWI allows a precise localization of both biopsy needle device and site marker.
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Meng N, Wang X, Sun J, Han D, Bai Y, Wei W, Wang Z, Jia F, Wang K, Wang M. A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer. Eur Radiol 2020; 31:1707-1717. [PMID: 32888071 DOI: 10.1007/s00330-020-07169-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/18/2020] [Accepted: 08/07/2020] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign and malignant breast lesions and analyze the correlations between the derived parameters and prognostic factors of breast cancer. METHODS One hundred thirty-five women underwent breast APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), apparent kurtosis coefficient (Kapp), and non-Gaussian diffusion coefficient (Dapp) were calculated according to the histological subtype, grade, and prognostic factors (Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), lymph node metastasis, and maximum lesion diameter). The differences, efficacy, and correlation between the parameters were determined. RESULTS The Kapp value was higher and the Dapp and MTRasym (3.5 ppm) values were lower in the malignant group than in the benign group (all p < 0.001; AUC (Kapp) = 0.913, AUC (Dapp) = 0.910, and AUC (MTRasym (3.5 ppm)) = 0.796). The differences in the AUC between Kapp and MTRasym (3.5 ppm) and between Dapp and MTRasym (3.5 ppm) were significant (p = 0.023, 0.046). Kapp was moderately correlated with the pathological grade (|r| = 0.724) and mildly correlated with Ki-67 and HER-2 expression (|r| = 0.454, 0.333). Dapp was moderately correlated with the pathological grade (|r| = 0.648) and mildly correlated with Ki-67 expression (|r| = 0.400). MTRasym (3.5 ppm) was only mildly correlated with the pathological grade (|r| = 0.468). CONCLUSION DKI is superior to APTWI in differentiating between benign and malignant breast lesions. Each parameter is correlated with some prognostic factors to a certain extent. KEY POINTS • DKI and APTWI provide valuable information regarding lesion characterization. • Kapp, Dapp, and MTRasym (3.5 ppm) are valid parameters for the characterization of tissue microstructure. • DKI is superior to APTWI in the study of breast cancer.
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Affiliation(s)
- Nan Meng
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, Henan, China.,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Xuejia Wang
- Department of MR, the First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Jing Sun
- Department of Pediatrics, Zhengzhou Central Hospital, Zhengzhou University, Zhengzhou, China
| | - Dongming Han
- Department of MR, the First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Yan Bai
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, Henan, China.,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Wei
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, Henan, China.,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhe Wang
- Department of Anesthesiology, the Third Affiliated Hospital, Xinxiang Medical University, Xinxiang, China
| | - Fei Jia
- Department of MR, the First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Meiyun Wang
- Department of Radiology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, Henan, China. .,Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
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Xu F, Liang YY, Guo Y, Liang ZP, Wu M, Chen S, Zeng XW. Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis. Acta Radiol 2020; 61:1165-1175. [PMID: 31924104 DOI: 10.1177/0284185119896520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool. PURPOSE To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions. MATERIAL AND METHODS A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics. RESULTS Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81-0.89) and 0.79 (95% CI = 0.72-0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected (P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71-0.88) and 0.86 (95% CI = 0.74-0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity (P > 0.05). CONCLUSION Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.
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Affiliation(s)
- Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Ying-ying Liang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Zhi-ping Liang
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Mei Wu
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Song Chen
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
| | - Xu-wen Zeng
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, Guangdong Province, PR China
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Daimiel Naranjo I, Lo Gullo R, Saccarelli C, Thakur SB, Bitencourt A, Morris EA, Jochelson MS, Sevilimedu V, Martinez DF, Pinker-Domenig K. Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI. Eur Radiol 2020; 31:356-367. [PMID: 32780207 PMCID: PMC7755636 DOI: 10.1007/s00330-020-07094-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/22/2020] [Accepted: 07/21/2020] [Indexed: 12/24/2022]
Abstract
Objectives To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values. Methods Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm2 images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI. Results Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm2 were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2 values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm2. DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases. Conclusion The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite. Key Points • The addition of synthetic b-values (1200–1500 s/mm2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts. • Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection. • A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies. Electronic supplementary material The online version of this article (10.1007/s00330-020-07094-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.,Department of Radiology, Breast Imaging Division, Istituto Europeo di Oncologia, Via Giuseppe Ripamonti, 435, 20141, Milano, Italy
| | - Carolina Saccarelli
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Almir Bitencourt
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.,Department of Imaging, A.C.Camargo Cancer Center, SP, São Paulo, Brazil
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Katja Pinker-Domenig
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA. .,Department of Biomedical Imaging and Image-guided Therapy Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Effect of IV Administration of a Gadolinium-Based Contrast Agent on Breast Diffusion-Tensor Imaging. AJR Am J Roentgenol 2020; 215:1030-1036. [PMID: 32755227 DOI: 10.2214/ajr.19.22085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The purpose of this study was to quantify changes in diffusion-tensor imaging (DTI) parameters before and after IV administration of a gadolinium-based contrast agent (GBCA) and explore the influence of those parameters on breast cancer diagnosis. SUBJECTS AND METHODS. A prospective cohort of 26 women with BI-RADS categories 0, 4, 5, or 6 underwent 3-T breast MRI with sequential DTI before GBCA administration and immediately after. Quantitative image analysis using dedicated DTI software yielded parametric DTI maps of each directional diffusion coefficient (DDC), mean diffusivity, and maximal anisotropy of the lesions and normal tissue. The color maps were evaluated and the lesion DTI parameters were compared before and after GBCA administration using appropriate statistical tests. RESULTS. Of the cohort, 58% had cancer (13 infiltrating ductal carcinoma, two ductal carcinoma in situ) and 42% had benign or normal results. All breast cancers were visually detected in the DDC λ1 maps before and after GBCA administration. Mean cancer size derived from λ1 maps before GBCA administration was 15.3 mm (range, 3.3-72.3 mm), and was not statistically significantly different from the size derived after GBCA administration of 17.3 mm (range, 3.9-71.0 mm). After GBCA administration, the cancers exhibited statistically significantly lower DDCs, mean diffusivity, and b0 intensity (p < 0.05), and no change in maximal anisotropy compared with before GBCA administration, whereas these parameters in normal and benign lesions did not change significantly after GBCA administration. The mean AUC values before and after GBCA administration, ranging from 0.735 to 0.985 and from 0.867 to 0.990, respectively, were not statistically significantly different for all parameters aside from λ3. CONCLUSION. Diagnostic accuracy using DTI was equivalent before and after GBCA administration, despite a change in the values of the DTI parameters. However, the limitations in standardization of contrast enhancement implies that unenhanced diffusion measurements should be preferred.
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Wielema M, Dorrius MD, Pijnappel RM, De Bock GH, Baltzer PAT, Oudkerk M, Sijens PE. Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis. PLoS One 2020; 15:e0232856. [PMID: 32374781 PMCID: PMC7202642 DOI: 10.1371/journal.pone.0232856] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/22/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC). METHODS AND FINDINGS In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95). CONCLUSIONS None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.
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Affiliation(s)
- M. Wielema
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - M. D. Dorrius
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - R. M. Pijnappel
- Department of Radiology, Utrecht University, University Medical Center Utrecht, Utrecht, The Netherlands
| | - G. H. De Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - P. A. T. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - M. Oudkerk
- University of Groningen, Groningen, The Netherlands
- Institute for Diagnostic Accuracy, Groningen, The Netherlands
| | - P. E. Sijens
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Samiei S, Smidt ML, Vanwetswinkel S, Engelen SME, Schipper RJ, Lobbes MBI, van Nijnatten TJA. Diagnostic performance of standard breast MRI compared to dedicated axillary MRI for assessment of node-negative and node-positive breast cancer. Eur Radiol 2020; 30:4212-4222. [PMID: 32221685 PMCID: PMC7338810 DOI: 10.1007/s00330-020-06760-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 02/11/2020] [Accepted: 02/18/2020] [Indexed: 11/25/2022]
Abstract
Objectives To investigate whether breast MRI has comparable diagnostic performance as dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer. Methods Forty-seven patients were included. All had undergone both breast MRI and dedicated axillary MRI, followed by surgery. All included breast MRI exams had complete field of view (FOV) of the axillary region. First, unenhanced T2-weighted (T2W) and subsequent diffusion-weighted (DW) images of both MRI exams were independently analyzed by two breast radiologists using a confidence scale and compared to histopathology. ADC values were measured by two researchers independently. Diagnostic performance parameters were calculated on a patient-by-patient basis. Results T2W breast MRI had the following diagnostic performance: sensitivity of 50.0% and 62.5%, specificity of 92.3%, PPV of 57.1% and 62.5%, NPV of 90.0% and 92.3%, and AUC of 0.72 for reader 1 and 0.78 for reader 2. T2W dedicated axillary MRI had the following diagnostic performance: sensitivity of 37.5% and 62.5%, specificity of 82.1% and 92.3%, PPV of 44.6% and 50.0%, NPV of 87.8% and 91.4%, and AUC of 0.65 for reader 1 and 0.73 for reader 2. In both evaluations, addition of DW images resulted in comparable diagnostic performance. For both breast MRI and dedicated axillary MRI, there was no significant difference between mean ADC values of benign and malignant lymph nodes. Conclusions T2W breast MRI with complete FOV of the axillary region has comparable diagnostic performance as T2W dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer. Optimization of T2W breast MRI protocol by including a complete FOV of the axillary region can, therefore, be recommended in clinical practice. Key Points • Breast MRI with complete field of view of the axillary region has comparable diagnostic performance as dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer. • Optimization of breast MRI protocol by including a complete field of view of the axillary region is recommended in clinical practice. • For both breast MRI and dedicated axillary MRI, DW imaging (including ADC measurements) is of no added value.
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Affiliation(s)
- Sanaz Samiei
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands.
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands.
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
| | - Marjolein L Smidt
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Sigrid Vanwetswinkel
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Sanne M E Engelen
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - Robert-Jan Schipper
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Thiemo J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Rotili A, Trimboli RM, Penco S, Pesapane F, Tantrige P, Cassano E, Sardanelli F. Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection. Breast Cancer Res Treat 2020; 180:111-120. [PMID: 31938940 DOI: 10.1007/s10549-019-05519-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/31/2019] [Indexed: 12/29/2022]
Abstract
PURPOSE To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection. METHODS Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ2, and κ statistics were applied. RESULTS Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11 years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78-93%) for both R1 and R2, 89/96 (93%, 86-97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10 mm was 22/31 (71%, 52-86%). Per-breast specificity was 562/609 (93%, 90-94%) for R1, 538/609 (88%, 86-91%) for R2, and 526/609 (86%¸ 83-89%) for DR (p < 0.001). Inter-observer agreement was substantial (κ = 0.736). Acquisition time varied from 3:00 to 6:22 min:s. Per-patient median interpretation time was 46 s (R1) and 51 s (R2). CONCLUSIONS DR DWI showed a 93% sensitivity and 88% specificity, with 71% sensitivity for cancers ≤ 10 mm, pointing out a potential for DWI as stand-alone screening method.
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Affiliation(s)
- Anna Rotili
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.
| | - Rubina Manuela Trimboli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
| | - Silvia Penco
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy
| | - Filippo Pesapane
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.,Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Priyan Tantrige
- Unit of Radiology, King's College Hospital, Denmark Hill, Brixton, London, SE5 9RS, UK
| | - Enrico Cassano
- IEO, European Institute of Oncology IRCCS, Milan, Via Giuseppe Ripamonti, 435, 20141, Milan, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
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Zhao Q, Xie T, Fu C, Chen L, Bai Q, Grimm R, Peng W, Wang S. Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient. Eur J Radiol 2019; 123:108782. [PMID: 31864142 DOI: 10.1016/j.ejrad.2019.108782] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 02/08/2023]
Abstract
PURPOSE The aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses. METHOD This retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm2) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported. RESULTS The 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement. CONCLUSIONS Whole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.
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Affiliation(s)
- Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance, Shenzhen, China
| | - Ling Chen
- Department of Pathology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qianming Bai
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, Partridge SC. Diffusion-weighted MRI for Unenhanced Breast Cancer Screening. Radiology 2019; 293:504-520. [PMID: 31592734 PMCID: PMC6884069 DOI: 10.1148/radiol.2019182789] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 06/18/2019] [Accepted: 07/10/2019] [Indexed: 01/12/2023]
Abstract
Diffusion-weighted (DW) MRI is a rapid technique that measures the mobility of water molecules within tissue, reflecting the cellular microenvironment. At DW MRI, breast cancers typically exhibit reduced diffusivity and appear hyperintense to surrounding tissues. On the basis of this characteristic, DW MRI may offer an unenhanced method to detect breast cancer without the costs and safety concerns associated with dynamic contrast material-enhanced MRI, the current reference standard in the setting of high-risk screening. This application of DW MRI has not been widely explored but is particularly timely given the growing health concerns related to the long-term use of gadolinium-based contrast material. Moreover, increasing breast density notification legislation across the United States is raising awareness of the limitations of mammography in women with dense breasts, emphasizing the need for additional cost-effective supplemental screening examinations. Preliminary studies suggest unenhanced MRI with DW MRI may provide higher sensitivity than screening mammography for the detection of breast malignancies. Larger prospective multicenter trials are needed to validate single-center findings and assess the performance of DW MRI for generalized breast cancer screening. Standardization of DW MRI acquisition and interpretation is essential to ensure reliable sensitivity and specificity, and an optimal approach for screening using readily available techniques is proposed here.
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Affiliation(s)
- Nita Amornsiripanitch
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Sebastian Bickelhaupt
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Hee Jung Shin
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Madeline Dang
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Habib Rahbar
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Katja Pinker
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Savannah C. Partridge
- From the Department of Breast Imaging, University of Massachusetts Memorial Medical Center, Worcester, Mass (N.A.); Medical Imaging and Radiology–Cancer Prevention, German Cancer Research Center, Heidelberg, Germany (S.B.); Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, South Korea (H.J.S.); Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (M.D., H.R., S.C.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (K.P.)
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Baltzer P, Mann RM, Iima M, Sigmund EE, Clauser P, Gilbert FJ, Martincich L, Partridge SC, Patterson A, Pinker K, Thibault F, Camps-Herrero J, Le Bihan D. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2019; 30:1436-1450. [PMID: 31786616 PMCID: PMC7033067 DOI: 10.1007/s00330-019-06510-3] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 09/03/2019] [Accepted: 10/10/2019] [Indexed: 01/03/2023]
Abstract
The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications. Key Points • The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use. • Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters. • Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.
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Affiliation(s)
- Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Ritse M Mann
- Department of Radiology, Radboud University Medical Centre, Nijmegen, Netherlands. .,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, Netherlands.
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, New York University School of Medicine, NYU Langone Health, Ney York, NY, 10016, USA
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | | | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria.,MSKCC, New York, NY, 10065, USA
| | | | | | - Denis Le Bihan
- NeuroSpin, Frédéric Joliot Institute, Gif Sur Yvette, France
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Zhao N, Ma C, Ye X, Danie N, Fu C, Hao Q, Lu J. The feasibility of b-value maps based on threshold DWI for detection of breast cancer: A case-control STROBE compliant study. Medicine (Baltimore) 2019; 98:e17640. [PMID: 31689773 PMCID: PMC6946245 DOI: 10.1097/md.0000000000017640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Diffusion-weighted imaging (DWI) plays an important role in the diagnosis of breast cancer as well as the evaluation of treatment effects. A novel technique named b-value map based on thresholded DWI images has been proposed and can achieve good contrast for demonstrating prostate lesions only by manipulating the window width and center of the images. Its application on the breast has not yet explored, so the aim of the study was to investigate the feasibility of b-value maps based on threshold DWI for detection of breast cancer. A total of 25 patients with pathologically proven invasive ductal breast carcinoma were included and underwent preoperative magnetic resonance imaging (MRI) examinations including DWI at 3T. The capabilities to display lesions of DWIb=800, b-value maps and optimal computed DWI (cDWI) images were evaluated by using a 4-point method of scoring. Apparent diffusion coefficient (ADC) values of lesions were measured for the breast carcinoma. Mean scores indicating the display capability were compared among DWIb=800, optimal cDWI and b-value maps by using Kruskal-Wallis test followed by Nemenyi test. The scores of both b-value maps (3.92 ± 0.28) and optimal cDWI images (3.80 ± 0.41) were higher than that of DWIb=800 (3.48 ± 0.51), with statistical differences (P = .001 and P = .033, respectively). The optimal b values for manifesting breast carcinoma based on cDWI were 1000 to 1200 s/mm. The b-value map enables fast identification for breast lesions and shows similar performance to the optimal cDWI images.
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Affiliation(s)
| | | | - Xiaolong Ye
- Department of Pathology, Changhai Hospital of Shanghai, The Second Military Medical University, Shanghai
| | | | - Caixia Fu
- Application Developments, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
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50
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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: 94] [Impact Index Per Article: 18.8] [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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