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Ma J, Hou L, Liang X, Yan B, Dai Q, Wang Y, Gao H, Zhu J, Song C, Yuan Q. Application value of MRI-guided wire localization to the non-palpable breast lesions only shown in Breast MRI. Front Oncol 2024; 14:1325362. [PMID: 38854734 PMCID: PMC11157007 DOI: 10.3389/fonc.2024.1325362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/30/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction Magnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women. Methods A total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed. Results All of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P<0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P<0.001). Conclusion MRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer.
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Affiliation(s)
- Jiaqi Ma
- Department of Radiology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Leina Hou
- Department of Anesthesiology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Xiufen Liang
- Department of Radiology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Bin Yan
- Department of Radiology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Qiang Dai
- Department of Radiology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Yunmei Wang
- Department of Medical Oncology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Hongbian Gao
- Department of Pathology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Jiang Zhu
- Department of Breast Cancer, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Canxu Song
- Department of Ultrasonography, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
| | - Quan Yuan
- Department of Ultrasonography, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China
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Liu D, Ba Z, Gao Y, Wang L. Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4). BMC Med Imaging 2023; 23:182. [PMID: 37950164 PMCID: PMC10636905 DOI: 10.1186/s12880-023-01144-w] [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: 05/30/2022] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND This study aims to providing a reliable method that has good compliance and is easy to master to improve the accuracy of NMLE diagnosis. METHODS This study retrospectively analyzed 122 cases of breast non-mass-like enhancement (NMLE) lesions confirmed by postoperative histology. MRI features and clinical features of benign and malignant non-mass enhancement breast lesions were compared by using independent sample t test, χ2test and Fisher exact test. P < 0.05 was considered statistically significant. Statistically significant parameters were then included in logistic regression analysis to build a multiparameter differential diagnosis modelto subdivide the BI-RADS Category 4. RESULTS The distribution (odds ratio (OR) = 8.70), internal enhancement pattern (OR = 6.29), ADC value (OR = 5.56), and vascular sign (OR = 2.84) of the lesions were closely related to the benignity and malignancy of the lesions. These signs were used to build the MRI multiparameter model for differentiating benign and malignant non-mass enhancement breast lesions. ROC analysis revealed that its optimal diagnostic cut-off value was 5. The diagnostic specificity and sensitivity were 87.01% and 82.22%, respectively. Lesions with 1-6 points were considered BI-RADS category 4 lesions, and the positive predictive values of subtypes 4a, 4b, and 4c lesions were15.79%, 31.25%, and 77.78%, respectively. CONCLUSIONS Comprehensively analyzing the features of MRI of non-mass enhancement breast lesions and building the multiparameter differential diagnosis model could improve the differential diagnostic performance of benign and malignant lesions.
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Affiliation(s)
- Dandan Liu
- Department of Radiology, The Eighth People's Hospital of Jinan, No. 68, Xinxing Road, Gangcheng District of Jinan, Jinan, Shandong, 271126, P. R. China.
| | - Zhaogui Ba
- Department of Radiology, The Eighth People's Hospital of Jinan, No. 68, Xinxing Road, Gangcheng District of Jinan, Jinan, Shandong, 271126, P. R. China
| | - Yan Gao
- Department of Radiology, The Eighth People's Hospital of Jinan, No. 68, Xinxing Road, Gangcheng District of Jinan, Jinan, Shandong, 271126, P. R. China
| | - Linhong Wang
- Department of Radiology, The Eighth People's Hospital of Jinan, No. 68, Xinxing Road, Gangcheng District of Jinan, Jinan, Shandong, 271126, P. R. China
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de Oliveira TMG. Are we ready to stratify BI-RADS 4 MRI lesions? Radiol Bras 2023; 56:V-VI. [PMID: 38504812 PMCID: PMC10948156 DOI: 10.1590/0100-3984.2023.56.6e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024] Open
Affiliation(s)
- Tatiane Mendes Gonçalves de Oliveira
- Attending Physician at the Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Radiologist at the Clínica Radiologia Especializada, Ribeirão Preto, SP, Brazil
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de Almeida JRM, Bitencourt AGV, Gomes AB, Chagas GL, Barros TP. Are we ready to stratify BI-RADS 4 lesions observed on magnetic resonance imaging? A real-world noninferiority/equivalence analysis. Radiol Bras 2023; 56:291-300. [PMID: 38504813 PMCID: PMC10948154 DOI: 10.1590/0100-3984.2023.0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/05/2023] [Accepted: 10/06/2023] [Indexed: 03/21/2024] Open
Abstract
Objective To demonstrate that positive predictive values (PPVs) for suspicious (category 4) magnetic resonance imaging (MRI) findings that have been stratified are equivalent to those stipulated in the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) for mammography and ultrasound. Materials and Methods This retrospective analysis of electronic medical records generated between January 4, 2016 and December 29, 2021 provided 365 patients in which 419 suspicious (BI-RADS category 4) findings were subcategorized as BI-RADS 4A, 4B or 4C. Malignant and nonmalignant outcomes were determined by pathologic analyses, follow-up, or both. For each subcategory, the level 2 PPV (PPV2) was calculated and tested for equivalence/noninferiority against the established benchmarks. Results Of the 419 findings evaluated, 168 (40.1%) were categorized as malignant and 251 (59.9%) were categorized as nonmalignant. The PPV2 for subcategory 4A was 14.2% (95% CI: 9.3-20.4%), whereas it was 41.2% (95% CI: 32.8-49.9%) for subcategory 4B and 77.2% (95% CI: 68.4-84.5%) for subcategory 4C. Multivariate analysis showed a significantly different cancer yield for each subcategory (p < 0.001). Conclusion We found that stratification of suspicious findings by MRI criteria is feasible, and malignancy probabilities for sub-categories 4B and 4C are equivalent to the values established for the other imaging methods in the BI-RADS. Nevertheless, low suspicion (4A) findings might show slightly higher malignancy rates.
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Li X, Fan Z, Jiang H, Niu J, Bian W, Wang C, Wang Y, Zhang R, Zhang H. Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status. Sci Rep 2023; 13:17978. [PMID: 37864025 PMCID: PMC10589282 DOI: 10.1038/s41598-023-45079-2] [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: 07/25/2022] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.
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Affiliation(s)
- Xiaojun Li
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Radiology, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China
| | - Zhichang Fan
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hongnan Jiang
- Department of Breast Surgery, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China
| | - Jinliang Niu
- Department of Radiology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Wenjin Bian
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Chen Wang
- Department of Pathology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ying Wang
- Department of Pathology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Runmei Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85, South Jiefang Road, Yingze District, Taiyuan, 030001, Shanxi, China.
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Soylu Boy FN, Esen Icten G, Kayadibi Y, Tasdelen I, Alver D. Idiopathic Granulomatous Mastitis or Breast Cancer? A Comparative MRI Study in Patients Presenting with Non-Mass Enhancement. Diagnostics (Basel) 2023; 13:diagnostics13081475. [PMID: 37189576 DOI: 10.3390/diagnostics13081475] [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: 01/17/2023] [Revised: 02/20/2023] [Accepted: 03/16/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVE To compare and determine discriminative magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis (IGM) and breast cancer (BC) that present as non-mass enhancement. MATERIALS AND METHODS This retrospective study includes 68 IGM and 75 BC cases that presented with non-mass enhancement on breast MRI. All patients with a previous history of breast surgery, radiotherapy, or chemotherapy due to BC or a previous history of mastitis were excluded. On MRI images, presence of architectural distortion skin thickening, edema, hyperintense ducts containing protein, dilated fat-containing ducts and axillary adenopathies were noted. Cysts with enhancing walls, lesion size, lesion location, fistulas, distribution, internal enhancement pattern and kinetic features of non-mass enhancement were recorded. Apparent diffusion coefficient (ADC) values were calculated. Pearson chi-square test, Fisher's exact test, independent t test and Mann-Whitney U test were used as needed for statistical analysis and comparison. Multivariate logistic regression model was used to determine the independent predictors. RESULTS IGM patients were significantly younger than BC patients (p < 0.001). Cysts with thin (p < 0.05) or thick walls (p = 0.001), multiple cystic lesions, (p < 0.001), cystic lesions draining to the skin (p < 0.001), and skin fistulas (p < 0.05) were detected more often in IGM. Central (p < 0.05) and periareolar (p < 0.001) location and focal skin thickening (p < 0.05) were significantly more common in IGM. Architectural distortion (p = 0.001) and diffuse skin thickening (p < 0.05) were associated with BC. Multiple regional distribution was more common in IGM, whereas diffuse distribution and clumped enhancement were more common in BC (p < 0.05). In kinetic analysis, persistent enhancement was more common in IGM, whereas plateau and wash-out types were more common in BC (p < 0.001). Independent predictors for BC were age, diffuse skin thickening and kinetic curve types. There was no significant difference in the diffusion characteristics. Based on these findings, MRI had a sensitivity, specificity and accuracy of 88%, 67.65%, and 78.32%, respectively, in differentiating IGM from BC. CONCLUSIONS In conclusion, for non-mass enhancement, MRI can rule out malignancy with a considerably high sensitivity; however, specificity is still low, as many IGM patients have overlapping findings. Final diagnosis should be complemented with histopathology whenever necessary.
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Affiliation(s)
- Fatma Nur Soylu Boy
- Department of Radiology, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
| | - Gul Esen Icten
- Senology Research Institute, Acibadem Mehmet Ali Aydınlar University, 34457 Istanbul, Turkey
- Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydınlar University, 34457 Istanbul, Turkey
| | - Yasemin Kayadibi
- Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, 34320 Istanbul, Turkey
| | - Iksan Tasdelen
- Department of General Surgery, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
| | - Dolunay Alver
- Department of Radiology, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
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Value of shear wave elastography during second-look breast ultrasonography for suspicious lesions on magnetic resonance imaging. J Med Ultrason (2001) 2022; 49:719-730. [DOI: 10.1007/s10396-022-01253-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/26/2022] [Indexed: 11/26/2022]
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Zhang H, Zhang XY, Wang Y. Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions. World J Clin Cases 2022; 10:6021-6031. [PMID: 35949832 PMCID: PMC9254209 DOI: 10.12998/wjcc.v10.i18.6021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
AIM To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.
METHODS This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.
RESULTS The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10-3 mm2/s and 1.24 ± 0.16 × 10-3 mm2/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10-3 mm2/s, which was lower than that of the benign group (1.42 ± 0.68 × 10-3 mm2/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (Ve) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.
CONCLUSION DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
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Affiliation(s)
- Hui Zhang
- Department of Radiology, Hebei General Hospital, Shijiazhuang 050000, Hebei Province, China
| | - Xin-Yi Zhang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
| | - Yong Wang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
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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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Cui Q, Sun L, Zhang Y, Zhao Z, Li S, Liu Y, Ge H, Qin D, Zhao Y. Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1677. [PMID: 34988186 PMCID: PMC8667137 DOI: 10.21037/atm-21-5441] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/04/2021] [Indexed: 12/14/2022]
Abstract
Background The Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions is categorized into 4A, 4B, and 4C, which reflect an increasing malignancy potential from low (2–10%) moderate (10–50%) and high (50–95%). Determining the benign and malignant of BI-RADS category 4 breast lesions is very important for accurate diagnosis and follow-up treatment. This study aimed to explore the value of breast magnetic resonance imaging (MRI) omics features and clinical characteristics in the assessment of BI-RADS category 4 breast lesions. Methods This retrospective study analyzed 96 lesions (39 benign and 57 malignant) from 92 patients diagnosed with MRI BI-RADS category 4 lesions in the Second Affiliated Hospital of Dalian Medical University between May 2017 and December 2019. The lesions were sub-categorized as BI-RADS 4A, 4B, or 4C based on the MRI findings. An imaging omics analysis model was applied to extract the MRI features. The positive predictive value (PPV) of each subcategory was calculated, and the area under the curve (AUC) was used to describe the efficiency for different diagnoses. Moreover, we analyzed 17 clinical indicators to assess their diagnostic value for BI-RADS category 4 breast lesions. Results The PPVs of BI-RADS 4A, 4B, and 4C were 7.1% (2/28), 41.2% (7/17), and 94.1% (48/51), respectively. The AUC, sensitivity, and specificity were 0.919, 84.2%, and 92.3%, respectively. The combination of T1-weighted images (T1WI) with dynamic contrast-enhanced (DCE) MRI yielded the best diagnostic results among all dual sequences. Two clinical indicators [progesterone receptor (PR) and Ki-67 expression] achieved an AUC almost equal to 1.0. The radiomics and redundancy reduction methods reduced the clinical data features from 1,233 to 14. Conclusions High diagnostic performance can be achieved in distinguishing malignant breast BI-RADS category 4 lesions using the combination of T1WI and DCE in MRI. Combining the PR and Ki-67 expression variables can further improve MRI accuracy for breast BI-RADS category 4 lesions.
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Affiliation(s)
- Qian Cui
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Liang Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Yu Zhang
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Zimu Zhao
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shuo Li
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yajie Liu
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hongwei Ge
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Dongxue Qin
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yiping Zhao
- Department of Radiology, Second Affiliated Hospital of Dalian Medical University, Dalian, China
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Yang ZL, Li Y, Zhan CA, Hu YQ, Guo YH, Xia LM, Ai T. Evaluation of suspicious breast lesions with diffusion kurtosis MR imaging and connection with prognostic factors. Eur J Radiol 2021; 145:110014. [PMID: 34749223 DOI: 10.1016/j.ejrad.2021.110014] [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: 08/26/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 12/09/2022]
Abstract
PURPOSE To investigate the additional value of DKI in discriminating suspicious breast lesions on DCE-MRI, as compared with conventional DWI; and to explore connection between DKI-parameters and prognostic factors of breast cancers. METHODS The institutional review board approved this retrospective study and written informed consent was waived. Totally, 300 women (mean age, 43.2 ± 10.4 years) with suspicious breast lesions on DCE-MRI were enrolled from November 2014 to September 2019. With pathology as reference, performance of ADC, Kapp and Dapp in discriminating suspicious breast lesions were analyzed by receiver operating characteristic (ROC) analysis with area under ROC curve (AUC). The specificities of parameters were compared by Chi-square test. The ADC, Kapp and Dapp of breast cancers with different receptor status were compared using Student's t or Mann-Whitney U or Kruskal-Wallis test. RESULTS There were 344 suspicious breast lesions (220 malignant, 124 benign) in 300 women. No significant differences were found for AUCs of ADC and DKI-parameters in discriminating suspicious breast lesions (0.882 vs. 0.888, p = 0.480). The specificities were significantly higher with ADC and Dapp than that with DCE-MRI (p = 0.003 and 0.005). The ADC, Kapp and Dapp were correlated with HER2 expression and lymph node status, and ADC and Kapp differed between ER-positive and negative tumors (all p < 0.05). Except Kapp, DKI/DWI-parameters showed relation with Ki-67 expression. None of the DKI/DWI-parameters showed relation with lesion grade (all p > 0.05). CONCLUSION The more complicated and time-consuming DKI is not superior to conventional DWI in differentiating suspicious breast lesions and reflecting prognostic information of breast cancer.
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Affiliation(s)
- Zhen Lu Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China
| | - Yan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China
| | - Chen Ao Zhan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China
| | - Yi Qi Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China
| | - Yi Hao Guo
- MR Collaboration, Siemens Healthcare Ltd., Guangzhou 510000, China
| | - Li Ming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China.
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, Hubei 430030, China.
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12
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Moreno G, Molina M, Wu R, Sullivan JR, Jorns JM. Unveiling the histopathologic spectrum of MRI-guided breast biopsies: an institutional pathological-radiological correlation. Breast Cancer Res Treat 2021; 187:673-680. [PMID: 34043124 DOI: 10.1007/s10549-021-06251-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/04/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Breast magnetic resonance imaging (MRI) has high sensitivity but suffers from low specificity, resulting in many benign breast biopsies for MRI-detected lesions. We sought to compare histologic findings between patients who underwent MRI-guided breast biopsy versus biopsy via other imaging modalities as well as to examine features associated with malignancy in the MRI cohort to help inform MRI-biopsy practice. METHODS A 2-year (2018-2019) retrospective review of breast biopsies at our enterprise was conducted. Biopsies were categorized as stereotactic, ultrasound, MRI, or palpation guided. Pathology was categorized as benign (further divided into nine categories), atypical, or malignant (subdivided into in situ and invasive carcinoma). Pathology was compared between biopsy groups. Clinical, pathologic, and imaging features were compared between pathology groups within the MRI cohort. RESULTS 5828 biopsies from 4154 patients were reviewed, including 548 MRI-guided biopsies with stratification of MRI-biopsy pathology as follows: 69% benign, 13.8% atypical, and 17.2% malignant. Among benign MRI biopsies, there was higher frequency of "clustered cysts with papillary apocrine metaplasia" (56/548; 10.2%) and lower rate of fibroadenoma/fibroadenomatous change (55/548; 10%) compared to other modalities (158 or 3% and 1144 or 21.7% of 5280 biopsies, respectively). Multivariate analysis revealed indication of breast cancer (p < .0001), ipsilateral cancer (p < .0001) and rapid initial phase kinetics (p = .017) to remain significantly associated with malignant MRI-biopsy pathology. CONCLUSIONS A concurrent or recent breast cancer diagnosis was most predictive of malignancy on MRI-guided breast biopsy. Combined MRI feature evaluation and radiologic-pathologic concordance activities may allow for prognostic refinement and improved risk stratification.
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Affiliation(s)
- Gustavo Moreno
- Department of Pathology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Lab Building, Lower Level, Room L69, Milwaukee, WI, 53226, USA
| | - Mariel Molina
- Department of Pathology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Lab Building, Lower Level, Room L69, Milwaukee, WI, 53226, USA
| | - Ruizhe Wu
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Julie R Sullivan
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Julie M Jorns
- Department of Pathology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Lab Building, Lower Level, Room L69, Milwaukee, WI, 53226, USA.
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13
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Istomin A, Masarwah A, Vanninen R, Okuma H, Sudah M. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system. Eur J Radiol 2021; 138:109659. [PMID: 33752000 DOI: 10.1016/j.ejrad.2021.109659] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/05/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE To determine the diagnostic performance of the Kaiser score and to compare it with the BI-RADS-based multiparametric classification system (MCS). METHOD Two breast radiologists, blinded to the clinical and pathological information, separately evaluated a database of 499 consecutive patients with structural 3.0 T breast MRI and 697 histopathologically verified lesions. The Kaiser scores and corresponding MCS categories were recorded. The sensitivity and specificity of the Kaiser score and the MCS categories to differentiate benign from malignant lesions were calculated. The interobserver reproducibility and receiver operating characteristic (ROC) parameters were analysed. RESULTS The sensitivity and specificity of the MCS were 100 % and 12 %, respectively, and those of the Kaiser score were 98.5 % and 34.8 % for reader 1 and 98.7 % and 47.5 % for reader 2. The area under the ROC-curve was 85.9 and 87.6 for readers 1 and 2. The interobserver intraclass correlation coefficient was excellent at 0.882. Reader 1 upgraded six lesions from BI-RADS 3 to a Kaiser score of >4, and reader 2 upgraded seven lesions. When applying the Kaiser score to 158 benign lesions readers 1 and 2 would have reduced the biopsy rate by 22.8 % and 35.4 %, respectively. CONCLUSIONS The Kaiser score showed high diagnostic accuracy with excellent interobserver reproducibility. The MCS had perfect sensitivity but low specificity. Although the Kaiser score had slightly lower sensitivity, its specificity was 3-4 times greater than that of the MCS. Thus, the Kaiser score has the potential to considerably reduce the biopsy rate for true negative lesions.
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Affiliation(s)
- Aleksandr Istomin
- Kuopio University Hospital, Diagnostic Imaging Center, Department of Clinical Radiology, Kuopio, Finland
| | - Amro Masarwah
- Kuopio University Hospital, Diagnostic Imaging Center, Department of Clinical Radiology, Kuopio, Finland
| | - Ritva Vanninen
- Kuopio University Hospital, Diagnostic Imaging Center, Department of Clinical Radiology, Kuopio, Finland; University of Eastern Finland, Cancer Center of Eastern Finland, Kuopio, Finland; University of Eastern Finland, Institute of Clinical Medicine, School of Medicine, Kuopio, Finland
| | - Hidemi Okuma
- Kuopio University Hospital, Diagnostic Imaging Center, Department of Clinical Radiology, Kuopio, Finland
| | - Mazen Sudah
- Kuopio University Hospital, Diagnostic Imaging Center, Department of Clinical Radiology, Kuopio, Finland; University of Eastern Finland, Cancer Center of Eastern Finland, Kuopio, Finland.
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14
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Istomin A, Masarwah A, Okuma H, Sutela A, Vanninen R, Sudah M. A multiparametric classification system for lesions detected by breast magnetic resonance imaging. Eur J Radiol 2020; 132:109322. [DOI: 10.1016/j.ejrad.2020.109322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/19/2020] [Accepted: 09/24/2020] [Indexed: 12/18/2022]
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15
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Hao W, Gong J, Wang S, Zhu H, Zhao B, Peng W. Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment. Front Oncol 2020; 10:531476. [PMID: 33194589 PMCID: PMC7660748 DOI: 10.3389/fonc.2020.531476] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 09/24/2020] [Indexed: 12/16/2022] Open
Abstract
Objective This study aimed to explore the potential of magnetic resonance imaging (MRI) radiomics-based machine learning to improve assessment and diagnosis of contralateral Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions in women with primary breast cancer. Materials and Methods A total of 178 contralateral BI-RADS 4 lesions (97 malignant and 81 benign) collected from 178 breast cancer patients were involved in our retrospective dataset. T1 + C and T2 weighted images were used for radiomics analysis. These lesions were randomly assigned to the training (n = 124) dataset and an independent testing dataset (n = 54). A three-dimensional semi-automatic segmentation method was performed to segment lesions depicted on T2 and T1 + C images, 1,046 radiomic features were extracted from each segmented region, and a least absolute shrinkage and operator feature selection method reduced feature dimensionality. Three support vector machine (SVM) classifiers were trained to build classification models based on the T2, T1 + C, and fusion image features, respectively. The diagnostic performance of each model was evaluated and tested using the independent testing dataset. The area under the receiver operating characteristic curve (AUC) was used as a performance metric. Results The T1+C image feature-based model and T2 image feature-based model yielded AUCs of 0.71 ± 0.07 and 0.69 ± 0.07 respectively, and the difference between them was not significant (P > 0.05). After fusing T1 + C and T2 imaging features, the proposed model’s AUC significantly improved to 0.77 ± 0.06 (P < 0.001). The fusion model yielded an accuracy of 74.1%, which was higher than that of the T1 + C (66.7%) and T2 (59.3%) image feature-based models. Conclusion The MRI radiomics-based machine learning model is a feasible method to assess contralateral BI-RADS 4 lesions. T2 and T1 + C image features provide complementary information in discriminating benign and malignant contralateral BI-RADS 4 lesions.
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Affiliation(s)
- Wen Hao
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bin Zhao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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16
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Honda M, Kataoka M, Kawaguchi K, Iima M, Miyake KK, Kishimoto AO, Ota R, Ohashi A, Toi M, Nakamoto Y. Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI. Jpn J Radiol 2020; 39:56-65. [PMID: 32870440 DOI: 10.1007/s11604-020-01029-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/09/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE Category 4 in BI-RADS for magnetic resonance imaging (MRI) has a wide range of probabilities of malignancy, extending from > 2 to < 95%. We classified category 4 lesions into three subcategories and analyzed the positive predictive value (PPV) of malignancy in a tertiary hospital. MATERIALS AND METHODS This retrospective study included 346 breast MRIs with 434 category 2-5 lesions. All enhancing lesions were classified as category 2 (0% probability of malignancy), 3 (> 0%, ≤ 2%), 4 (> 2%, < 95%) and 5 (≥ 95%); category 4 lesions were further subcategorized into 4A (> 2%, ≤ 10%), 4B (> 10%, ≤ 50%) and 4C (> 50%, < 95%) at the time of diagnosis. Radiological and pathological reports were retrospectively analyzed, and the PPVs were calculated. RESULTS We included 149 malignant and 285 benign lesions. The PPVs of subcategories 4A, 4B and 4C were 1.8%, 11.8% and 67.5%, respectively. The PPVs were higher for lesions coexisting with category 5 or 6 lesions compared with those for isolated lesions. CONCLUSION Category 4 lesions can be classified into three subcategories depending on the likelihood of malignancy. Lesions coexisting with category 5 or 6 lesions are more likely to be malignant.
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Affiliation(s)
- Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Kosuke Kawaguchi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan.,Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-7507, Japan
| | - Kanae Kawai Miyake
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Rie Ota
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Akane Ohashi
- Department of Radiology, National Hospital Organization Kyoto Medical Center, 1-1, Fukakushamukaihatacho, Fishimi-ku, Kyoto, 612-8555, Japan
| | - Masakazu Toi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
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Yang X, Dong M, Li S, Chai R, Zhang Z, Li N, Zhang L. Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions. Eur Radiol 2020; 30:4795-4805. [PMID: 32350660 DOI: 10.1007/s00330-020-06883-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 02/21/2020] [Accepted: 04/09/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions. METHODS A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p < 0.05 was considered as statistically different. External validation was used. RESULTS In the training set, the sensitivities, specificities, and AUCs of the DWI/TIC model were 95.2%/95.8%, 70.8%/47.9%, and 0.932/0.891 for masses, and 94.2%/90.4%, 47.4%/47.4%, and 0.798 (95% CI, 0.686-0.884)/0.802 (95% CI, 0.691-0.887) for NMEs, respectively. The AUC of the DWI model was significantly higher than that of the TIC model (p < 0.05) for masses. In the validation set, the AUCs of the DWI/TIC model were 0.896/0.861 for masses (p < 0.05) and 0.936/0.836 for NMEs (p > 0.05). CONCLUSIONS Combined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions. KEY POINTS • Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.
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Affiliation(s)
- Xiaoping Yang
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Mengshi Dong
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Shu Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Ruimei Chai
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Zheng Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Nan Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China
| | - Lina Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
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18
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Lee SJ, Ko KH, Jung HK, Koh JE, Park AY. The additional utility of ultrafast MRI on conventional DCE-MRI in evaluating preoperative MRI of breast cancer patients. Eur J Radiol 2020; 124:108841. [PMID: 31981877 DOI: 10.1016/j.ejrad.2020.108841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/27/2019] [Accepted: 01/13/2020] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate whether the additional use of ultrafast MRI can improve the diagnostic performance of conventional dynamic contrast-enhanced MRI (DCE-MRI) in evaluating MRI-detected lesions in breast cancer patients. METHODS This retrospective study enrolled 101 consecutive breast cancer patients with 202 breast lesions (62 benign and 140 malignant) who underwent preoperative DCE-MRI with ultrafast imaging (9 image sets with 6.5-second temporal resolution). Two reviewers assessed the BI-RADS categories of breast lesions using conventional DCE-MRI and assessed the following parameters using the ultrafast MRI: initial enhancement phase, maximum relative enhancement, slope, and maximum slope (slopemax) on the kinetic curve. Interobserver agreement was analyzed between the two reviewers. The ultrafast MRI parameters were compared between benign and malignant tumors, and cut-off values were determined. For 97 additional MRI-detected lesions, the BI-RADS category was re-assessed using cut-off values, and the diagnostic performance was compared between the conventional DCE-MRI and the combined conventional and ultrafast DCE-MRI. RESULTS All ultrafast MRI parameters differed significantly between malignant and benign tumors (p < 0.001). Initial enhancement phase by reviewer and slopemax were the top two parameters showing significant differences between benign and malignant tumors with high reliability. With the use of cut-off values for initial enhancement phase (≤phase 2) and slopemax (>9.8%/sec), the specificity of conventional DCE-MRI was significantly increased (29.4% vs 64.7%, p < 0.001) without significant loss of sensitivity (100% vs 88.2%, p = 0.157) in evaluating masses. CONCLUSIONS The additional use of ultrafast MRI can improve the specificity of conventional DCE-MRI when evaluating MRI-detected masses in breast cancer patients.
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Affiliation(s)
- Soo Jeong Lee
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
| | - Kyung Hee Ko
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
| | - Hae Kyoung Jung
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
| | - Ji Eun Koh
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
| | - Ah Young Park
- Department of Radiology, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13496, Republic of Korea.
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19
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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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20
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Roknsharifi S, Fishman MDC, Agarwal MD, Brook A, Kharbanda V, Dialani V. The role of diffusion weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence? Acad Radiol 2019; 26:923-929. [PMID: 30293819 DOI: 10.1016/j.acra.2018.09.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 08/29/2018] [Accepted: 09/06/2018] [Indexed: 12/18/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence. METHOD AND MATERIALS In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant. RESULTS Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10-6 vs 1481 ± 276 × 10-6 mm2/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status. CONCLUSION ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.
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MESH Headings
- Adenocarcinoma, Mucinous/diagnostic imaging
- Adenocarcinoma, Mucinous/pathology
- Adult
- Breast Neoplasms/diagnostic imaging
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/diagnostic imaging
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Intraductal, Noninfiltrating/diagnostic imaging
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Carcinoma, Lobular/diagnostic imaging
- Carcinoma, Lobular/pathology
- Contrast Media
- Diffusion Magnetic Resonance Imaging/methods
- Female
- Humans
- Magnetic Resonance Imaging/methods
- Middle Aged
- Neoplasm Grading
- Neoplasm Recurrence, Local/diagnostic imaging
- Neoplasms, Complex and Mixed/diagnostic imaging
- Neoplasms, Complex and Mixed/pathology
- Predictive Value of Tests
- Receptor, ErbB-2/metabolism
- Receptors, Estrogen/metabolism
- Receptors, Progesterone/metabolism
- Retrospective Studies
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Affiliation(s)
- Shima Roknsharifi
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
| | - Michael D C Fishman
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
| | - Monica D Agarwal
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
| | - Alexander Brook
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
| | - Vritti Kharbanda
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
| | - Vandana Dialani
- Department of Radiology, Beth Israel Deaconess Medical Center/Harvard Medical School, 330 Brookline Ave, Shapiro 4, Breast Imaging, Boston, MA 02215
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21
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Goto M, Le Bihan D, Yoshida M, Sakai K, Yamada K. Adding a Model-free Diffusion MRI Marker to BI-RADS Assessment Improves Specificity for Diagnosing Breast Lesions. Radiology 2019; 292:84-93. [PMID: 31112086 DOI: 10.1148/radiol.2019181780] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background The apparent diffusion coefficient (ADC) is a commonly used quantitative diffusion-weighted (DW) imaging marker in breast lesion assessment; however, reported ADC values to distinguish malignant and benign lesions show wide variability. Purpose To investigate the diagnostic performance of a tissue signature index (S-index) as a model-free diffusion marker to differentiate malignant and benign breast lesions. Materials and Methods This was a single-institution retrospective study of patients who underwent breast MRI from April 2017 to September 2018. Dynamic contrast-enhanced (DCE) MRI and DW imaging were performed with a 3-T MRI system. For DW imaging, three b values (0, 200, and 1500 sec/mm2) were used for Breast Imaging Reporting and Data Systems (BI-RADS) scoring and to calculate the S-index and a shifted ADC. The diagnostic performances of S-index, shifted ADC, and BI-RADS scoring were evaluated by using receiver operating coefficient analysis. Results The study involved 99 women (mean age, 54 years ± 14 [standard deviation]) with 69 malignant and 38 benign lesions. The S-index was higher for malignant lesions (mean, 75.9 ± 17.4) than for benign lesions (mean, 31.6 ± 21.0; P < .001). Overall diagnostic performance was identical for S-index and shifted ADC (area under the receiver operating characteristic curve [AUC], 0.95; 95% confidence interval [CI]: 0.91, 0.99) and slightly higher than for BI-RADS (AUC, 0.91; 95% CI: 0.87, 0.96; P = .22). The AUC of S-index combined with BI-RADS reached 0.98 (95% CI: 0.96, 1.00), higher than for BI-RADS alone (P < .001), yielding high sensitivity (65 of 69 [94%]; 95% CI: 85%, 98%) and specificity (36 of 38 [95%]; 95% CI: 81%, 99%). Significant differences were identified with the S-index for progesterone receptor and human epidermal growth factor receptor type 2 status (P = .003 and P < .001, respectively). Conclusion The signature index has the potential to enable classification of breast lesion types with high accuracy, especially in combination with dynamic contrast-enhanced MRI and correlates with histologic prognostic factors in invasive breast cancer. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Mariko Goto
- From the Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto 602-8566, Japan (M.G., M.Y., K.S., K.Y.); and NeuroSpin, Gif-sur-Yvette, France (D.L.B.)
| | - Denis Le Bihan
- From the Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto 602-8566, Japan (M.G., M.Y., K.S., K.Y.); and NeuroSpin, Gif-sur-Yvette, France (D.L.B.)
| | - Mariko Yoshida
- From the Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto 602-8566, Japan (M.G., M.Y., K.S., K.Y.); and NeuroSpin, Gif-sur-Yvette, France (D.L.B.)
| | - Koji Sakai
- From the Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto 602-8566, Japan (M.G., M.Y., K.S., K.Y.); and NeuroSpin, Gif-sur-Yvette, France (D.L.B.)
| | - Kei Yamada
- From the Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto 602-8566, Japan (M.G., M.Y., K.S., K.Y.); and NeuroSpin, Gif-sur-Yvette, France (D.L.B.)
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Grimm LJ, Enslow M, Ghate SV. Solitary, Well-Circumscribed, T2 Hyperintense Masses on MRI Have Very Low Malignancy Rates. JOURNAL OF BREAST IMAGING 2019; 1:37-42. [PMID: 38424872 DOI: 10.1093/jbi/wby014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
OBJECTIVE The purpose of this study was to determine the malignancy rate of solitary MRI masses with benign BI-RADS descriptors. METHODS A retrospective review was conducted of all breast MRI reports that described a mass with a final BI-RADS assessment of 3, 4, or 5, from February 1, 2005, through February 28, 2014 (n = 1510). Studies were excluded if the mass was not solitary, did not meet formal criteria for a mass, or had classically suspicious BI-RADS features (e.g., washout kinetics, and spiculated margin). The masses were reviewed by 2 fellowship-trained breast radiologists who reported consensus BI-RADS mass margin, shape, internal-enhancement, and kinetics descriptors. The T2 signal was reported as hyperintense if equal to or greater than the signal intensity of the axillary lymph nodes. Pathology results or 2 years of imaging follow-up were recorded. Comparisons were made between mass descriptors and clinical outcomes. RESULTS There were 127 women with 127 masses available for analysis. There were 76 (60%) masses that underwent biopsy for an overall malignancy rate of 4% (5/127): 2 ductal carcinoma in situ (DCIS) and 3 invasive ductal carcinoma. The malignancy rate was 2% (1/59) for T2 hyperintense solitary masses. The malignancy rate was greater than 2% for all of the following BI-RADS descriptors: oval (3%, 3/88), round (5%, 2/39), circumscribed (4%, 5/127), homogeneous (4%, 3/74), and dark internal septations (4%, 2/44). CONCLUSION T2 hyperintense solitary masses without associated suspicious features have a low malignancy rate, and they could be considered for a BI-RADS 3 final assessment.
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Affiliation(s)
- Lars J Grimm
- Duke University Medical Center, Department of Radiology, Durham, NC
| | - Michael Enslow
- Duke University Medical Center, Department of Radiology, Durham, NC
| | - Sujata V Ghate
- Duke University Medical Center, Department of Radiology, Durham, NC
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Goto M, Sakai K, Yokota H, Kiba M, Yoshida M, Imai H, Weiland E, Yokota I, Yamada K. Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesions. Eur Radiol 2018; 29:1164-1174. [DOI: 10.1007/s00330-018-5643-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 06/14/2018] [Accepted: 06/29/2018] [Indexed: 12/29/2022]
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Shi RY, Yao QY, Wu LM, Xu JR. Breast Lesions: Diagnosis Using Diffusion Weighted Imaging at 1.5T and 3.0T—Systematic Review and Meta-analysis. Clin Breast Cancer 2018; 18:e305-e320. [DOI: 10.1016/j.clbc.2017.06.011] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 05/20/2017] [Accepted: 06/24/2017] [Indexed: 12/26/2022]
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Diagnostic Usefulness of Combination of Diffusion-weighted Imaging and T2WI, Including Apparent Diffusion Coefficient in Breast Lesions: Assessment of Histologic Grade. Acad Radiol 2018; 25:643-652. [PMID: 29339079 DOI: 10.1016/j.acra.2017.11.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 10/31/2017] [Accepted: 11/10/2017] [Indexed: 01/13/2023]
Abstract
PURPOSE This study aimed to compare the diagnostic values of a combination of diffusion-weighted imaging and T2-weighted imaging (DWI-T2WI) with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and to evaluate the correlation of DWI with the histologic grade in breast cancer. MATERIALS AND METHODS This study evaluated a total of 169 breast lesions from 136 patients who underwent both DCE-MRI and DWI (b value, 1000s/mm2). Morphologic and kinetic analyses for DCE-MRI were classified according to the Breast Imaging-Reporting and Data System. For the DWI-T2WI set, a DWI-T2WI score for lesion characterization that compared signal intensity of DWI and T2WI (benign: DWI-T2WI score of 1, 2; malignant: DWI-T2WI score of 3, 4, 5) was used. The diagnostic values of DCE-MRI, DWI-T2WI set, and combined assessment of DCE and DWI-T2WI were calculated. RESULTS Of 169 breast lesions, 48 were benign and 121 were malignant (89 invasive ductal carcinoma, 24 ductal carcinoma in situ, 4 invasive lobular carcinoma, 4 mucinous carcinoma). The mean apparent diffusion coefficient (ADC) of invasive ductal carcinoma (0.92 ± 0.19 × 10-3 mm2/s) and ductal carcinoma in situ (1.11 ± 0.13 × 10-3 mm2/s) was significantly lower than the value seen in benign lesions (1.36 ± 0.22 × 10-3 mm2/s). The specificity, positive predictive value (PPV), and accuracy of DWI-T2WI set and combined assessment of DCE and DWI-T2WI (specificity, 87.5% and 91.7%; PPV, 94.3% and 96.2%; accuracy, Az = 0.876 and 0.922) were significantly higher than those of the DCE-MRI (specificity, 45.8%; PPV, 81.7%; accuracy, Az = 0.854; P < .05). A low ADC value and the presence of rim enhancement were associated with a higher histologic grade cancer (P < .05). CONCLUSION Combining DWI, T2WI, and ADC values provides increased accuracy for differentiation between benign and malignant lesions, compared with DCE-MRI. A lower ADC value was associated with a higher histologic grade cancer.
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Grading System to Categorize Breast MRI in BI-RADS 5th Edition: A Multivariate Study of Breast Mass Descriptors in Terms of Probability of Malignancy. AJR Am J Roentgenol 2018; 210:W118-W127. [PMID: 29381382 DOI: 10.2214/ajr.17.17926] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [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 is to analyze the association between the probability of malignancy and breast mass descriptors in the BI-RADS 5th edition and to devise criteria for grading mass lesions, including subcategorization of category 4 lesions with or without apparent diffusion coefficient (ADC) values. MATERIALS AND METHODS A total of 519 breast masses in 499 patients were selected. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel dedicated breast radiofrequency coil. Two radiologists determined the morphologic and kinetic features of the breast masses. Mean ADC values were measured on ADC maps by placing round ROIs that encircled the largest possible solid mass portions. An optimal ADC threshold was chosen to maximize the Youden index. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. RESULTS A total of 472 masses were malignant. Multivariate model analysis showed that shape (irregular, p < 0.001), margin type (not circumscribed, p < 0.001), internal enhancement (rim enhancement and heterogeneous enhancement, p = 0.0001), and delayed phase (washout, p = 0.0003) were the significant explanatory variables. The 3-point scoring system for findings suspicious for malignancy and the proposed classification system for breast mass descriptors (with points for category designation ranging from 0 to > 4) were significant with respect to malignancy (p < 0.01). The inclusion of ADC values improved the positive predictive values for categories 3, 4A, and 4B. CONCLUSION The 3-point scoring system for findings suspicious for malignancy and the proposed classification system for breast mass descriptors would be valid as a categorization system. ADC values may be used to downgrade benign lesions in categories 3, 4A, and 4B.
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Elezaby M, Li G, Bhargavan-Chatfield M, Burnside ES, DeMartini WB. ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database. Radiology 2018; 287:416-422. [PMID: 29315061 DOI: 10.1148/radiol.2017170770] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ2 test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Mai Elezaby
- From the Department of Radiology (M.E., E.S.B.), Department of Biostatistics and Medical Informatics (G.L.), and Carbone Comprehensive Cancer Center (E.S.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; American College of Radiology, Reston, Va (M.B.); and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.)
| | - Geng Li
- From the Department of Radiology (M.E., E.S.B.), Department of Biostatistics and Medical Informatics (G.L.), and Carbone Comprehensive Cancer Center (E.S.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; American College of Radiology, Reston, Va (M.B.); and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.)
| | - Mythreyi Bhargavan-Chatfield
- From the Department of Radiology (M.E., E.S.B.), Department of Biostatistics and Medical Informatics (G.L.), and Carbone Comprehensive Cancer Center (E.S.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; American College of Radiology, Reston, Va (M.B.); and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.)
| | - Elizabeth S Burnside
- From the Department of Radiology (M.E., E.S.B.), Department of Biostatistics and Medical Informatics (G.L.), and Carbone Comprehensive Cancer Center (E.S.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; American College of Radiology, Reston, Va (M.B.); and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.)
| | - Wendy B DeMartini
- From the Department of Radiology (M.E., E.S.B.), Department of Biostatistics and Medical Informatics (G.L.), and Carbone Comprehensive Cancer Center (E.S.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792; American College of Radiology, Reston, Va (M.B.); and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (W.B.D.)
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Hu B, Xu K, Zhang Z, Chai R, Li S, Zhang L. A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings. Chin J Cancer Res 2018; 30:432-438. [PMID: 30210223 PMCID: PMC6129569 DOI: 10.21147/j.issn.1000-9604.2018.04.06] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Objective To develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI). Methods Eighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI. Results A total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency. Conclusions ADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.
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Affiliation(s)
- Bin Hu
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China.,Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Ke Xu
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Zheng Zhang
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Ruimei Chai
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Shu Li
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Lina Zhang
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
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Kawai M, Kataoka M, Kanao S, Iima M, Onishi N, Ohashi A, Sakaguchi R, Toi M, Togashi K. The Value of Lesion Size as an Adjunct to the BI-RADS-MRI 2013 Descriptors in the Diagnosis of Solitary Breast Masses. Magn Reson Med Sci 2017; 17:203-210. [PMID: 29213007 PMCID: PMC6039786 DOI: 10.2463/mrms.mp.2017-0024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose: This study aimed to evaluate the MRI findings of breast solitary masses in diagnostic procedures to decide the appropriate category based on American College of Radiology (ACR) BI-RADS-MRI 2013, with the focus on lesion size. Methods: A retrospective review of 2,603 consecutive breast MRI reports identified 250 pathologically-proven solitary breast masses. Dynamic-contrast enhanced images and diffusion-weighted images were performed on a 3.0/1.5 Tesla Scanner with a 16/4 channel dedicated breast coil. MRI findings were re-evaluated according to ACR BI-RADS-MRI 2013. BI-RADS-MRI descriptors, lesion size and minimum apparent diffusion coefficient (ADC) value were statistically analyzed using univariate/multivariate logistic regression analysis and receiver operator characteristic (ROC) analysis. Based on the results, a diagnostic decision tree was constructed. Results: Of the 250 lesions, 152 (61%) were malignant and 98 (39%) were benign. In univariate logistic regression analysis, most of the BI-RADS descriptors, lesion size, and ADC value were significant. Lesion size and ADC value were binarized with optimal cut-off values of 12 mm and 1.1 × 10−3 mm2/s, respectively. Multivariate logistic regression analysis showed that lesion size (≥12 mm or not), margin (circumscribed or not), kinetics (washout or not) and internal enhancement characteristics (IEC) (rim enhancement present or absent) significantly contributed to the diagnosis (P < 0.05). Using these four significant parameters, a decision tree was constructed to categorize lesions into detailed assessment categories/subcategories (Category 4A, 4B, 4C and 5). Conclusion: Lesion size is an independent contributor in diagnosing solitary breast masses. Adding the information of lesion size to BI-RADS-MRI 2013 descriptors will allow more detailed categorizations.
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Affiliation(s)
- Makiko Kawai
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Shotaro Kanao
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Natsuko Onishi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Akane Ohashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Rena Sakaguchi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Masakazu Toi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
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Work in Progress: Subdividing MRI BI-RADS Category 4 Assessment. AJR Am J Roentgenol 2017; 209:W401. [DOI: 10.2214/ajr.17.18585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI. AJR Am J Roentgenol 2017; 208:1392-1399. [PMID: 28792802 DOI: 10.2214/ajr.16.16730] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE BI-RADS for mammography and ultrasound subdivides category 4 assessments by likelihood of malignancy into categories 4A (> 2% to ≤ 10%), 4B (> 10% to ≤ 50%), and 4C (> 50% to < 95%). Category 4 is not subdivided for breast MRI because of a paucity of data. The purpose of the present study is to determine the utility of categories 4A, 4B, and 4C for MRI by calculating their positive predictive values (PPVs) and comparing them with BI-RADS-specified rates of malignancy for mammography and ultrasound. MATERIALS AND METHODS All screening breast MRI examinations performed from July 1, 2010, through June 30, 2013, were included in this study. We identified in medical records prospectively assigned MRI BI-RADS categories, including category 4 subdivisions, which are used routinely in our practice. Benign versus malignant outcomes were determined by pathologic analysis, findings from 12 months or more clinical or imaging follow-up, or a combination of these methods. Distribution of BI-RADS categories and positive predictive value level 2 (PPV2; based on recommendation for tissue diagnosis) for categories 4 (including its subdivisions) and 5 were calculated. RESULTS Of 860 screening breast MRI examinations performed for 566 women (mean age, 47 years), 82 with a BI-RADS category 4 assessment were identified. A total of 18 malignancies were found among 84 category 4 and 5 assessments, for an overall PPV2 of 21.4% (18/84). For category 4 subdivisions, PPV2s were as follows: for category 4A, 2.5% (1/40); for category 4B, 27.6% (8/29); for category 4C, 83.3% (5/6); and for category 4 (not otherwise specified), 28.6% (2/7). CONCLUSION Category 4 subdivisions for MRI yielded malignancy rates within BI-RADS-specified ranges, supporting their use for benefits to patient care and more meaningful practice audits.
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de Almeida JRM, Gomes AB, Barros TP, Fahel PE, Rocha MDS. Diffusion-weighted imaging of suspicious (BI-RADS 4) breast lesions: stratification based on histopathology. Radiol Bras 2017; 50:154-161. [PMID: 28670026 PMCID: PMC5487229 DOI: 10.1590/0100-3984.2015.0224] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Objective: To test the use of diffusion-weighted imaging (DWI) in stratifying suspicious
breast lesions (BI-RADS 4), correlating them with histopathology. We also
investigated the performance of DWI related to the main enhancement patterns
(mass and non-mass) and tested its reproducibility. Materials and Methods: Seventy-six patients presented 92 lesions during the sampling period. Two
independent examiners reviewed magnetic resonance imaging studies, described
the lesions, and determined the apparent diffusion coefficient (ADC) values.
Differences among benign, indeterminate- to high-risk, and malignant
findings, in terms of the ADCs, were assessed by analysis of variance. Using
receiver operating characteristic (ROC) curves, we compared the performance
of ADC values in masses and non-mass lesions, and tested the reproducibility
of measurements by determining the coefficient of variation and smallest
real difference. Results: Among the 92 lesions evaluated, the histopathology showed that 37 were
benign, 11 were indeterminate- to high-risk, and 44 were malignant. The mean
ADC differed significantly among those histopathological groups, the value
obtained for the malignant lesions (1.10 × 10-3
mm2/s) being significantly lower than that obtained for the
other groups (p < 0.001). ROC curves demonstrated that DWI performed
better when applied to masses than when applied to non-mass lesions (area
under the curve, 0.88 vs. 0.67). Reproducibility was good (coefficient of
variation, 7.03%; and smallest real difference, ± 0.242 ×
10-3 mm2/s). Conclusion: DWI can differentiate between malignant and nonmalignant (benign or
indeterminate- to high-risk) lesions, showing better performance for masses.
Nevertheless, stratification based on histopathological criteria that are
more refined has yet to be achieved.
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Affiliation(s)
| | - André Boechat Gomes
- MD, Radiologist, Department of Diagnostic Imaging, Clínica de Assistência à Mulher - Grupo CAM, Salvador, BA, Brazil
| | - Thomas Pitangueira Barros
- BMSc, Clínica de Assistência à Mulher - Grupo CAM, Department of Biomedicine, Escola Bahiana de Medicina e Saúde Pública - Campus Brotas, Salvador, BA, Brazil
| | - Paulo Eduardo Fahel
- MD, Pathologist, Clínica de Assistência à Mulher - Grupo CAM, Salvador, BA, Brazil
| | - Mario de Souza Rocha
- MD, PhD, Department of Medicine, Escola Bahiana de Medicina e Saúde Pública - Campus Brotas, Salvador, BA, Brazil
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Prevalence and Predictive Value of BI-RADS 3, 4, and 5 Lesions Detected on Breast MRI: Correlation with Study Indication. Acad Radiol 2017; 24:435-441. [PMID: 27955878 DOI: 10.1016/j.acra.2016.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/31/2016] [Accepted: 11/01/2016] [Indexed: 12/31/2022]
Abstract
RATIONALE AND OBJECTIVES This study aims to determine the prevalence and predictive value of Breast Imaging Reporting and Data System (BI-RADS) 3, 4, and 5 findings on breast magnetic resonance imaging (MRI) and to evaluate the impact of study indication on the predictive value of BI-RADS categories. MATERIALS AND METHODS This institutional review board approved, Health Insurance Portability and Accountability Act (HIPAA) compliant retrospective review of our breast MRI database from 2009 to 2011, of 5778 contrast-enhanced studies in 3360 patients was performed. At our institution, each breast receives an individual BI-RADS assessment. Breast MRI reports and electronic medical records were reviewed to obtain BI-RADS assessment, patient demographics, and outcomes. Univariate analysis was performed with Fisher exact and chi-square tests. RESULTS A total of 9216 BI-RADS assessments were assigned during the study period: 7879 (85.5%) BI-RADS 1 and 2, 567 (6.2%) BI-RADS 3, 715 (7.8%) BI-RADS 4, and 55 (0.6%) BI-RADS 5 assessments. The frequency of BI-RADS 3, 4, and 5 assessments was higher in studies performed for diagnostic (7.8%, 14.6%, 1.6%, respectively) than screening (5.2%, 4.0%, 0.1%) indications (P < 0.01). A total of 663 BI-RADS 4 and 5 lesions were biopsied with 209 (31.5%) malignant and 454 (68.5%) benign outcomes. The overall cancer rate for BI-RADS 3 findings was 1.9% (11 of 567) with no difference observed by study indication (diagnostic, 1.6%; screening, 2.3%; P = 0.76). The positive predictive value (PPV2) of BI-RADS 4 and 5 was higher for diagnostic (29.1%, 154 of 530) than for screening (22.9%, 55 of 240) indications. CONCLUSIONS Abnormal interpretation rates and PPV2 for MRIs performed for diagnostic indications are higher than for screening indications. Similar to mammography, breast MRI audits should be separated by study indication.
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Durur-Subasi I, Durur-Karakaya A, Karaman A, Seker M, Demirci E, Alper F. Is the necrosis/wall ADC ratio useful for the differentiation of benign and malignant breast lesions? Br J Radiol 2017; 90:20160803. [PMID: 28339285 DOI: 10.1259/bjr.20160803] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To determine whether the necrosis/wall apparent diffusion coefficient (ADC) ratio is useful for the malignant-benign differentiation of necrotic breast lesions. METHODS Breast MRI was performed using a 3-T system. In this retrospective study, calculation of the necrosis/wall ADC ratio was based on ADC values measured from the necrosis and from the wall of malignant and benign breast lesions by diffusion-weighted imaging (DWI). By synchronizing post-contrast T1 weighted images, the separate parts of wall and necrosis were maintained. All the diagnoses were pathologically confirmed. Statistical analyses were conducted using an independent sample t-test and receiver operating characteristic analysis. The intraclass and interclass correlations were evaluated. RESULTS A total of 66 female patients were enrolled, 38 of whom had necrotic breast carcinomas and 28 of whom had breast abscesses. The ADC values were obtained from both the wall and necrosis. The mean necrosis/wall ADC ratio (± standard deviation) was 1.61 ± 0.51 in carcinomas, and it was 0.65 ± 0.33 in abscesses. The area under the curve values for necrosis ADC, wall ADC and the necrosis/wall ADC ratio were 0.680, 0.068 and 0.942, respectively. A wall/necrosis ADC ratio cut-off value of 1.18 demonstrated a sensitivity of 97%, specificity of 93%, a positive-predictive value of 95%, a negative-predictive value of 96% and an accuracy of 95% in determining the malignant nature of necrotic breast lesions. There was a good intra- and interclass reliability for the ADC values of both necrosis and wall. CONCLUSION The necrosis/wall ADC ratio appears to be a reliable and promising tool for discriminating breast carcinomas from abscesses using DWI. Advances in knowledge: ADC values of the necrosis obtained by DWI are valuable for malignant-benign differentiation in necrotic breast lesions. The necrosis/wall ADC ratio appears to be a reliable and promising tool in the breast imaging field.
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Affiliation(s)
- Irmak Durur-Subasi
- 1 Department of Radiology, Diskapi Yildirim Beyazit Training and Research Hospital, Ankara, Turkey
| | - Afak Durur-Karakaya
- 2 Department of Radiology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Adem Karaman
- 3 Department of Radiology, Faculty of Medicine, Ataturk University, Erzurum, Turkey
| | - Mehmet Seker
- 2 Department of Radiology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Elif Demirci
- 4 Department of Pathology, Faculty of Medicine, Ataturk University, Erzurum, Turkey
| | - Fatih Alper
- 3 Department of Radiology, Faculty of Medicine, Ataturk University, Erzurum, Turkey
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Bitencourt AGV. Subdividing BI-RADS category 4 breast lesions observed on magnetic resonance imaging: Is it feasible? Radiol Bras 2016; 49:V. [PMID: 27403028 PMCID: PMC4938441 DOI: 10.1590/0100-3984.2016.49.3e1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Dijkstra H, Dorrius MD, Wielema M, Pijnappel RM, Oudkerk M, Sijens PE. Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions. J Magn Reson Imaging 2016; 44:1642-1649. [PMID: 27273694 DOI: 10.1002/jmri.25331] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 05/19/2016] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess if specificity can be increased when semiautomated breast lesion analysis of quantitative diffusion-weighted imaging (DWI) is implemented after dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) in the workup of BI-RADS 3 and 4 breast lesions larger than 1 cm. MATERIALS AND METHODS In all, 120 consecutive patients (mean-age, 48 years; age range, 23-75 years) with 139 breast lesions (≥1 cm) were examined (2010-2014) with 1.5T DCE-MRI and DWI (b = 0, 50, 200, 500, 800, 1000 s/mm2 ) and the BI-RADS classification and histopathology were obtained. For each lesion malignancy was excluded using voxelwise semiautomated breast lesion analysis based on previously defined thresholds for the apparent diffusion coefficient (ADC) and the three intravoxel incoherent motion (IVIM) parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). The sensitivity (Se), specificity (Sp), and negative predictive value (NPV) based on only IVIM parameters combined in parallel (Dslow , Dfast , and ffast ), or the ADC or the BI-RADS classification by DCE-MRI were compared. Subsequently, the Se, Sp, and NPV of the combination of the BI-RADS classification by DCE-MRI followed by the IVIM parameters in parallel (or the ADC) were compared. RESULTS In all, 23 of 139 breast lesions were benign. Se and Sp of DCE-MRI was 100% and 30.4% (NPV = 100%). Se and Sp of IVIM parameters in parallel were 92.2% and 52.2% (NPV = 57.1%) and for the ADC 95.7% and 17.4%, respectively (NPV = 44.4%). In all, 26 of 139 lesions were classified as BI-RADS 3 (n = 7) or BI-RADS 4 (n = 19). DCE-MRI combined with ADC (Se = 99.1%, Sp = 34.8%) or IVIM (Se = 99.1%, Sp = 56.5%) did significantly improve (P = 0.016) Sp of DCE-MRI alone for workup of BI-RADS 3 and 4 lesions (NPV = 92.9%). CONCLUSION Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649.
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Affiliation(s)
- Hildebrand Dijkstra
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands
| | - Monique D Dorrius
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands
| | - Mirjam Wielema
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands.,University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands
| | - Ruud M Pijnappel
- University of Utrecht, University Medical Center Utrecht, Department of Radiology, Utrecht, The Netherlands
| | - Matthijs Oudkerk
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands
| | - Paul E Sijens
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands
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