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Zhou J, Liu H, Miao H, Ye S, He Y, Zhao Y, Chen Z, Zhang Y, Liu YL, Pan Z, Su MY, Wang M. Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. Eur Radiol 2024:10.1007/s00330-024-10922-1. [PMID: 38990324 DOI: 10.1007/s00330-024-10922-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 03/23/2024] [Accepted: 05/28/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions. METHODS A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed. RESULTS When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity. CONCLUSION The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved. CLINICAL RELEVANCE STATEMENT KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity. KEY POINTS KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.
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Affiliation(s)
- Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Huiru Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiwei Miao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuxin Ye
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yun He
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Youfan Zhao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Zhifang Pan
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, US.
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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Liu C, Sun M, Arefan D, Zuley M, Sumkin J, Wu S. Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study. Breast Cancer Res 2024; 26:82. [PMID: 38790005 PMCID: PMC11127450 DOI: 10.1186/s13058-024-01830-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies. MATERIALS AND METHODS This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients. RESULTS A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively. CONCLUSION Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress.
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Affiliation(s)
- Chang Liu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Min Sun
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15215, USA
| | - Dooman Arefan
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Margarita Zuley
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
- Magee-Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
| | - Jules Sumkin
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
- Magee-Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
| | - Shandong Wu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
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Ramli Hamid MT, Ab Mumin N, Abdul Hamid S, Ahmad Saman MS, Rahmat K. Abbreviated breast magnetic resonance imaging (MRI) or digital breast tomosynthesis for breast cancer detection in dense breasts? A retrospective preliminary study with comparable results. Clin Radiol 2024; 79:e524-e531. [PMID: 38267349 DOI: 10.1016/j.crad.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 11/08/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024]
Abstract
AIM To compare the diagnostic performance of abbreviated breast magnetic resonance (AB-MR) imaging (MRI) and digital breast tomosynthesis (DBT) for breast cancer detection in Malaysian women with dense breasts, using histopathology as the reference standard. MATERIALS AND METHODS This was a single-centre cross-sectional study of 115 women with American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BIRADS) breast density C and D on DBT with breast lesions who underwent AB-MR from June 2018 to December 2021. AB-MR was performed on a 3 T MRI system with an imaging protocol consisting of three sequences: axial T1 fat-saturated unenhanced; axial first contrast-enhanced; and subtracted first contrast-enhanced with maximum intensity projection (MIP). DBT and AB-MR images were evaluated by two radiologists blinded to the histopathology and patient outcomes. Diagnostic accuracy (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) was assessed. RESULT Of the 115 women, the mean age was 50.6 years. There were 48 (41.7%) Malay, 54 (47%) Chinese, and 12 (10.4%) Indian women. The majority (n=87, 75.7%) were from the diagnostic population. Sixty-one (53.1%) were premenopausal and 54 (46.9%) postmenopausal. Seventy-eight (72.4%) had an increased risk of developing breast cancer. Ninety-one (79.1%) women had density C and 24 (20.9%) had density D. There were 164 histopathology-proven lesions; 69 (42.1%) were malignant and 95 (57.9%) were benign. There were 62.8% (n=103/164) lesions detected at DBT. All the malignant lesions 100% (n=69) and 35.7% (n=34) of benign lesions were detected. Of the 61 lesions that were not detected, 46 (75.4%) were in density C, and 15 (24.6%) were in density D. The sensitivity, specificity, PPV, and NPV for DBT were 98.5%, 34.6%, 66.3%, and 94.7%, respectively. There were 65.2% (n=107/164) lesions detected on AB-MR, with 98.6% (n=68) malignant and 41.1% (39) benign lesions detected. The sensitivity, specificity, PPV, and NPV for AB-MR were 98.5%, 43.9%, 67.2%, and 96.2%, respectively. One malignant lesion (0.6%), which was a low-grade ductal carcinoma in-situ (DCIS), was missed on AB-MR. CONCLUSION The present findings suggest that both DBT and AB-MR have comparable effectiveness as an imaging method for detecting breast cancer and have high NPV for low-risk lesions in women with dense breasts.
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Affiliation(s)
- M T Ramli Hamid
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
| | - N Ab Mumin
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - S Abdul Hamid
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
| | - M S Ahmad Saman
- Department of Public Health, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - K Rahmat
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia
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Bălan MR, Lascu LC, Dumitrescu D, Osiac E, Dumitrescu CI, Băilescu I, Moraru MC, Măceș S, Liliac IM, Popescu M. Breast MRI-the Importance of Type II and III Dynamic Curves Evaluation and Framing in BI-RADS 4C and 5 Score. CURRENT HEALTH SCIENCES JOURNAL 2024; 50:45-52. [PMID: 38854420 PMCID: PMC11157347 DOI: 10.12865/chsj.50.01.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/30/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Breast Magnetic Resonance Imaging (MRI) offers the highest sensitivity in detecting breast cancer among existing clinical and imaging techniques, making it a crucial component of breast imaging protocols. This study aims to investigate MRI importance in correlation with previous imaging discordant procedures performed as echography and/or mammography to evaluate characteristics and framing in high-risk BI-RADS 4C or 5 categories based on morphological features and kinetic curves of masses found in the breasts of patients from our database. METHODS A retrospective study with related statistical analysis was performed on a group of 33 cases, selected from a total of 488 patients who underwent breast MRI examinations at SPAD Imaging International S.R.L. Craiova, between 01.01.2021 and 31.12.2023, aged between 33 and 75 years. In all patients, MRI images parameters were analysed. RESULTS In 33 patients, 23 had a single lesion and 10 had multiple lesions, 9 of them in the ipsilateral breast and, as a particularity, one of them, located in the contralateral breast. In 21 of the total patients with multiple or single lesions they had type III curves, which were classified in the BI-RADS 5 category, considering both criteria-morphology and type of curve, where the other previous techniques had not mentioned an increased risk, hence revealing that the situation in a percentage of 63.63 in the case of MRI investigation proved to be clearly superior. CONCLUSION Combining both kinetic and morphologic criteria can enhance the diagnostic accuracy of MRI in breast lesion evaluation.
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Affiliation(s)
- Marian Răzvan Bălan
- PhD student, Doctoral School, University of Medicine and Pharmacy of Craiova, Romania
| | - Luana Corina Lascu
- Department of Radiology and Medical Imaging, University Emergency County Clinical Hospital, Craiova, Romania
- Department of Radiology and Medical Imaging University of Medicine and Pharmacy of Craiova, Romania
- SPAD IMAGING INTERNATIONAL Center Craiova, 200352 Craiova, Romania
| | - Daniela Dumitrescu
- Department of Radiology and Medical Imaging, University Emergency County Clinical Hospital, Craiova, Romania
- Department of Radiology and Medical Imaging University of Medicine and Pharmacy of Craiova, Romania
- SPAD IMAGING INTERNATIONAL Center Craiova, 200352 Craiova, Romania
| | - Eugen Osiac
- Department of Biophysics, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | | | - Iulia Băilescu
- SPAD IMAGING INTERNATIONAL Center Craiova, 200352 Craiova, Romania
| | | | - Suzana Măceș
- Department of Radiology and Medical Imaging, University Emergency County Clinical Hospital, Craiova, Romania
- SPAD IMAGING INTERNATIONAL Center Craiova, 200352 Craiova, Romania
| | - Ilona Mihaela Liliac
- Department of Histology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Mihai Popescu
- Department of Radiology and Medical Imaging, University Emergency County Clinical Hospital, Craiova, Romania
- Department of Radiology and Medical Imaging University of Medicine and Pharmacy of Craiova, Romania
- SPAD IMAGING INTERNATIONAL Center Craiova, 200352 Craiova, Romania
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Zhuang L, Lian C, Wang Z, Zhang X, Wu Z, Huang R. Breast-lesion assessment using amide proton transfer-weighted imaging and dynamic contrast-enhanced MR imaging. Radiol Oncol 2023; 57:446-454. [PMID: 38038421 PMCID: PMC10690748 DOI: 10.2478/raon-2023-0051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/01/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Previous studies have indicated that amide proton transfer-weighted imaging (APTWI) could be utilized for differentiating benign and malignant tumors. The APTWI technology has increasingly being applied to breast tumor research in recent years. However, according to the latest literature retrieval, no relevant previous studies compared the value of APTWI and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in distinguishing benign lesions from malignant lesions. In the present study, the application of APTWI and DCE for differentiating the benign and malignant breast lesions was investigated. PATIENTS AND METHODS APTWI was performed on 40 patients (42 lesions) who were enrolled in this prospective study. The lesions were split into two groups, one with malignant breast lesions (n = 28) and the other with benign breast lesions (n = 14), based on the results of the histology. The measured image characteristics (APT value, apparent diffusion coefficient [ADC] value, and time-of-intensity-curve [TIC] type) were compared between the two groups, and the ROC curve was used to quantify the diagnostic performance on the basis of these factors. The correlation between the APT values and the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression levels and histological grades was examined using Spearman's correlation coefficient. RESULTS The measured APT and ADC values showed a strong inter-observer agreement according to the intraclass correlation coefficients (0.954 and 0.825). Compared to benign lesions, malignant lesions had significantly higher APT values (3.18 ± 1.07 and 2.01 ± 0.51, p < 0.001). Based on APTWI, DCE, diffusion-weighted imaging (DWI), and ADC + APTWI, ADC + DCE, and DCE + APTWI, the area-under-the-curve values were 0.915, 0.815, 0.878, 0.921, 0.916, and 0.936, respectively. CONCLUSIONS APTWI is a potentially promising method in differentiating benign and malignant breast lesions, and may it become a great substitute for DCE examination in the future.
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Affiliation(s)
- Lulu Zhuang
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
- Shantou University, Shantou University Medical College, Shantou, China
| | - Chun Lian
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
- Shantou University, Shantou University Medical College, Shantou, China
| | - Zehao Wang
- Shantou University, Shantou University Medical College, Shantou, China
| | - Ximin Zhang
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
- Shantou University, Shantou University Medical College, Shantou, China
| | - Zhigang Wu
- Clinical & Technical Support, Philips Healthcare (Shenzhen) Ltd., China
| | - Rong Huang
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, 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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Ezeana CF, He T, Patel TA, Kaklamani V, Elmi M, Brigmon E, Otto PM, Kist KA, Speck H, Wang L, Ensor J, Shih YCT, Kim B, Pan IW, Cohen AL, Kelley K, Spak D, Yang WT, Chang JC, Wong STC. A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms. Radiol Artif Intell 2023; 5:e220259. [PMID: 38074778 PMCID: PMC10698614 DOI: 10.1148/ryai.220259] [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: 11/25/2022] [Revised: 06/08/2023] [Accepted: 07/07/2023] [Indexed: 01/31/2024]
Abstract
Purpose To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.
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Affiliation(s)
- Chika F. Ezeana
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Tiancheng He
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Tejal A. Patel
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Virginia Kaklamani
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Maryam Elmi
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Erika Brigmon
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Pamela M. Otto
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Kenneth A. Kist
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Heather Speck
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Lin Wang
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Joe Ensor
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Ya-Chen T. Shih
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Bumyang Kim
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - I-Wen Pan
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Adam L. Cohen
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Kristen Kelley
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - David Spak
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
| | - Wei T. Yang
- From the Department of Systems Medicine and Bioengineering, Houston
Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, Tex (C.F.E.,
T.H., L.W., S.T.C.W.); Houston Methodist Neal Cancer Center, Houston Methodist
Hospital, Houston, Tex (J.E., J.C.C.); Departments of General Oncology (T.A.P.),
Health Services Research (Y.C.T.S., B.K., I.W.P.), and Radiology (D.S., W.T.Y.),
University of Texas MD Anderson Cancer Center, Houston, Tex; University of Texas
Health Science Center, San Antonio, Tex (V.K., M.E., E.B., P.M.O., K.A.K.);
University of the Incarnate Word School of Osteopathic Medicine, San Antonio,
Tex (H.S.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
(A.L.C., K.K.); and Department of Radiology, Houston Methodist Hospital, Weill
Cornell Medicine, 6670 Bertner Ave, Houston, TX 77030 (S.T.C.W.)
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10
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Wang LC, Rao S, Schacht D, Bhole S. Reducing False Negatives in Biopsy of Suspicious MRI Findings. JOURNAL OF BREAST IMAGING 2023; 5:597-610. [PMID: 38416912 DOI: 10.1093/jbi/wbad024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Indexed: 03/01/2024]
Abstract
Breast MRI is a highly sensitive imaging modality that often detects findings that are occult on mammography and US. Given the overlap in appearance of benign and malignant lesions, an accurate method of tissue sampling for MRI-detected findings is essential. Although MRI-directed US and correlation with mammography can be helpful for some lesions, a correlate is not always found. MRI-guided biopsy is a safe and effective method of tissue sampling for findings seen only on MRI. The unique limitations of this technique, however, contribute to false negatives, which can result in delays in diagnosis and adverse patient outcomes; this is of particular importance as most MRI examinations are performed in the high-risk or preoperative setting. Here, we review strategies to minimize false negatives in biopsy of suspicious MRI findings, including appropriate selection of biopsy modality, use of meticulous MRI-guided biopsy technique, management after target nonvisualization, assessment of adequate lesion sampling, and determination of radiology-pathology concordance. A proposed management algorithm for MRI-guided biopsy results will also be discussed.
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Affiliation(s)
- Lilian C Wang
- Northwestern Medicine, Department of Radiology, Chicago, IL, USA
| | - Sandra Rao
- Northwestern Medicine, Department of Radiology, Chicago, IL, USA
| | - David Schacht
- Northwestern Medicine, Department of Radiology, Chicago, IL, USA
| | - Sonya Bhole
- Northwestern Medicine, Department of Radiology, Chicago, IL, USA
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11
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Zhu Y, Chen X, Dou H, Liu Y, Li F, Wang Y, Xiao M. Vacuum-assisted biopsy system for breast lesions: a potential therapeutic approach. Front Oncol 2023; 13:1230083. [PMID: 37593094 PMCID: PMC10430071 DOI: 10.3389/fonc.2023.1230083] [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: 05/28/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023] Open
Abstract
Purpose The primary objective is to optimize the population eligible for Mammotome Minimally Invasive Surgery (MIS) by refining selection criteria. This involves maximizing procedure benefits, minimizing malignancy risk, and reducing the rate of malignant outcomes. Patients and methods A total of 1158 female patients who came to our hospital from November 2016 to August 2021 for the Mammotome MIS were analyzed retrospectively. Following χ2 tests to screen for risk variables, binary logistic regression analysis was used to determine the independent predictors of malignant lesions. In addition, the correlation between age and lesion diameter was investigated for BI-RADS ultrasound (US) category 4a lesions in order to better understand the relationship between these variables. Results The malignancy rates of BI-RADS US category 3, category 4a and category 4b patients who underwent the Mammotome MIS were 0.6% (9/1562), 6.4% (37/578) and 8.3% (2/24) respectively. Malignant lesions were more common in patients over the age of 40, have visible blood supply, and BI-RADS category 4 of mammography. In BI-RADS US category 4a lesions, the diameter of malignant tumor was highly correlated with age, and this correlation was strengthened in patients over the age of 40 and with BI-RADS category 4 of mammography. Conclusion The results of this study demonstrate that the clinical data and imaging results, particularly age, blood supply, and mammography classification, offer valuable insights to optimize patients' surgical options and decrease the incidence of malignant outcomes.
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Affiliation(s)
| | | | | | | | | | | | - Min Xiao
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
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12
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Liu Y, Wang S, Qu J, Tang R, Wang C, Xiao F, Pang P, Sun Z, Xu M, Li J. High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4. BMC Med Imaging 2023; 23:58. [PMID: 37076817 PMCID: PMC10116788 DOI: 10.1186/s12880-023-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.
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Affiliation(s)
- Yufeng Liu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Qu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Rui Tang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chundan Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Fengchun Xiao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Peipei Pang
- GE Healthcare, Precision Health Institution, Hangzhou, China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
| | - Jiaying Li
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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13
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Miyake T, Shimazu K. Third-look contrast-enhanced ultrasonography plus needle biopsy for differential diagnosis of magnetic resonance imaging-only detected breast lesions. J Med Ultrason (2001) 2023:10.1007/s10396-023-01298-8. [PMID: 36905491 DOI: 10.1007/s10396-023-01298-8] [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: 09/20/2022] [Accepted: 01/23/2023] [Indexed: 03/12/2023]
Abstract
Research has shown that in approximately 20-30% of cases, breast lesions that were not detected on mammography (MG) or ultrasonography (US) were incidentally found during preoperative magnetic resonance imaging (MRI) examination for breast cancer. MRI-guided needle biopsy is recommended or considered for such MRI-only detected breast lesions invisible on second-look US, but many facilities in Japan cannot perform this biopsy procedure because it is expensive and time consuming. Thus, a simpler and more accessible diagnostic method is needed. Two studies to date have shown that third-look contrast-enhanced US (CEUS) plus needle biopsy for MRI-only detected breast lesions (i.e., MRI + /MG-/US-) that were not detected on second-look US showed moderate/high sensitivity (57.1 and 90.9%) and high specificity (100.0% in both studies) with no severe complications. In addition, the identification rate was higher for MRI-only lesions with a higher MRI BI-RADS category (i.e., category 4/5) than for those with a lower category (i.e., category 3). Despite the fact that there are limitations in our literature review, CEUS plus needle biopsy is a feasible and convenient diagnostic tool for MRI-only lesions invisible on second-look US and is expected to reduce the frequency of MRI-guided needle biopsy. When third-look CEUS does not reveal MRI-only lesions, a further indication for MRI-guided needle biopsy should be considered according to the BI-RADS category.
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Affiliation(s)
- Tomohiro Miyake
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, 2-2-E10 Yamada-Oka, Suita-Shi, Osaka, 565-0871, Japan.
| | - Kenzo Shimazu
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, 2-2-E10 Yamada-Oka, Suita-Shi, Osaka, 565-0871, Japan
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14
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Milon A, Flament V, Gueniche Y, Kermarrec E, Chabbert-Buffet N, Darai É, Touboul C, Razakamanantsoa L, Thomassin-Naggara I. How to optimize MRI breast protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences. Diagn Interv Imaging 2023; 104:284-291. [PMID: 36801096 DOI: 10.1016/j.diii.2023.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
PURPOSE The purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI. MATERIAL AND METHODS Women who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10-3 mm2/s to classify the lesions based on morphology and these two functional criteria only. RESULTS Two hundred fifty-seven women (median age: 51 years; range: 16-92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10-3 mm2/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively. CONCLUSION BI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.
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Affiliation(s)
- Audrey Milon
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France.
| | - Vincent Flament
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Yoram Gueniche
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Edith Kermarrec
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Nathalie Chabbert-Buffet
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Émile Darai
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Cyril Touboul
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Leo Razakamanantsoa
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Isabelle Thomassin-Naggara
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France
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Lyu Y, Chen Y, Meng L, Guo J, Zhan X, Chen Z, Yan W, Zhang Y, Zhao X, Zhang Y. Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies. Front Oncol 2023; 13:1074060. [PMID: 36816972 PMCID: PMC9929366 DOI: 10.3389/fonc.2023.1074060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
Objectives To investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies. Methods This retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance. Results 173 patients (mean age 43.1 years, range 18-69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset. Conclusions The ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.
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Affiliation(s)
- Yidong Lyu
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lingsong Meng
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jinxia Guo
- General Electric (GE) Healthcare, MR Research China, Beijing, China
| | - Xiangyu Zhan
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhuo Chen
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenjun Yan
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuyan Zhang
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Xin Zhao, ; Yanwu Zhang,
| | - Yanwu Zhang
- Department I of Breast, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China,*Correspondence: Xin Zhao, ; Yanwu Zhang,
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Meng L, Zhao X, Guo J, Lu L, Cheng M, Xing Q, Shang H, Wang K, Zhang B, Lei D, Zhang X. Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score. Front Oncol 2022; 12:964078. [PMID: 36303839 PMCID: PMC9595598 DOI: 10.3389/fonc.2022.964078] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/26/2022] [Indexed: 12/24/2022] Open
Abstract
Objective To investigate whether there is added value of quantitative parameters from synthetic magnetic resonance imaging (SyMRI) as a complement to the Kaiser score (KS) to differentiate benign and malignant breast lesions. Materials and methods In this single-institution study, 122 patients who underwent breast MRI from March 2020 to May 2021 were retrospectively analyzed. SyMRI and dynamic contrast-enhanced MRI were performed using a 3.0-T system. Two experienced radiologists independently assigned the KS and measured the quantitative values of T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) from SyMRI. Pathology was regarded as the gold standard. The diagnostic values were compared using the appropriate statistical tests. Results There were 122 lesions (86 malignant and 36 benign) in 122 women. The T1 value was identified as the only independent factor for the differentiation of malignant and benign lesions. The diagnostic accuracy of incorporating the T1 into the KS protocol (T1+KS) was 95.1% and 92.1% for all lesions (ALL) and The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, respectively, which was significantly higher than that of either T1 (ALL: 82.8%, P = 0.0001; BI-RADS 4: 78.9%, P = 0.002) or KS (ALL: 90.2%, P = 0.031; BI-RADS 4: 84.2%, P = 0.031) alone. The sensitivity and specificity of T1+KS were also higher than those of the T1 or KS alone. The combined diagnosis could have avoided another 15.6% biopsies compared with using KS alone. Conclusions Incorporating T1 into the KS protocol improved both the sensitivity and specificity to differentiate benign and malignant breast lesions, thus avoiding unnecessary invasive procedures.
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Affiliation(s)
- Lingsong Meng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinxia Guo
- General Electric (GE) Healthcare, MR Research China, Beijing, China
| | - Lin Lu
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meiying Cheng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingna Xing
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Honglei Shang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- General Electric (GE) Healthcare, MR Research China, Beijing, China
| | - Bohao Zhang
- Henan Key Laboratory of Child Brain Injury, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongmei Lei
- Department of Pathology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoan Zhang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Xiaoan Zhang,
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Penn A, Medved M, Abe H, Dialani V, Karczmar GS, Brousseau D. Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding. BMC Med Imaging 2022; 22:171. [PMID: 36175878 PMCID: PMC9524062 DOI: 10.1186/s12880-022-00897-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 09/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Thresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies.
Purpose We evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10–3 mm2/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics. Study type Prospective. Population 55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy. Field strength/sequence 1.5 T and 3.0 T. DWI. Assessment Analysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images. Statistical tests Improvement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance. Results Using ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies. Data conclusion Supplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.
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Affiliation(s)
- Alan Penn
- Alan Penn and Associates, Inc., Rockville, MD, 20850, USA.
| | | | | | - Vandana Dialani
- Beth Israel Deaconess Medical Center, Boston, MA, 02467, USA
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Miles BL, Nguyen QD. Free Silicone With Giant Cell Reaction Can Enhance on Breast MRI. Cureus 2022; 14:e29365. [PMID: 36284818 PMCID: PMC9584033 DOI: 10.7759/cureus.29365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
Background Breast augmentation with silicone implants is commonplace, and such implants have a risk of rupture which increases over time. Most implant ruptures are asymptomatic, and magnetic resonance imaging (MRI) is a recommended imaging modality for surveillance to detect these events. If a silicone leak enhances on MRI, it is currently categorized according to the Breast Imaging Reporting and Data System (BI-RADS) as category 4, which results in a recommendation for biopsy even when free silicone leakage is the most likely diagnosis. In this article, we present a case series that illustrates this issue with the BI-RADS system and propose an algorithmic approach that may allow some patients to be placed into BI-RADS category 3 and avoid biopsy. Methodology Eight cases of silicone breast implant rupture were identified at the University of Texas Medical Branch at Galveston over a five-year period. Two cases were excluded because MRI was not performed. The remaining six cases were evaluated for history and physical findings as well as mammogram, ultrasound, and MRI. All identified cases had been categorized as BI-RADS 4 and underwent biopsy. Results The six cases in this series exhibited pre-biopsy radiographic findings that were most consistent with silicone implant rupture. The ruptures were proven by biopsy, and no evidence of malignancy was identified in any of the patients. Conclusions Free silicone from breast implant rupture can present with enhancement on MRI. The two main categories of breast MRI enhancement, namely, mass and non-mass, include malignancies in their differential diagnoses and result in a BI-RADS category 4 designation. By correlating the findings with other imaging modalities, some of these patients can be classified as BI-RADS category 3 and biopsy can safely be avoided.
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A Comparative Efficacy Study of Diagnostic Digital Breast Tomosynthesis and Digital Mammography in BI-RADS 4 Breast Cancer Diagnosis. Eur J Radiol 2022; 153:110361. [DOI: 10.1016/j.ejrad.2022.110361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/12/2022] [Accepted: 05/09/2022] [Indexed: 12/28/2022]
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Can Contrast-Enhanced Spectral Mammography (CESM) Reduce Benign Breast Biopsy? Breast J 2022; 2022:7087408. [PMID: 35711887 PMCID: PMC9187292 DOI: 10.1155/2022/7087408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 01/30/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
Objectives To evaluate the potential of contrast-enhanced spectral mammography (CESM) in reducing benign breast biopsy rate, thereby improving resource utilization. To explore its potential as a value-adding modality in the management of BI-RADS 4/5 lesions. Materials and Methods This was a prospective study conducted between July 2016 and September 2018. Patients with BI-RADS 4/5 lesions detected on conventional imaging (mammogram, digital breast tomosynthesis, and ultrasound) were enrolled for adjunct CESM. Histopathologic correlation was done for all lesions. Additional suspicious lesions detected on CESM were all identified on second-look ultrasound and subsequently biopsied. Images were evaluated independently by two radiologists trained in breast imaging using BI-RADS classification. Presence of enhancement on CESM, BI-RADS score, and histopathology of each lesion were analyzed and tested with the chi-square/fisher-exact test for statistical significance. Results The study included 105 lesions in 63 participants—1 man and 62 women, an average age of 53.7 ± 10.8 years. On CESM, 22 (20.9%) of the lesions did not show enhancement. All 22 lesions had been classified as BI-RADS 4A and were subsequently proven to be benign. Of the remaining 83 enhancing lesions, 54 (65.1%) were malignant and 29 (34.9%) were benign (p < 0.05). CESM detected 6 additional lesions which were not identified on initial conventional imaging. Four of these were proven malignant and were in a different quadrant than the primary lesion investigated. Conclusion There is evidence that the absence of enhancement in CESM strongly favors benignity. It may provide the reporting radiologist with greater confidence in imaging assessment, especially in BI-RADS 4A cases, where a proportion of them are in actuality BI-RADS 3. Greater accuracy of BI-RADS grading can reduce nearly half of benign biopsies and allow better resource allocation. CESM also increases the detection rate of potentially malignant lesions, thereby changing the treatment strategies.
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21
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Huang N, Chen L, He J, Nguyen QD. The Efficacy of Clinical Breast Exams and Breast Self-Exams in Detecting Malignancy or Positive Ultrasound Findings. Cureus 2022; 14:e22464. [PMID: 35371742 PMCID: PMC8942605 DOI: 10.7759/cureus.22464] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 11/25/2022] Open
Abstract
Objective A breast exam is a low-risk, low-cost method for early detection, which is crucial for improved mortality. However, clinical breast exams (CBE) and breast self-exams (BSEs) remain controversial with unclear guidelines. This study analyzes the efficacy of these two exam types in evaluating palpable breast masses. Methods This retrospective cross-sectional study included 2019 medical records from Epic of women with breast lumps. Patient demographics, provider types, and breast exam types were recorded. Primary outcomes were detection of cancer and positive ultrasound finding. Fisher’s exact tests and two-sample t-tests determined the statistical significance of the association between the outcomes and categorical and continuous variables. Results Of 462 breast masses, 69 demonstrated positive ultrasound findings, with 26 of those yielding cancer; 96% of cancers and 81% of ultrasound findings resulted from patient-identified lumps. Of provider-identified lumps, 100% of cancers and 92.3% of positive ultrasound findings were diagnosed by MDs (doctors of medicine) rather than midlevel providers. There was no statistically significant difference in identifying cancer or positive ultrasound finding between CBEs and BSEs (p = 0.3709 and p = 0.1556). Conclusion Despite no difference between CBEs and BSEs in identifying cancer or positive ultrasound finding, 25 of the 26 breast cancers were initially detected by patients, while only one of 26 was detected by CBE. BSEs detect breast cancers. Although some guidelines encourage CBEs over self-exams, not all CBEs are equal. Key message There is no significant difference between CBEs and BSEs in identifying cancer or positive ultrasound finding. The majority of cancers were initially identified by patients. BSEs detect breast cancers and women should continue performing them. Not all CBEs are equal. CBEs by MDs, especially women health specialists, are generally more effective than those by midlevel providers.
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22
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Meng L, Zhao X, Lu L, Xing Q, Wang K, Guo Y, Shang H, Chen Y, Huang M, Sun Y, Zhang X. A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value. Front Oncol 2021; 11:779642. [PMID: 34926290 PMCID: PMC8675081 DOI: 10.3389/fonc.2021.779642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI. Methods This was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test. Results The study involved 243 women (mean age, 43.1 years; age range, 18-67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity. Conclusion For MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.
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Affiliation(s)
- Lingsong Meng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Lu
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingna Xing
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- Magnetic Resonance (MR) Research China, General Electric (GE) Healthcare, Beijing, China
| | - Yafei Guo
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Honglei Shang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengyue Huang
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongbing Sun
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoan Zhang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Zhao YF, Chen Z, Zhang Y, Zhou J, Chen JH, Lee KE, Combs FJ, Parajuli R, Mehta RS, Wang M, Su MY. Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography. Front Oncol 2021; 11:774248. [PMID: 34869020 PMCID: PMC8637829 DOI: 10.3389/fonc.2021.774248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 10/29/2021] [Indexed: 12/09/2022] Open
Abstract
Objective To build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer. Materials and Methods 266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined. Results In the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography. Conclusion The radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.
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Affiliation(s)
- You-Fan Zhao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Kyoung Eun Lee
- Department of Radiology, Inje University Seoul Paik Hospital, Inje University, Seoul, South Korea
| | - Freddie J Combs
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Rita S Mehta
- Department of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
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Clauser P, Krug B, Bickel H, Dietzel M, Pinker K, Neuhaus VF, Marino MA, Moschetta M, Troiano N, Helbich TH, Baltzer PAT. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy. Clin Cancer Res 2021; 27:1941-1948. [PMID: 33446565 PMCID: PMC8406278 DOI: 10.1158/1078-0432.ccr-20-3037] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies. EXPERIMENTAL DESIGN This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10-3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis. RESULTS There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282). CONCLUSIONS An ADC cutoff of ≥1.5 × 10-3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Krug
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Hubert Bickel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, Friedrich-Alexander-University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor-Frederic Neuhaus
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy
| | - Marco Moschetta
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Nicoletta Troiano
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
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Ghaemian N, Haji Ghazi Tehrani N, Nabahati M. Accuracy of mammography and ultrasonography and their BI-RADS in detection of breast malignancy. CASPIAN JOURNAL OF INTERNAL MEDICINE 2021; 12:573-579. [PMID: 34820065 PMCID: PMC8590403 DOI: 10.22088/cjim.12.4.573] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/02/2021] [Accepted: 03/13/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND We aimed to compare the diagnostic accuracy of mammography and ultrasonography and their breast imaging-reporting and data system (BI-RADS) classification versus breast core needle biopsy (CNB) findings in distinguishing the breast masses. METHODS This cross-sectional study was conducted during 2016-2018 on female patients who were referred to a radiology center in Babol, northern Iran, for routine screening and/or for CNB. Patients underwent sonography and mammography by a senior radiologist. The breast lesions were also evaluated according to BI-RADS classification. CNB was performed on the breast masses by the same radiologist and pathological procedures were performed by an expert pathologist. Descriptive statistics were used to analyze the data. RESULTS In total, 213 breast masses were finally assessed, of which 107 (50.2 %) masses were benign and 106 (49.8 %) masses were malignant. The sensitivity for mammography and ultrasound alone was 72.6% and 68.9%, respectively. This rate for combined mammography and ultrasound was 84.9%. About BI-RADS classification, 28 masses were classified as BI-RADS 3, 99 as BI-RADS 4A, 4 as BI-RADS 4B, 18 as BI-RADS 4C, and 64 as BI-RADS 5. BI-RADS 4A had the highest sensitivity (70.1%) among BI-RADS categories. The highest specificity pertained to BI-RADS 3 and 5 (100%) among BI-RADS categories. Also, the highest accuracy was related to BI-RADS 5 (80.3%). CONCLUSION The results of the present study showed that combined mammography and ultrasound had a higher rate of accuracy than mammography or ultrasound alone. Furthermore, the imaging methods BI-RADS classification had an acceptable positive predictive value.
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Affiliation(s)
- Naser Ghaemian
- Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Babol, Iran
| | | | - Mehrdad Nabahati
- Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Babol, Iran,Correspondence: Mehrdad Nabahati, Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Ganjafrooz Street, Babol, 47176-47745, Iran. E-mail: , Tel: 0098 1132252071, Fax: 0098 1132252071
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Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol 2020; 216:860-873. [PMID: 33295802 DOI: 10.2214/ajr.20.24894] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.
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Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis. J Comput Assist Tomogr 2020; 44:914-920. [PMID: 33196599 DOI: 10.1097/rct.0000000000001108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast. METHODS The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression. RESULTS Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively. CONCLUSION Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.
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Radick AC, Reisch LM, Shucard HL, Piepkorn MW, Kerr KF, Elder DE, Barnhill RL, Knezevich SR, Oster N, Elmore JG. Terminology for melanocytic skin lesions and the MPATH-Dx classification schema: A survey of dermatopathologists. J Cutan Pathol 2020; 48:733-738. [PMID: 32935869 DOI: 10.1111/cup.13873] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Diagnostic terms used in histopathology reports of cutaneous melanocytic lesions are not standardized. We describe dermatopathologists' views regarding diverse diagnostic terminology and the utility of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) for categorizing melanocytic lesions. METHODS July 2018-2019 survey of board-certified and/or fellowship-trained dermatopathologists with experience interpreting melanocytic lesions. RESULTS Among 160 participants, 99% reported witnessing different terminology being used for the same melanocytic lesion. Most viewed diverse terminology as confusing to primary care physicians (98%), frustrating to pathologists (83%), requiring more of their time as a consultant (64%), and providing necessary clinical information (52%). Most perceived that adoption of the MPATH-Dx would: improve communication with other pathologists and treating physicians (87%), generally be a change for the better (80%), improve patient care (79%), be acceptable to clinical colleagues (68%), save time in pathology report documentation (53%), and protect from malpractice (51%). CONCLUSIONS Most dermatopathologists view diverse terminology as contributing to miscommunication with clinicians and patients, adversely impacting patient care. They view the MPATH-Dx as a promising tool to standardize terminology and improve communication. The MPATH-Dx may be a useful supplement to conventional pathology reports. Further revision and refinement are necessary for widespread clinical use.
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Affiliation(s)
- Andrea C Radick
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Lisa M Reisch
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Hannah L Shucard
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Michael W Piepkorn
- Division of Dermatology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA.,Dermatopathology Northwest, Bellevue, Washington, USA
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - David E Elder
- Department of Pathology and Laboratory Medicine, Division of Anatomic Pathology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Raymond L Barnhill
- Department of Pathology, Institut Curie, Paris Sciences and Letters Research University, Paris, France.,Department of Translational Research, Institut Curie, Paris Sciences and Letters Research University, Paris, France.,Faculty of Medicine, University of Paris Descartes, Paris, France
| | | | - Natalia Oster
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Joann G Elmore
- Department of Medicine, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, California, USA
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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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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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Matsuda M, Tsuda T, Ebihara R, Toshimori W, Takeda S, Okada K, Nakasuka K, Shiraishi Y, Suekuni H, Kamei Y, Kurata M, Kitazawa R, Mochizuki T, Kido T. Enhanced Masses on Contrast-Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast-Enhanced MRI and Quantitative Evaluation with Synthetic MRI. J Magn Reson Imaging 2020; 53:381-391. [PMID: 32914921 DOI: 10.1002/jmri.27362] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 08/21/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The addition of synthetic MRI might improve the diagnostic performance of dynamic contrast-enhanced MRI (DCE-MRI) in patients with breast cancer. PURPOSE To evaluate the diagnostic value of a combination of DCE-MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses. STUDY TYPE Retrospective, observational. POPULATION In all, 121 patients with 131 breast masses who underwent DCE-MRI with additional synthetic MRI were enrolled. FIELD STRENGTH/SEQUENCE 3.0 Tesla, T1 -weighted DCE-MRI and synthetic MRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT All lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE-MRI type based on the Breast Imaging-Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE-MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared. STATISTICAL TESTS Univariate (Mann-Whitney U-test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver-operating characteristic curve (AUC) analysis. RESULTS Univariate and multivariate analyses showed that the mean T1 relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre-T1 ) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE-MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE-MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE-MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE-MRI type (0.74 vs. 0.50, P < 0.05). DATA CONCLUSION A combination of synthetic MRI and DCE-MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses. Level of Evidence 4 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:381-391.
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Affiliation(s)
- Megumi Matsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Takaharu Tsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Rui Ebihara
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Wataru Toshimori
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Shiori Takeda
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Kanako Okada
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Kaori Nakasuka
- Department of Radiology, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Yasuhiro Shiraishi
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Hiroshi Suekuni
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
| | | | - Mie Kurata
- Department of Pathology, Ehime University Proteo-Science Center, Toon, Japan.,Department of Analytical Pathology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Riko Kitazawa
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Teruhito Mochizuki
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan.,Department of Radiology, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Teruhito Kido
- Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan
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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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Au FWF, Ghai S, Lu FI, Lu H. Clinical Value of Shear Wave Elastography Added to Targeted Ultrasound (Second-Look Ultrasound) in the Evaluation of Breast Lesions Suspicious of Malignancy Detected on Magnetic Resonance Imaging. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:2395-2406. [PMID: 30666681 DOI: 10.1002/jum.14936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 12/07/2018] [Accepted: 12/20/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To determine the value of shear wave elastography (SWE) added to targeted ultrasound (US) after breast magnetic resonance imaging (MRI). METHODS From July 2015 to October 2017, 40 patients who underwent targeted US evaluations of suspicious MRI-detected American College of Radiology Breast Imaging Reporting and Data System category 4 lesions (mass or nonmass enhancement) were enrolled in this prospective study. B-mode US and SWE examinations were performed to detect US correlates to MRI-detected lesions; their Breast Imaging Reporting and Data System categories were recorded; lesions that were dark blue on a 6-point color scale or had maximum elasticity of 30 kPa or less were categorized as soft. Biopsy was performed with US or MRI guidance, with the pathologic findings correlated with MRI, US, and SWE findings. The value of SWE for lesion detection and identification of benign lesions was determined. RESULTS The mean age of the 40 patients was 51.1 years. There were 48 MRI-detected lesions (20 cancers, 3 high-risk lesions, and 25 benign lesions). Ultrasound correlates (8 category 3 and 25 category 4) were shown for 33 lesions (69%; P < .0001), with 16 cancers (80%; P < .0001) and 17 benign lesions. Shear wave elastography assisted detection of 3 (19%) cancers on US imaging. All 7 soft US category 3 lesions were benign (7 of 33 [21%]; P = .0014). CONCLUSIONS Shear wave elastography was useful with targeted US after breast MRI to increase cancer detection by US. A significant number of US correlates to MRI-detected lesions could have been identified as benign (category 3 and soft) before biopsy, with the potential of short-interval follow-up of MRI-detected lesions with benign US correlates instead of biopsy.
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Affiliation(s)
- Frederick Wing-Fai Au
- Joint Department of Medical Imaging, Toronto General Hospital, Toronto, Ontario, Canada
| | - Sandeep Ghai
- Joint Department of Medical Imaging, Women's College Hospital, Toronto, Ontario, Canada
| | - Fang-I Lu
- Department of Pathology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Hua Lu
- University of Toronto, Toronto, Ontario, Canada
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A comparative study on superb microvascular imaging and conventional ultrasonography in differentiating BI-RADS 4 breast lesions. Oncol Lett 2019; 18:3202-3210. [PMID: 31452797 PMCID: PMC6676576 DOI: 10.3892/ol.2019.10603] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 06/11/2019] [Indexed: 11/06/2022] Open
Abstract
This prospective study aimed to explore the diagnostic value of superb microvascular imaging (SMI) in differentiating Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions compared with conventional ultrasonography (US). A total of 111 patients with 116 breast lesions underwent grayscale ultrasound (US), colour Doppler flow imaging (CDFI) and SMI breast imaging between February 2016 and May 2018. CDFI and SMI were performed to evaluate vascular quantity, morphology, and distribution characteristics. The detection of malignancy was compared between grayscale US alone, US + CDFI and US + SMI in terms of the BI-RADS stratification system. SMI was observed to be significantly more accurate in distinguishing malignant breast lesions (86.67%) compared with CDFI (80.00%) (P<0.001). Among malignant lesions, SMI detected 80.00% of those that contained ≥4 vessels, while CDFI only detected 56.67%. Penetrating and branching vessels were identified by SMI in 53.33% of malignant breast lesions and by CDFI in 10.00%. There was no significant difference in vascular distribution by SMI (P=0.094) and by CDFI (P=0.087). US + SMI was associated with higher sensitivity, specificity, and accuracy rates (86.67, 83.72 and 84.48%, respectively) compared with US + CDFI (80.00, 72.09 and 74.14%, respectively). The area under the curve values from receiver operating characteristic analysis of US + SMI, US + CDFI and US alone were 0.852 [95% confidence interval (CI): 0.768–0.936)] 0.760 (95% CI: 0.660–0.860), 0.698 (95% CI: 0.589–0.807), respectively (P<0.001). SMI yielded more detailed vascular information associated with malignant breast masses when compared with conventional US. Therefore, as an adjunct to grayscale, SMI exhibited a markedly improved diagnostic capability in distinguishing malignant and benign breast lesions, particularly those of BI-RADS category 4.
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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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