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Parillo M, Mallio CA, Van der Molen AJ, Rovira À, Dekkers IA, Karst U, Stroomberg G, Clement O, Gianolio E, Nederveen AJ, Radbruch A, Quattrocchi CC. The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS). MAGMA (NEW YORK, N.Y.) 2024; 37:15-25. [PMID: 37702845 PMCID: PMC10876744 DOI: 10.1007/s10334-023-01113-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 09/14/2023]
Abstract
Among the 28 reporting and data systems (RADS) available in the literature, we identified 15 RADS that can be used in Magnetic Resonance Imaging (MRI). Performing examinations without using gadolinium-based contrast agents (GBCA) has benefits, but GBCA administration is often required to achieve an early and accurate diagnosis. The aim of the present review is to summarize the current role of GBCA in MRI RADS. This overview suggests that GBCA are today required in most of the current RADS and are expected to be used in most MRIs performed in patients with cancer. Dynamic contrast enhancement is required for correct scores calculation in PI-RADS and VI-RADS, although scientific evidence may lead in the future to avoid the GBCA administration in these two RADS. In Bone-RADS, contrast enhancement can be required to classify an aggressive lesion. In RADS scoring on whole body-MRI datasets (MET-RADS-P, MY-RADS and ONCO-RADS), in NS-RADS and in Node-RADS, GBCA administration is optional thanks to the intrinsic high contrast resolution of MRI. Future studies are needed to evaluate the impact of the high T1 relaxivity GBCA on the assignment of RADS scores.
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Affiliation(s)
- Marco Parillo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128, Rome, Italy
| | - Aart J Van der Molen
- Department of Radiology, C-2S, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ilona A Dekkers
- Department of Radiology, C-2S, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Uwe Karst
- Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstr. 48, 48149, Münster, Germany
| | - Gerard Stroomberg
- RIWA-Rijn-Association of River Water Works, Groenendael 6, 3439 LV, Nieuwegein, The Netherlands
| | - Olivier Clement
- Service de Radiologie, Université de Paris, AP-HP, Hôpital Européen Georges Pompidou, DMU Imagina, 20 Rue LeBlanc, 75015, Paris, France
| | - Eliana Gianolio
- Department of Molecular Biotechnologies and Health Science, University of Turin, Via Nizza 52, 10125, Turin, Italy
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Alexander Radbruch
- Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany
| | - Carlo Cosimo Quattrocchi
- Centre for Medical Sciences-CISMed, University of Trento, Via S. Maria Maddalena 1, 38122, Trento, Italy.
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Li N, Hou Z, Wang J, Bi Y, Wu X, Zhan Y, Peng M. Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses. BMC Med Imaging 2023; 23:206. [PMID: 38066441 PMCID: PMC10709938 DOI: 10.1186/s12880-023-01164-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND We aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS). METHODS A total of 364 patients with 367 breast masses (151 benign and 216 malignant) who underwent conventional ultrasound and inversion imaging prior to breast surgery were included. A 5-point inversion score (IS) scale was proposed based on the masses' internal echogenicity and distribution characteristics in the inversion images. The combination of IS and BI-RADS was compared with BI-RADS alone to evaluate the value of inversion imaging for breast mass diagnosis. The diagnostic performance of the BI-RADS and its combination with IS for breast masses were analyzed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The IS for malignant breast masses (3.96 ± 0.77) was significantly higher than benign masses (2.58 ± 0.98) (P < 0.001). The sensitivity, specificity, accuracy, PPV, and NPV of BI-RADS were 86.1%, 81.5%, 84.2%, 86.9%, and 80.4%, respectively, and an AUC was 0.909. By compared with BI-RADS, 72 breast masses were downgraded from suspected malignancy to benign, and 6 masses were upgraded from benign to suspected malignancy. Thus, the specificity was increased from 81.5 to 84.8%, it allows 72 benign masses avoid biopsy. CONCLUSION The combination of inversion imaging with BI-RADS can effectively improve the diagnostic efficacy of breast masses, and inversion imaging could help benign masses avoid biopsy.
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Affiliation(s)
- Na Li
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China
| | - Zhongguang Hou
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China
| | - Jiajia Wang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China
| | - Yu Bi
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China
| | - Xiabi Wu
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China
| | - Yunyun Zhan
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China.
| | - Mei Peng
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Economic and Technological Development Zone, No.678, Furong Road, Hefei, Anhui, China.
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de Oliveira TMG. Are we ready to stratify BI-RADS 4 MRI lesions? Radiol Bras 2023; 56:V-VI. [PMID: 38504812 PMCID: PMC10948156 DOI: 10.1590/0100-3984.2023.56.6e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024] Open
Affiliation(s)
- Tatiane Mendes Gonçalves de Oliveira
- Attending Physician at the Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Radiologist at the Clínica Radiologia Especializada, Ribeirão Preto, SP, Brazil
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de Almeida JRM, Bitencourt AGV, Gomes AB, Chagas GL, Barros TP. Are we ready to stratify BI-RADS 4 lesions observed on magnetic resonance imaging? A real-world noninferiority/equivalence analysis. Radiol Bras 2023; 56:291-300. [PMID: 38504813 PMCID: PMC10948154 DOI: 10.1590/0100-3984.2023.0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/05/2023] [Accepted: 10/06/2023] [Indexed: 03/21/2024] Open
Abstract
Objective To demonstrate that positive predictive values (PPVs) for suspicious (category 4) magnetic resonance imaging (MRI) findings that have been stratified are equivalent to those stipulated in the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) for mammography and ultrasound. Materials and Methods This retrospective analysis of electronic medical records generated between January 4, 2016 and December 29, 2021 provided 365 patients in which 419 suspicious (BI-RADS category 4) findings were subcategorized as BI-RADS 4A, 4B or 4C. Malignant and nonmalignant outcomes were determined by pathologic analyses, follow-up, or both. For each subcategory, the level 2 PPV (PPV2) was calculated and tested for equivalence/noninferiority against the established benchmarks. Results Of the 419 findings evaluated, 168 (40.1%) were categorized as malignant and 251 (59.9%) were categorized as nonmalignant. The PPV2 for subcategory 4A was 14.2% (95% CI: 9.3-20.4%), whereas it was 41.2% (95% CI: 32.8-49.9%) for subcategory 4B and 77.2% (95% CI: 68.4-84.5%) for subcategory 4C. Multivariate analysis showed a significantly different cancer yield for each subcategory (p < 0.001). Conclusion We found that stratification of suspicious findings by MRI criteria is feasible, and malignancy probabilities for sub-categories 4B and 4C are equivalent to the values established for the other imaging methods in the BI-RADS. Nevertheless, low suspicion (4A) findings might show slightly higher malignancy rates.
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Li L, Deng H, Ye X, Li Y, Wang J. Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules. Sci Rep 2023; 13:16047. [PMID: 37749121 PMCID: PMC10519965 DOI: 10.1038/s41598-023-42937-x] [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: 05/16/2023] [Accepted: 09/16/2023] [Indexed: 09/27/2023] Open
Abstract
This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.
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Affiliation(s)
- Lu Li
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Hongyan Deng
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Yong Li
- Institute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences, 50 Zhongling Street, Nanjing, 210014, China.
| | - Jie Wang
- Department of Radiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China.
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Abstract
BACKGROUND Computer-aided diagnosis (CAD) systems have shown great potential as an effective auxiliary diagnostic tool in breast imaging. Previous studies have shown that S-Detect technology has a high accuracy in the differential diagnosis of breast masses. However, the application of S-Detect in clinical practice remains controversial, and the results vary among different clinical trials. This meta-analysis aimed to determine the diagnostic accuracy of S-Detect for distinguishing between benign and malignant breast masses. METHODS We searched PubMed, Cochrane Library, and CBM databases from inception to April 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive, and negative likelihood ratio (LR+/LR-), diagnostic odds ratio(DOR), and summary receiver operating characteristic (SROC) curves. Cochran Q-statistic and I2 test were used to evaluate the potential heterogeneity between studies. Sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate potential sources of heterogeneity. RESULTS Eleven studies that met all the inclusion criteria were included in the meta-analysis. A total of 951 malignant and 1866 benign breast masses were assessed. All breast masses were histologically confirmed using S-Detect. The pooled Sen was 0.82 (95% confidence interval(CI) = 0.74-0.88); the pooled Spe was 0.83 (95%CI = 0.78-0.88). The pooled LR + was 4.91 (95%CI = 3.75-6.41); the pooled negative LR - was 0.21 (95%CI = 0.15-0.31). The pooled DOR of S-Detect in the diagnosis of breast nodules was 23.12 (95% CI = 14.53-36.77). The area under the SROC curve was 0.90 (SE = 0.0166). No evidence of publication bias was found (t = 0.54, P = .61). CONCLUSIONS Our meta-analysis indicates that S-Detect may have high diagnostic accuracy in distinguishing benign and malignant breast masses.
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Affiliation(s)
- Xiaolei Wang
- Ultrasound department of the First Affiliated Hospital of Dalian Medical University
| | - Shuang Meng
- Ultrasound department of the First Affiliated Hospital of Dalian Medical University
- *Correspondence: Shuang Meng, No. 222 Zhongshan Road, Xigang District, Dalian City, Liaoning Province, China ()
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Guo W, Li F, Jia C, Wang T, Zhang X, Yao G, Shi X, Bai M. The clinical value of conventional ultrasound combined with contrast-enhanced ultrasound in the evaluation of BI-RADS 4 lesions detected by magnetic resonance imaging. Br J Radiol 2022; 95:20220025. [PMID: 35604699 PMCID: PMC10162066 DOI: 10.1259/bjr.20220025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 05/04/2022] [Accepted: 05/08/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE To determine the value of conventional ultrasound combined with contrast-enhanced ultrasound (CEUS) in Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions as detected by MRI. METHODS A total of 176 breast lesions from 171 patients were detected by MRI and categorised as BI-RADS 4. All patients also underwent ultrasound and CEUS scans. The combination of ultrasound-BI-RADS and CEUS 5-point scoring system created the Rerated BI-RADS (referred to as CEUS-BI-RADS). The diagnostic performances of ultrasound and CEUS-BI-RADS were then compared. A χ2 test was used to compare the CEUS features of mass-like and non-mass-like enhancement types of MRI-BI-RADS 4 lesions. RESULTS There were 167 (167/176) breast lesions detected by ultrasound, with a detection rate of 94.89%, while all were subsequently detected by "second-look" ultrasound combined with CEUS, with a detection rate of 100%. The areas under the receiver operating characteristic curves for ultrasound and CEUS-BI-RADS were 0.810 and 0.940, respectively. The diagnostic efficiency of CEUS-BI-RADS was significantly higher than that of ultrasound alone (z = 3.264, p = 0.001). For both mass-like and non-mass-like enhancement types of MRI-BI-RADS 4 lesions, CEUS-BI-RADS demonstrated satisfactory sensitivity and accuracy. Moreover, 29 (29/176) category 4 lesions were downgraded to 3 by CEUS-BI-RADS. CONCLUSION Ultrasound combined with CEUS can allow reclassification, reduce biopsy rates, and facilitate pre-surgical localisation for biopsy or surgery for MRI-BI-RADS 4 lesions. ADVANCES IN KNOWLEDGE For MRI-BI-RADS Category 4 lesions with a wide range of malignancies, ultrasound combined with CEUS is a promising diagnostic approach with high clinical utility.
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Affiliation(s)
- Wenjuan Guo
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Jia
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tong Wang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuemei Zhang
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gehong Yao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiudong Shi
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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