1
|
Sauer ST, Christner SA, Lois AM, Woznicki P, Curtaz C, Kunz AS, Weiland E, Benkert T, Bley TA, Baeßler B, Grunz JP. Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI. J Magn Reson Imaging 2024; 60:1190-1200. [PMID: 37974498 DOI: 10.1002/jmri.29139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. PURPOSE To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. STUDY TYPE Retrospective. POPULATION 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). ASSESSMENT DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. STATISTICAL TESTS Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. RESULTS Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. DATA CONCLUSION Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Stephanie Tina Sauer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Sara Aniki Christner
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Anna-Maria Lois
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Piotr Woznicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Carolin Curtaz
- Department of Obstetrics and Gynecology, University Hospital Würzburg, Würzburg, Germany
| | - Andreas Steven Kunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thorsten Alexander Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| |
Collapse
|
2
|
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.
Collapse
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.
| |
Collapse
|
3
|
Skwierawska D, Laun FB, Wenkel E, Kapsner LA, Janka R, Uder M, Ohlmeyer S, Bickelhaupt S. Diffusion-Weighted Imaging for Skin Pathologies of the Breast-A Feasibility Study. Diagnostics (Basel) 2024; 14:934. [PMID: 38732348 PMCID: PMC11083106 DOI: 10.3390/diagnostics14090934] [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: 03/07/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.
Collapse
Affiliation(s)
- Dominika Skwierawska
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Frederik B. Laun
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Radiologie München, Burgstraße 7, 80331 München, Germany
- Medical Faculty, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Lorenz A. Kapsner
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen-Tennenlohe, Germany
| | - Rolf Janka
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany
| |
Collapse
|
4
|
Dietzel M, Laun FB, Heiß R, Wenkel E, Bickelhaupt S, Hack C, Uder M, Ohlmeyer S. Initial experience with a next-generation low-field MRI scanner: Potential for breast imaging? Eur J Radiol 2024; 173:111352. [PMID: 38330534 DOI: 10.1016/j.ejrad.2024.111352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T. METHODS This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations. RESULTS Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min. CONCLUSION This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time.
Collapse
Affiliation(s)
- Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Frederik B Laun
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Rafael Heiß
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Evelyn Wenkel
- Radiologie München, Burgstrasse 7, 80331 München, Germany.
| | - Sebastian Bickelhaupt
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Carolin Hack
- Department of Gynecology, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Universitätsstraße 21/23, 91054 Erlangen, Germany.
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Sabine Ohlmeyer
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| |
Collapse
|
5
|
Pötsch N, Vatteroni G, Clauser P, Rainer E, Kapetas P, Milos R, Helbich TH, Baltzer P. Using the Kaiser Score as a clinical decision rule for breast lesion classification: Does computer-assisted curve type analysis improve diagnosis? Eur J Radiol 2024; 170:111271. [PMID: 38185026 DOI: 10.1016/j.ejrad.2023.111271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/09/2024]
Abstract
PURPOSE We aimed to investigate the effect of using visual or automatic enhancement curve type assessment on the diagnostic performance of the Kaiser Score (KS), a clinical decision rule for breast MRI. METHOD This IRB-approved retrospective study analyzed consecutive conventional BI-RADS 0, 4 or 5 patients who underwent biopsy after 1.5T breast MRI according to EUSOBI recommendations between 2013 and 2015. The KS includes five criteria (spiculations; signal intensity (SI)-time curve type; margins of the lesion; internal enhancement; and presence of edema) resulting in scores from 1 (=lowest) to 11 (=highest risk of breast cancer). Enhancement curve types (Persistent, Plateau or Wash-out) were assessed by two radiologists independently visually and using a pixel-wise color-coded computed parametric map of curve types. KS diagnostic performance differences between readings were compared by ROC analysis. RESULTS In total 220 lesions (147 benign, 73 malignant) including mass (n = 148) and non-mass lesions (n = 72) were analyzed. KS reading performance in distinguishing benign from malignant lesions did not differ between visual analysis and parametric map (P = 0.119; visual: AUC 0.875, sensitivity 95 %, specificity 63 %; and map: AUC 0.901, sensitivity 97 %, specificity 65 %). Additionally, analyzing mass and non-mass lesions separately, showed no difference between parametric map based and visual curve type-based KS analysis as well (P = 0.130 and P = 0.787). CONCLUSIONS The performance of the Kaiser Score is largely independent of the curve type assessment methodology, confirming its robustness as a clinical decision rule for breast MRI in any type of breast lesion in clinical routine.
Collapse
Affiliation(s)
- N Pötsch
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - G Vatteroni
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - P Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - E Rainer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - P Kapetas
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - R Milos
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - T H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - P Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| |
Collapse
|
6
|
Tollens F, Baltzer PA, Froelich MF, Kaiser CG. Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses. Front Oncol 2023; 13:1292268. [PMID: 38130995 PMCID: PMC10733447 DOI: 10.3389/fonc.2023.1292268] [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: 09/11/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening. Purpose To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening. Materials and methods The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed. Results Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes. Conclusion Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.
Collapse
Affiliation(s)
- Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Pascal A.T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Clemens G. Kaiser
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| |
Collapse
|
7
|
Dietzel M, Bernathova M, Clauser P, Kapetas P, Uder M, Baltzer PAT. Added value of clinical decision rules for the management of enhancing breast MRI lesions: A systematic comparison of the Kaiser score and the Göttingen score. Eur J Radiol 2023; 169:111185. [PMID: 37939606 DOI: 10.1016/j.ejrad.2023.111185] [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: 08/17/2023] [Revised: 10/16/2023] [Accepted: 11/02/2023] [Indexed: 11/10/2023]
Abstract
PURPOSE We investigated the added value of two internationally used clinical decision rules in the management of enhancing lesions on breast MRI. METHODS This retrospective, institutional review board approved study included consecutive patients from two different populations. Patients received breast MRI according to the recommendations of the European Society of Breast Imaging (EUSOBI). Initially, all examinations were assessed by expert readers without using clinical decision rules. All lesions rated as category 4 or 5 according to the Breast Imaging Reporting and Data System were histologically confirmed. These lesions were re-evaluated by an expert reader blinded to the histology. He assigned each lesion a Göttingen score (GS) and a Kaiser score (KS) on different occasions. To provide an estimate on inter-reader agreement, a second fellowship-trained reader assessed a subset of these lesions. Subgroup analyses based on lesion type (mass vs. non-mass), size (>1 cm vs. ≤ 1 cm), menopausal status, and significant background parenchymal enhancement were conducted. The areas under the ROC curves (AUCs) for the GS and KS were compared, and the potential to avoid unnecessary biopsies was determined according to previously established cutoffs (KS > 4, GS > 3) RESULTS: 527 lesions in 506 patients were included (mean age: 51.8 years, inter-quartile-range: 43.0-61.0 years). 131/527 lesions were malignant (24.9 %; 95 %-confidence-interval: 21.3-28.8). In all subgroups, the AUCs of the KS (median = 0.91) were higher than those of the GS (median = 0.83). Except for "premenopausal patients" (p = 0.057), these differences were statistically significant (p ≤ 0.01). Kappa agreement was higher for the KS (0.922) than for the GS (0.358). CONCLUSION Both the KS and the GS provided added value for the management of enhancing lesions on breast MRI. The KS was superior to the GS in terms of avoiding unnecessary biopsies and showed superior inter-reader agreement; therefore, it may be regarded as the clinical decision rule of choice.
Collapse
Affiliation(s)
- Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| |
Collapse
|
8
|
Meng L, Zhao X, Guo J, Lu L, Cheng M, Xing Q, Shang H, Zhang B, Chen Y, Zhang P, Zhang X. Improved Differential Diagnosis Based on BI-RADS Descriptors and Apparent Diffusion Coefficient for Breast Lesions: A Multiparametric MRI Analysis as Compared to Kaiser Score. Acad Radiol 2023; 30 Suppl 2:S93-S103. [PMID: 37236897 DOI: 10.1016/j.acra.2023.03.035] [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: 02/13/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 05/28/2023]
Abstract
RATIONALE AND OBJECTIVES To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions. MATERIALS AND METHODS A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 × 10-3 mm2/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models' discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared. RESULTS In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929-0.976 and AUC, 0.958; 95% CI: 0.931-0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885-0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS. CONCLUSION The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.
Collapse
Affiliation(s)
- Lingsong Meng
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.); Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China (L.M., P.Z.).
| | - Xin Zhao
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Jinxia Guo
- General Electric (GE) Healthcare, Beijing, China (J.G.).
| | - Lin Lu
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Meiying Cheng
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Qingna Xing
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Honglei Shang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Bohao Zhang
- Henan Key Laboratory of Child Brain Injury, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (B.Z.).
| | - Yan Chen
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Penghua Zhang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.); Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China (L.M., P.Z.).
| | - Xiaoan Zhang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| |
Collapse
|
9
|
Pan J, Huang X, Yang S, Ouyang F, Ouyang L, Wang L, Chen M, Zhou L, Du Y, Chen X, Deng L, Hu Q, Guo B. The added value of apparent diffusion coefficient and microcalcifications to the Kaiser score in the evaluation of BI-RADS 4 lesions. Eur J Radiol 2023; 165:110920. [PMID: 37320881 DOI: 10.1016/j.ejrad.2023.110920] [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: 04/27/2023] [Revised: 05/22/2023] [Accepted: 06/04/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE To explore the added value of combining microcalcifications or apparent diffusion coefficient (ADC) with the Kaiser score (KS) for diagnosing BI-RADS 4 lesions. METHODS This retrospective study included 194 consecutive patients with 201 histologically verified BI-RADS 4 lesions. Two radiologists assigned the KS value to each lesion. Adding microcalcifications, ADC, or both these criteria to the KS yielded KS1, KS2, and KS3, respectively. The potential of all four scores to avoid unnecessary biopsies was assessed using the sensitivity and specificity. Diagnostic performance was evaluated by the area under the curve (AUC) and compared between KS and KS1. RESULTS The sensitivity of KS, KS1, KS2, and KS3 ranged from 77.1% to 100.0%.KS1 yielded significantly higher sensitivity than other methods (P < 0.05), except for KS3 (P > 0.05), most of all, when assessing NME lesions. For mass lesions, the sensitivity of these four scores was comparable (p > 0.05). The specificity of KS, KS1, KS2, and KS3 ranged from 56.0% to 69.4%, with no statistically significant differences(P > 0.05), except between KS1 and KS2 (p < 0.05).The AUC of KS1 (0.877) was significantly higher than that of KS (0.837; P = 0.0005), particularly for assessing NME (0.847 vs 0.713; P < 0.0001). CONCLUSION KS can stratify BI-RADS 4 lesions to avoid unnecessary biopsies. Adding microcalcifications, but not adding ADC, as an adjunct to KS improves diagnostic performance, particularly for NME lesions. ADC provides no additional diagnostic benefit to KS. Thus, only combining microcalcifications with KS is most conducive to clinical practice.
Collapse
Affiliation(s)
- Jialing Pan
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Xiyi Huang
- Department of Clinical Laboratory, Lecong Hospital of Shunde, Foshan, Guangdong, China
| | - Shaomin Yang
- Department of Radiology, Lecong Hospital of Shunde, Foshan, Guangdong, China
| | - Fusheng Ouyang
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lizhu Ouyang
- Department of Ultrasound, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Liwen Wang
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Ming Chen
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lanni Zhou
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Yongxing Du
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Xinjie Chen
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lingda Deng
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China.
| | - Baoliang Guo
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China.
| |
Collapse
|
10
|
Bickel H, Clauser P, Pinker K, Helbich T, Biondic I, Brkljacic B, Dietzel M, Ivanac G, Krug B, Moschetta M, Neuhaus V, Preidler K, Baltzer P. Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database. Eur Radiol 2023; 33:5400-5410. [PMID: 37166495 PMCID: PMC10326122 DOI: 10.1007/s00330-023-09675-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVES To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. METHODS This was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. RESULTS A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached. CONCLUSIONS The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. CLINICAL RELEVANCE STATEMENT The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. KEY POINTS • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.
Collapse
Affiliation(s)
- Hubert Bickel
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Paola Clauser
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker
- Evelyn H. Lauder Breast Center, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Thomas Helbich
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Iva Biondic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Boris Brkljacic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Matthias Dietzel
- Dpt. of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Gordana Ivanac
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Barbara Krug
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Marco Moschetta
- Dpt. of Emergency and Organ Transplantation-Breast Care Unit, Aldo Moro University of Bari Medical School, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Victor Neuhaus
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Klaus Preidler
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Pascal Baltzer
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
| |
Collapse
|
11
|
Tang W, Zhang M, Xu C, Shao Y, Tang J, Gong S, Dong H, Sheng M. Diagnostic efficiency of multi-modal MRI based deep learning with Sobel operator in differentiating benign and malignant breast mass lesions-a retrospective study. PeerJ Comput Sci 2023; 9:e1460. [PMID: 37547396 PMCID: PMC10403185 DOI: 10.7717/peerj-cs.1460] [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: 12/08/2022] [Accepted: 06/06/2023] [Indexed: 08/08/2023]
Abstract
Purpose To compare the diagnostic efficiencies of deep learning single-modal and multi-modal for the classification of benign and malignant breast mass lesions. Methods We retrospectively collected data from 203 patients (207 lesions, 101 benign and 106 malignant) with breast tumors who underwent breast magnetic resonance imaging (MRI) before surgery or biopsy between January 2014 and October 2020. Mass segmentation was performed based on the three dimensions-region of interest (3D-ROI) minimum bounding cube at the edge of the lesion. We established single-modal models based on a convolutional neural network (CNN) including T2WI and non-fs T1WI, the dynamic contrast-enhanced (DCE-MRI) first phase was pre-contrast T1WI (d1), and Phases 2, 4, and 6 were post-contrast T1WI (d2, d4, d6); and Multi-modal fusion models with a Sobel operator (four_mods:T2WI, non-fs-T1WI, d1, d2). Training set (n = 145), validation set (n = 22), and test set (n = 40). Five-fold cross validation was performed. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, and area under the ROC curve (AUC) were used as evaluation indicators. Delong's test compared the diagnostic performance of the multi-modal and single-modal models. Results All models showed good performance, and the AUC values were all greater than 0.750. Among the single-modal models, T2WI, non-fs-T1WI, d1, and d2 had specificities of 77.1%, 77.2%, 80.2%, and 78.2%, respectively. d2 had the highest accuracy of 78.5% and showed the best diagnostic performance with an AUC of 0.827. The multi-modal model with the Sobel operator performed better than single-modal models, with an AUC of 0.887, sensitivity of 79.8%, specificity of 86.1%, and positive prediction value of 85.6%. Delong's test showed that the diagnostic performance of the multi-modal fusion models was higher than that of the six single-modal models (T2WI, non-fs-T1WI, d1, d2, d4, d6); the difference was statistically significant (p = 0.043, 0.017, 0.006, 0.017, 0.020, 0.004, all were greater than 0.05). Conclusions Multi-modal fusion deep learning models with a Sobel operator had excellent diagnostic value in the classification of breast masses, and further increase the efficiency of diagnosis.
Collapse
Affiliation(s)
- Weixia Tang
- Radiology Department, Affiliated Hospital 2 of Nantong University, Nantong First People’s Hospital, NanTong, Jiangsu, China
| | - Ming Zhang
- Radiology Department, Affiliated Hospital 2 of Nantong University, Nantong First People’s Hospital, NanTong, Jiangsu, China
| | - Changyan Xu
- School of Transportation and Civil Engineering, Nantong University, Nantong, China
| | - Yeqin Shao
- School of Transportation and Civil Engineering, Nantong University, Nantong, China
| | - Jiahuan Tang
- Radiology Department, Affiliated Hospital 2 of Nantong University, Nantong First People’s Hospital, NanTong, Jiangsu, China
| | - Shenchu Gong
- Radiology Department, Affiliated Hospital 2 of Nantong University, Nantong First People’s Hospital, NanTong, Jiangsu, China
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Meihong Sheng
- Radiology Department, Affiliated Hospital 2 of Nantong University, Nantong First People’s Hospital, NanTong, Jiangsu, China
| |
Collapse
|
12
|
Zhou XZ, Liu LH, He S, Yao HF, Chen LP, Deng C, Li SL, Zhang XY, Lai H. Diagnostic value of Kaiser score combined with breast vascular assessment from breast MRI for the characterization of breast lesions. Front Oncol 2023; 13:1165405. [PMID: 37483510 PMCID: PMC10359820 DOI: 10.3389/fonc.2023.1165405] [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: 02/14/2023] [Accepted: 06/06/2023] [Indexed: 07/25/2023] Open
Abstract
Objectives The Kaiser scoring system for breast magnetic resonance imaging is a clinical decision-making tool for diagnosing breast lesions. However, the Kaiser score (KS) did not include the evaluation of breast vascularity. Therefore, this study aimed to use KS combined with breast vascular assessment, defined as KS*, and investigate the effectiveness of KS* in differentiating benign from malignant breast lesions. Methods This retrospective study included 223 patients with suspicious breast lesions and pathologically verified results. The histopathological diagnostic criteria were according to the fifth edition of the WHO classification of breast tumors. The KS* was obtained after a joint evaluation combining the original KS and breast vasculature assessment. The receiver operating characteristic (ROC) curve was used for comparing differences in the diagnostic performance between KS* and KS, and the area under the receiver operating characteristic (AUC) was compared. Results There were 119 (53.4%) benign and 104 (46.6%) malignant lesions in total. The overall sensitivity, specificity, and accuracy of increased ipsilateral breast vascularity were 69.2%, 76.5%, and 73.1%, respectively. The overall sensitivity, specificity, and accuracy of AVS were 82.7%, 76.5%, and 79.4%, respectively. For all lesions included the AUC of KS* was greater than that of KS (0.877 vs. 0.858, P = 0.016). The largest difference in AUC was observed in the non-mass subgroup (0.793 vs. 0.725, P = 0.029). Conclusion Ipsilaterally increased breast vascularity and a positive AVS sign were significantly associated with malignancy. KS combined with breast vascular assessment can effectively improve the diagnostic ability of KS for breast lesions, especially for non-mass lesions.
Collapse
Affiliation(s)
- Xin-zhu Zhou
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lian-hua Liu
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuang He
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui-fang Yao
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Li-ping Chen
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chen Deng
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuang-Ling Li
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Hua Lai
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
13
|
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.
Collapse
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,
| |
Collapse
|
14
|
Jones LI, Klimczak K, Geach R. Breast MRI: an illustration of benign findings. Br J Radiol 2023; 96:20220280. [PMID: 36488196 PMCID: PMC9975519 DOI: 10.1259/bjr.20220280] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 08/24/2022] [Accepted: 09/29/2022] [Indexed: 12/13/2022] Open
Abstract
Despite its unparalleled sensitivity for aggressive breast cancer, breast MRI continually excites criticism for a specificity that lags behind that of modern mammographic techniques. Radiologists reporting breast MRI need to recognise the range of benign appearances on breast MRI to avoid unnecessary biopsy. This review summarises the reported diagnostic accuracy of breast MRI with particular attention to the technique's specificity, provides a referenced reporting strategy and discusses factors that compromise diagnostic confidence. We then present a pictorial review of benign findings on breast MRI. Enhancing radiological skills to discriminate malignant from benign findings will minimise false positive biopsies, enabling optimal use of multiparametric breast MRI for the benefit of screening clients and breast cancer patients.
Collapse
Affiliation(s)
- Lyn Isobel Jones
- Bristol Breast Care Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Katherine Klimczak
- Bristol Breast Care Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Rebecca Geach
- Bristol Breast Care Centre, North Bristol NHS Trust, Bristol, United Kingdom
| |
Collapse
|
15
|
Kang Y, Li Z, Yang G, Xue J, Zhang L, Rong X. Diagnostic performance of the Kaiser score in the evaluation of breast lesions on contrast-enhanced mammography. Eur J Radiol 2022; 156:110524. [PMID: 36126352 DOI: 10.1016/j.ejrad.2022.110524] [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: 05/10/2022] [Revised: 08/14/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVES We aimed to investigate whether the Kaiser score (KS) could improve the diagnostic performance of breast imaging reporting and data system (BI-RADS) in evaluating breast enhancing lesions on contrast-enhanced mammography (CEM). METHODS Three hundred fifty-nine patients with 375 lesions (231 malignant and 144 benign) were included in this retrospective study from April 2019 to December 2021.Two readers with different levels of experience in breast imaging were asked to give a BI-RADS assessment category according to the CEM BI-RADS and final score based on the KS. The diagnostic performance of all lesions, mass and non-mass enhancement (NME) were assessed by receiver operating characteristic (ROC) analysis, and the areas under the ROC curve (AUCs) were measured. The weighted kappa coefficients were calculated to investigate the interreader agreement. RESULTS The AUCs of the KS for all lesions were 0.915 (95 %CI: 0.884-0.947) and 0.876 (95 %CI: 0.838-0.914) for two readers. When mass and NME were evaluated separately, the AUCs of the KS for mass were higher than those for NME (p < 0.001). The AUCs of BI-RADS for all lesion diagnoses ranged between 0.821 (95 %CI: 0.778-0.864) and 0.842(95 %CI: 0.801-0.883) for two readers. The AUCs of the KS were higher than those of BI-RADS (p < 0.001, p = 0.016). There were no significant differences in the sensitivity between the KS (97.4 %) and BI-RADS (99.6 %) for all lesions (p = 0.130). The specificity of the KS was significantly higher than that of BI-RADS (p < 0.001). Compared with BI-RADS, the application of the KS could have potentially obviated 41.7 % to 47.9 % unnecessary biopsies in 144 benign lesions. Interreader agreement between the two readers of the KS was almost perfect (k = 0.883 [95 % CI: 0.842-0.924]). CONCLUSION The use of the KS provided a high diagnostic performance in distinguishing malignant and benign breast lesions on CEM and outperformed BI-RADS. The application of the KS can downgrade up to 47.9% of unnecessary biopsies of benign breast lesions.
Collapse
Affiliation(s)
- Yihe Kang
- Department of Radiology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Zhigang Li
- Department of Radiology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Guang Yang
- Department of Radiology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Jing Xue
- Department of Radiology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Lingling Zhang
- Department of Pathology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Xiaocui Rong
- Department of Radiology. The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China.
| |
Collapse
|
16
|
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.
Collapse
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,
| |
Collapse
|
17
|
Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling. Eur Radiol 2022; 32:6608-6618. [PMID: 35726099 PMCID: PMC9815725 DOI: 10.1007/s00330-022-08899-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/10/2022] [Accepted: 05/19/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.
Collapse
|
18
|
Can DWI provide additional value to Kaiser score in evaluation of breast lesions. Eur Radiol 2022; 32:5964-5973. [PMID: 35357535 DOI: 10.1007/s00330-022-08674-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). METHODS Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10-3mm2/s, 1.4 × 10-3mm2/s, 1.53 × 10-3mm2/s, and 1.6 × 10-3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. RESULTS The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). CONCLUSION KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. KEY POINTS • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.
Collapse
|
19
|
The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6:42. [PMID: 35989400 PMCID: PMC9393116 DOI: 10.1186/s41747-022-00291-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
Collapse
|
20
|
Pötsch N, Korajac A, Stelzer P, Kapetas P, Milos RI, Dietzel M, Helbich TH, Clauser P, Baltzer PAT. Breast MRI: does a clinical decision algorithm outweigh reader experience? Eur Radiol 2022; 32:6557-6564. [PMID: 35852572 PMCID: PMC9474540 DOI: 10.1007/s00330-022-09015-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/30/2022] [Accepted: 07/02/2022] [Indexed: 11/28/2022]
Abstract
Objectives Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. Methods Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. Results A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). Conclusion The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. Key Points • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-09015-8.
Collapse
Affiliation(s)
- Nina Pötsch
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Aida Korajac
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Philipp Stelzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Panagiotis Kapetas
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Ruxandra-Iulia Milos
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Matthias Dietzel
- Institute of Radiology, Erlangen University Hospital, Maximiliansplatz 2, 91054, Erlangen, Germany
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Paola Clauser
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Pascal A T Baltzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, A-1090, Vienna, Austria.
| |
Collapse
|
21
|
Stelzer P, Clauser P, Vatteroni G, Kapetas P, Helbich T, Baltzer P. How much can abbreviated protocols for breast MRI increase patient throughput? A multi-centric evaluation. Eur J Radiol 2022; 154:110436. [DOI: 10.1016/j.ejrad.2022.110436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/23/2022] [Accepted: 07/04/2022] [Indexed: 11/03/2022]
|
22
|
Baltzer PAT, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. ROFO-FORTSCHR RONTG 2022; 194:1216-1228. [PMID: 35613905 DOI: 10.1055/a-1829-5985] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis. METHOD Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed. RESULTS The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios. KEY POINTS · The KS as an evidence-based decision rule to objectively distinguish benign from malignant breast lesions is based on information contained in T2w und dynamic contrast-enhanced T1w sequences and is largely independent of specific examination protocols.. · The KS diagnostic criteria are in line with the MRI BI-RADS lexicon. We focused on defining a default category to be applied in the case of equivocal imaging criteria.. · The KS reflects increasing probabilities of malignancy and, together with the clinical context, assists individual decision making.. CITATION FORMAT · Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1829-5985.
Collapse
Affiliation(s)
- Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Medical University of Vienna, Wien, Austria
| | - Kathrin Barbara Krug
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Köln, Germany
| | | |
Collapse
|
23
|
Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. ROFO-FORTSCHR RONTG 2022; 194:966-974. [PMID: 35439830 DOI: 10.1055/a-1775-8572] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND The MRI of the breast is of great importance in the diagnosis of disorders of the breast. This can be stated for the primary diagnosis as well as the follow up. Of special interest is diffusion weighted imaging (DWI), which has an increasingly important role. The present review provides results regarding the diagnostic and prognostic relevance of DWI for disorders of the breast. METHODS Under consideration of the recently published literature, the clinical value of DWI of the breast is discussed. Several diagnostic applications are shown, especially for the primary diagnosis of unclear tumors of the breast, the prediction of the axillary lymph node status and the possibility of a native screening. Moreover, correlations between DWI and histopathology features and treatment prediction with DWI are provided. RESULTS Many studies have shown the diagnostic value of DWI for the primary diagnosis of intramammary lesions. Benign lesions of the breast have significantly higher apparent diffusion coefficients (ADC values) compared to malignant tumors. This can be clinically used to reduce unnecessary biopsies in clinical routine. However, there are inconclusive results for the prediction of the histological subtype of the breast cancer. DWI can aid in the prediction of treatment to neoadjuvant chemotherapy. CONCLUSION DWI is a very promising imaging modality, which should be included in the standard protocol of the MRI of the breast. DWI can provide clinically value in the diagnosis as well as for prognosis in breast cancer. KEY POINTS · DWI can aid in the discrimination between benign and malignant tumors of the breast and therefore avoiding unnecessary biopsies.. · The ADC value cannot discriminate between immunhistochemical subtypes of the breast cancer. · The ADC value of breast cancer increases under neoadjuvant chemotherapy and can by this aid in treatment prediction.. · There is definite need of standardisation for clinical translation. CITATION FORMAT · Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8572.
Collapse
Affiliation(s)
- Hans Jonas Meyer
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
| | - Mireille Martin
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
| | - Timm Denecke
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
ADC und KS zur Differenzierung zwischen maligen und benignen Brustherden. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1556-4871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
26
|
Vassiou K, Fanariotis M, Tsougos I, Fezoulidis I. Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography. Acta Radiol 2021; 63:1332-1343. [PMID: 34605311 DOI: 10.1177/02841851211041822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. PURPOSE To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. MATERIAL AND METHODS A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. RESULTS In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. CONCLUSION We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.
Collapse
Affiliation(s)
- Katerina Vassiou
- Department of Anatomy, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
| | - Michael Fanariotis
- Department of Radiology, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
- Department of Radiology, Sykehuset Telemark HF, Skien, Telemark, Norway
| | - Ioannis Tsougos
- Department of Medical Physics, Faculty of Medicine, University of Thessaly, Larissa, Greece
| | - Ioannis Fezoulidis
- Department of Radiology, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
| |
Collapse
|
27
|
Baltzer PAT. Supplemental screening using breast MRI in women with mammographically dense breasts. Eur J Radiol 2020; 136:109513. [PMID: 33422397 DOI: 10.1016/j.ejrad.2020.109513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 12/28/2020] [Indexed: 01/08/2023]
Affiliation(s)
- Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
| |
Collapse
|