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Vatteroni G, Dietzel M, Baltzer PAT. Can structured integration of BI-RADS criteria by a clinical decision rule reduce the number of unnecessary biopsies in BI-RADS 4 lesions? A systematic review and meta-analysis. Eur Radiol 2025; 35:1504-1513. [PMID: 39694886 PMCID: PMC11836227 DOI: 10.1007/s00330-024-11274-6] [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: 07/25/2024] [Revised: 09/12/2024] [Accepted: 11/04/2024] [Indexed: 12/20/2024]
Abstract
AIM This systematic review and meta-analysis investigate the added value of structured integration of Breast Imaging Reporting and Data System (BI-RADS) criteria using the Kaiser score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions. MATERIAL AND METHODS A systematic review and meta-analysis were conducted using predefined criteria. Eligible articles, published in English until May 2024, dealt with KS in the context of BI-RADS 4 MRI. Two reviewers extracted study characteristics, including true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were calculated using bivariate random effects. Fagan nomograms identified the maximum pre-test probability at which post-test probabilities of a negative MRI aligned with the 2% malignancy rate benchmark for downgrading BI-RADS 4 to BI-RADS 3. I² statistics and meta-regression explored sources of heterogeneity. p-values < 0.05 were considered significant. RESULTS Seven studies with 1877 lesions (833 malignant, 1044 benign) were included. The average breast cancer prevalence was 47.3%. Pooled sensitivity was 94.3% (95%-CI 88.9%-97.1%), and pooled specificity was 68.1% (95%-CI 56.6%-77.7%) using a random effects model. Overall, 52/833 cases were FNs (6.2%). Fagan nomograms showed that KS could rule out breast cancer in BI-RADS 4 lesions at a pre-test probability of 20.3% for all lesions, 25.4% for masses, and 15.2% for non-mass lesions. CONCLUSIONS In MRI-assessed BI-RADS 4 lesions, applying structured BI-RADS criteria with the KS reduces unnecessary biopsies by 70% with a 6.2% FN rate. Breast cancer can be ruled out up to pre-test probabilities of 20.3%. KEY POINTS Question What, if any, value is added by structured integration of BI-RADS criteria using the Kaiser Score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions? Findings The structured integration of BI-RADS criteria using the Kaiser Score (KS) reduces unnecessary biopsies in BI-RADS 4 lesions by 70%. Clinical relevance The structured approach offered by the Kaiser Score (KS) avoids unnecessary recalls, potentially reducing patient anxiety, lessening the burden on medical personnel, and the need for further imaging and biopsies due to more objective and thus efficient clinical decision-making in evaluating BI-RADS 4 lesions.
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Affiliation(s)
- Giulia Vatteroni
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Milan, Pieve Emanuele, Italy
| | - Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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van der Veer EL, Rozemond F, Generaal MI, Bluekens AMJ, Coolen AMP, Voogd AC, Duijm LEM. Interhospital variations in diagnostic work-up following recall at biennial screening mammography-a population-based study. Eur Radiol 2024:10.1007/s00330-024-11302-5. [PMID: 39708083 DOI: 10.1007/s00330-024-11302-5] [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: 06/28/2024] [Revised: 09/29/2024] [Accepted: 11/17/2024] [Indexed: 12/23/2024]
Abstract
OBJECTIVES Quality control in breast cancer screening programmes has been subject of several studies. However, less is known about the clinical diagnostic work-up in recalled women with a suspicious finding at screening mammography. The current study focuses on interhospital differences in diagnostic work-up strategies. MATERIALS AND METHODS In this retrospective analysis, using a prospectively obtained database, we included 17,809 women who participated in the Dutch national screening programme between 2009 and 2019 and were recalled to a hospital for analysis of a suspicious mammographic abnormality. The diagnostic work-up (e.g., type and frequency of additional imaging and biopsy) in the different hospitals were compared and analysed by multivariable analysis to correct for confounders. RESULTS Use of biopsy varied from 36.7% to 48.7% (p < 0.001) between hospitals, and the use of problem-solving magnetic resonance imaging (MRI) from 2.1% to 6.9% (p < 0.001). These interhospital differences remained after correction for patients and tumour characteristics. The percentage of women with a delayed breast cancer diagnosis, defined as histopathological confirmation of breast cancer more than three months after recall or first analysis in the hospital, varied from 2.7% to 6.1% between hospitals (p = 0.07). CONCLUSIONS In our screening region interhospital differences were observed in diagnostic work-up following recall at biennial screening mammography. Though statistically significant, absolute differences were small, and therefore, their clinical impact appears to be limited. KEY POINTS Question It is unclear how diagnostic work-up strategies vary between hospitals for women recalled after suspicious findings in breast cancer screening. Findings Significant differences in biopsy techniques and the use of problem-solving MRI were observed, though the clinical impact of these variations is likely to be marginal. Clinical relevance Evaluation of interhospital variation in the diagnostic work-up strategies after recall may aid in optimising the quality of breast cancer care and, indirectly, the effectiveness of the screening programme.
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Affiliation(s)
- Eline L van der Veer
- Elisabeth TweeSteden Hospital, Hilvarenbeekse Weg 60, 5022 GC, Tilburg, The Netherlands.
- Erasmus Medical Centre, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| | - Fenna Rozemond
- Erasmus Medical Centre, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Manon I Generaal
- Maastricht University, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
| | - Adriana M J Bluekens
- Elisabeth TweeSteden Hospital, Hilvarenbeekse Weg 60, 5022 GC, Tilburg, The Netherlands
| | - Angela M P Coolen
- Elisabeth TweeSteden Hospital, Hilvarenbeekse Weg 60, 5022 GC, Tilburg, The Netherlands
| | - Adri C Voogd
- Maastricht University, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
| | - Lucien E M Duijm
- Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, The Netherlands
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Akwo J, Hadadi I, Ekpo E. Diagnostic Efficacy of Five Different Imaging Modalities in the Assessment of Women Recalled at Breast Screening-A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:3505. [PMID: 39456600 PMCID: PMC11505902 DOI: 10.3390/cancers16203505] [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: 09/02/2024] [Revised: 10/14/2024] [Accepted: 10/15/2024] [Indexed: 10/28/2024] Open
Abstract
There are variations in the assessment pathways for women recalled at screening, and the imaging assessment pathway with the best diagnostic outcome is poorly understood. This paper examines the efficacy of five imaging modalities for the assessment of screen-recalled breast lesions. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) strategy was employed to identify studies that assessed the efficacy of imaging modalities in the assessment of lesions recalled at screening from the following eight databases: Medline, Web of Science, Embase, Scopus, Science Direct, PubMed, CINAHL, and Global Health. Search terms included "Breast assessment" AND "Diagnostic Workup" OR "Mammography" AND "Digital Breast tomosynthesis" AND "contrast enhanced mammography and Magnetic Resonance imaging" AND "breast ultrasound". Studies that examined the performance of digital mammography (DM), digital breast tomosynthesis (DBT), handheld ultrasound (HHUS), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in screen-recalled lesions were reviewed. Meta-analyses of these studies were conducted using the MetaDisc 2.0 software package. Results: Fifty-four studies met the inclusion criteria and examined between one and three imaging modalities. Pooled results of each imaging modality demonstrated that CEM has the highest sensitivity (95; 95% CI: 90-97) followed by MRI (93; 95% CI: 88-96), DBT (91; 95% CI: 87-94), HHUS (90; 95% CI: 86-93), and DM (85; 95% CI: 78-90). The DBT demonstrated the highest specificity (85; 95% CI: 75-91) followed by DM (77; 95% CI: 66-85), CEM (73; 95% CI: 63-81), MRI (69; 95% CI: 55-81), and HHUS (65; 95% CI: 46-80). Conclusions: The CEM, MRI, DBT, and HHUS demonstrate excellent performance in correctly identifying and classifying cancer lesions referred for diagnostic work-up, but HHUS, MRI, and CEM have a more limited ability to discriminate benign lesions than DBT and DM.
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Affiliation(s)
- Judith Akwo
- Medical Image Optimisation and Perception Group, Faculty of Medicine and Health, Discipline of Medical Imaging Science, The University of Sydney, Sydney, NSW 2050, Australia
| | - Ibrahim Hadadi
- Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha 62529, Saudi Arabia;
| | - Ernest Ekpo
- Medical Image Optimisation and Perception Group, Faculty of Medicine and Health, Discipline of Medical Imaging Science, The University of Sydney, Sydney, NSW 2050, Australia
- Department of Imaging and Radiation Therapy, Brookfield Health Sciences Complex, University College Cork, College Road, T12 AK54 Cork, Ireland
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Javor D, Bennani-Baiti BI, Clauser P, Kifjak D, Baltzer PAT. Automated analysis of the total choline resonance peak in breast proton magnetic resonance spectroscopy. NMR IN BIOMEDICINE 2024; 37:e5054. [PMID: 37794648 DOI: 10.1002/nbm.5054] [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: 03/17/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023]
Abstract
The aim of the current study was to compare the performance of fully automated software with human expert interpretation of single-voxel proton magnetic resonance spectroscopy (1H-MRS) spectra in the assessment of breast lesions. Breast magnetic resonance imaging (MRI) (including contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted imaging) and 1H-MRS images of 74 consecutive patients were acquired on a 3-T positron emission tomography-MRI scanner then automatically imported into and analyzed by SpecTec-ULR 1.1 software (LifeTec Solutions GmbH). All ensuing 117 spectra were additionally independently analyzed and interpreted by two blinded radiologists. Histopathology of at least 24 months of imaging follow-up served as the reference standard. Nonparametric Spearman's correlation coefficients for all measured parameters (signal-to-noise ratio [SNR] and integral of total choline [tCho]), Passing and Bablok regression, and receiver operating characteristic analysis, were calculated to assess test diagnostic performance, as well as to compare automated with manual reading. Based on 117 spectra of 74 patients, the area under the curve for tCho SNR and integrals ranged from 0.768 to 0.814 and from 0.721 to 0.784 to distinguish benign from malignant tissue, respectively. Neither method displayed significant differences between measurements (automated vs. human expert readers, p > 0.05), in line with the results from the univariate Spearman's rank correlation coefficients, as well as the Passing and Bablok regression analysis. It was concluded that this pilot study demonstrates that 1H-MRS data from breast MRI can be automatically exported and interpreted by SpecTec-ULR 1.1 software. The diagnostic performance of this software was not inferior to human expert readers.
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Affiliation(s)
- Domagoj Javor
- Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Radiology, University Hospital Krems, Krems, Austria
- Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Barbara I Bennani-Baiti
- Department of Radiology, University Hospital Krems, Krems, Austria
- Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Paola Clauser
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daria Kifjak
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Radiology, UMass Memorial Medical Center and University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Pascal A T Baltzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Fischer U. Breast MRI - The champion in the millimeter league: MIO breast MRI - The method of choice in women with dense breasts. Eur J Radiol 2023; 167:111053. [PMID: 37659208 DOI: 10.1016/j.ejrad.2023.111053] [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: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/04/2023]
Abstract
We perform MRI of the breast as a first pass technique. We successfully established 10-minute-protocols (including T2 images) with a fixed dosage of 5 ml 1 M CM. A high spatial resolution of 526 × 526, better 672 × 672 or maximum (1.024 × 1.024, MIO MRI) is vital to achieve best results. We use fixation tools to avoid motion artifacts. Motion correction algorithms can, however, often eliminate such artifacts when they are present. In initial breast MRI exams, morphologic features are the most important criteria for lesion evaluation. If previous exams are available for comparison, the main criteria indicating a suspicious lesion are an increase in lesion size or the depiction of new lesions. High quality HR MRI of the breast is the method of choice in women with dense or extremely dense breasts in all cases (screening, assessment, follow up). In density type A or B, MRI can be helpful in defined constellations, e.g. when MX and US are limited or contraindicated. According to our experience, 95% or more of all carcinomas of the breast are detectable on MRI. The remaining 5% of MRI-occult lesions are intraductal tumors or very small invasive carcinomas depicted with mammography due to associated microcalcifications. MRI is, however, superior to all other imaging modalities in the detection of the clinically relevant DCIS (high risk DCIS, intermediate type). Consecutive MRI examinations in intervals of 12 to 24 months allow a reliable detection of invasive breast cancer with an average size of 7-8 mm. This corresponds to a rate of metastasis-free locoregional lymph nodes in >95% of cases. The rate of interval cancers is <2%. In conclusion, this strategy may increase the overall-lifetime survival of breast cancer patients to more than 95%. Inversely, mortality may be reduced to <5%. Taking these improvements in early breast cancer detection and survival that can be achieved through the implementation of QA HR MRI of the breast into account, it should be discussed to modify oncologic guidelines for the treatment of breast cancer. MRI is the best diagnostic tool we have and according to our experience, a first pass, quality-assured high-resolution breast MRI protocol provides best diagnostic results at minimal procedural effort.
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Affiliation(s)
- Uwe Fischer
- Diagnostic Breast Care Center, Bahnhofsallee 1d, 37081 Goettingen, Germany.
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Cozzi A, Di Leo G, Houssami N, Gilbert FJ, Helbich TH, Álvarez Benito M, Balleyguier C, Bazzocchi M, Bult P, Calabrese M, Camps Herrero J, Cartia F, Cassano E, Clauser P, de Lima Docema MF, Depretto C, Dominelli V, Forrai G, Girometti R, Harms SE, Hilborne S, Ienzi R, Lobbes MBI, Losio C, Mann RM, Montemezzi S, Obdeijn IM, Ozcan UA, Pediconi F, Pinker K, Preibsch H, Raya Povedano JL, Rossi Saccarelli C, Sacchetto D, Scaperrotta GP, Schlooz M, Szabó BK, Taylor DB, Ulus ÖS, Van Goethem M, Veltman J, Weigel S, Wenkel E, Zuiani C, Sardanelli F. Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study. Eur Radiol 2023; 33:6213-6225. [PMID: 37138190 PMCID: PMC10415233 DOI: 10.1007/s00330-023-09600-5] [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: 09/24/2022] [Revised: 01/19/2023] [Accepted: 02/22/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVES To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes. METHODS The MIPA observational study enrolled women aged 18-80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis. RESULTS A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p ≤ 0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p < 0.001) for S-MRI, 1.0 (p = 0.957) for D-MRI, and 1.9 (p < 0.001) for P-MRI. CONCLUSIONS Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer. KEY POINTS • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups.
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Affiliation(s)
- Andrea Cozzi
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Nehmat Houssami
- The Daffodil Centre, Faculty of Medicine and Health, The University of Sydney (Joint Venture with Cancer Council NSW), Sydney, Australia
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | | | - Corinne Balleyguier
- Department of Radiology, Institut Gustave Roussy, Villejuif, France
- BioMaps (UMR1281), INSERM, CEA, CNRS, Université Paris-Saclay, Villejuif, France
| | - Massimo Bazzocchi
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Peter Bult
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Massimo Calabrese
- Unit of Oncological and Breast Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Francesco Cartia
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | | | - Catherine Depretto
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gábor Forrai
- Department of Radiology, MHEK Teaching Hospital, Semmelweis University, Budapest, Hungary
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Steven E Harms
- Breast Center of Northwest Arkansas, Fayetteville, AR, USA
| | - Sarah Hilborne
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Raffaele Ienzi
- Department of Radiology, Di.Bi.MED, Policlinico Universitario Paolo Giaccone, Università degli Studi di Palermo, Palermo, Italy
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudio Losio
- Department of Breast Radiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefania Montemezzi
- Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy
| | - Inge-Marie Obdeijn
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Umit A Ozcan
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma "La Sapienza", Rome, Italy
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | | | | | - Daniela Sacchetto
- Kiwifarm S.r.l, La Morra, Italy
- Disaster Medicine Service 118, ASL CN1, Saluzzo, Italy
- CRIMEDIM, Research Center in Emergency and Disaster Medicine, Università degli Studi del Piemonte Orientale "Amedeo Avogadro", Novara, Italy
| | | | - Margrethe Schlooz
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Botond K Szabó
- Department of Radiology, Barking Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Donna B Taylor
- Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia
- Department of Radiology, Royal Perth Hospital, Perth, Australia
| | - Özden S Ulus
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Mireille Van Goethem
- Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Department of Radiology, Multidisciplinary Breast Clinic, Antwerp University Hospital, University of Antwerp, Antwerpen, Belgium
| | - Jeroen Veltman
- Maatschap Radiologie Oost-Nederland, Oldenzaal, The Netherlands
| | - Stefanie Weigel
- Institute of Clinical Radiology and Reference Center for Mammography, University of Münster, Münster, Germany
| | - Evelyn Wenkel
- Department of Radiology, University Hospital of Erlangen, Erlangen, Germany
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy.
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
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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: 1.3] [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.
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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.
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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: 20] [Impact Index Per Article: 6.7] [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.
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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
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Cozzi A, Schiaffino S, Fanizza M, Magni V, Menicagli L, Monaco CG, Benedek A, Spinelli D, Di Leo G, Di Giulio G, Sardanelli F. Contrast-enhanced mammography for the assessment of screening recalls: a two-centre study. Eur Radiol 2022; 32:7388-7399. [PMID: 35648209 PMCID: PMC9668944 DOI: 10.1007/s00330-022-08868-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/19/2022] [Accepted: 05/08/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To evaluate the potential of contrast-enhanced mammography (CEM) for reducing the biopsy rate of screening recalls. METHODS Recalled women were prospectively enrolled to undergo CEM alongside standard assessment (SA) through additional views, tomosynthesis, and/or ultrasound. Exclusion criteria were symptoms, implants, allergy to contrast agents, renal failure, and pregnancy. SA and CEM were independently evaluated by one of six radiologists, who recommended biopsy or 2-year follow-up. Biopsy rates according to SA or recombined CEM (rCEM) were compared with the McNemar's test. Diagnostic performance was calculated considering lesions with available final histopathology. RESULTS Between January 2019 and July 2021, 220 women were enrolled, 207 of them (median age 56.6 years) with 225 suspicious findings analysed. Three of 207 patients (1.4%) developed mild self-limiting adverse reactions to iodinated contrast agent. Overall, 135/225 findings were referred for biopsy, 90/225 by both SA and rCEM, 41/225 by SA alone and 4/225 by rCEM alone (2/4 being one DCIS and one invasive carcinoma). The rCEM biopsy rate (94/225, 41.8%, 95% CI 35.5-48.3%) was 16.4% lower (p < 0.001) than the SA biopsy rate (131/225, 58.2%, 95% CI 51.7-64.5%). Considering the 124/135 biopsies with final histopathology (44 benign, 80 malignant), rCEM showed a 93.8% sensitivity (95% CI 86.2-97.3%) and a 65.9% specificity (95% CI 51.1-78.1%), all 5 false negatives being ductal carcinoma in situ detectable as suspicious calcifications on low-energy images. CONCLUSIONS Compared to SA, the rCEM-based work-up would have avoided biopsy for 37/225 (16.4%) suspicious findings. Including low-energy images in interpretation provided optimal overall CEM sensitivity. KEY POINTS • The work-up of suspicious findings detected at mammographic breast cancer screening still leads to a high rate of unnecessary biopsies, involving between 2 and 6% of screened women. • In 207 recalled women with 225 suspicious findings, recombined images of contrast-enhanced mammography (CEM) showed a 93.8% sensitivity and a 65.9% specificity, all 5 false negatives being ductal carcinoma in situ detectable on low-energy images as suspicious calcifications. • CEM could represent an easily available one-stop shop option for the morphofunctional assessment of screening recalls, potentially reducing the biopsy rate by 16.4%.
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Affiliation(s)
- Andrea Cozzi
- grid.4708.b0000 0004 1757 2822Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy
| | - Simone Schiaffino
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Marianna Fanizza
- grid.419425.f0000 0004 1760 3027Department of Breast Radiology, Fondazione IRCCS Policlinico San Matteo, Viale Camillo Golgi 19, 27100 Pavia, Italy
| | - Veronica Magni
- grid.4708.b0000 0004 1757 2822Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy
| | - Laura Menicagli
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Cristian Giuseppe Monaco
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Adrienn Benedek
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Diana Spinelli
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Giovanni Di Leo
- grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Giuseppe Di Giulio
- grid.419425.f0000 0004 1760 3027Department of Breast Radiology, Fondazione IRCCS Policlinico San Matteo, Viale Camillo Golgi 19, 27100 Pavia, Italy
| | - Francesco Sardanelli
- grid.4708.b0000 0004 1757 2822Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy ,grid.419557.b0000 0004 1766 7370Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
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