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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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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: 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.
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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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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: 11] [Impact Index Per Article: 5.5] [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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