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Pötsch N, Clauser P, Kapetas P, Baykara Ulusan M, Helbich T, Baltzer P. Enhancing the Kaiser score for lesion characterization in unenhanced breast MRI. Eur J Radiol 2024; 176:111520. [PMID: 38820953 DOI: 10.1016/j.ejrad.2024.111520] [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: 04/04/2024] [Revised: 05/15/2024] [Accepted: 05/19/2024] [Indexed: 06/02/2024]
Abstract
PURPOSE To adapt the methodology of the Kaiser score, a clinical decision rule for lesion characterization in breast MRI, for unenhanced protocols. METHOD In this retrospective IRB-approved cross-sectional study, we included 93 consecutive patients who underwent breast MRI between 2021 and 2023 for further work-up of BI-RADS 0, 3-5 in conventional imaging or for staging purposes (BI-RADS 6). All patients underwent biopsy for histologic verification or were followed for a minimum of 12 months. MRI scans were conducted using 1.5 T or 3 T scanners using dedicated breast coils and a protocol in line with international recommendations including DWI and ADC. Lesion characterization relied solely on T2w and DWI/ADC-derived features (such as lesion type, margins, shape, internal signal, surrounding tissue findings, ADC value). Statistical analysis was done using decision tree analysis aiming to distinguish benign (histology/follow-up) from malignant outcomes. RESULTS We analyzed a total of 161 lesions (81 of them non-mass) with a malignancy rate of 40%. Lesion margins (spiculated, irregular, or circumscribed) were identified as the most important criterion within the decision tree, followed by the ADC value as second most important criterion. The resulting score demonstrated a strong diagnostic performance with an AUC of 0.840, providing both rule-in and rule-out criteria. In an independent test set of 65 lesions the diagnostic performance was verified by two readers (AUC 0.77 and 0.87, kappa: 0.62). CONCLUSIONS We developed a clinical decision rule for unenhanced breast MRI including lesion margins and ADC value as the most important criteria, achieving high diagnostic accuracy.
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Affiliation(s)
- N Pötsch
- 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
| | - P Clauser
- 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
| | - P Kapetas
- 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
| | - M Baykara Ulusan
- Department of Radiology, University of Health Sciences Istanbul Training and Research Hospital, Org. Abdurrahman Nafiz Gurman Cad, No:1 Fatih, İstanbul, Turkey
| | - T Helbich
- 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
| | - P 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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Guo Y, Zhang H, Yuan L, Chen W, Zhao H, Yu QQ, Shi W. Machine learning and new insights for breast cancer diagnosis. J Int Med Res 2024; 52:3000605241237867. [PMID: 38663911 PMCID: PMC11047257 DOI: 10.1177/03000605241237867] [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: 08/21/2023] [Accepted: 02/21/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.
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Affiliation(s)
- Ya Guo
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Heng Zhang
- Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China
| | - Leilei Yuan
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Weidong Chen
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Haibo Zhao
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Qing-Qing Yu
- Phase I Clinical Research Centre, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Wenjie Shi
- Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular- and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany
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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.
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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.
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Bäuerle T, Dietzel M, Pinker K, Bonekamp D, Zhang KS, Schlemmer HP, Bannas P, Cyran CC, Eisenblätter M, Hilger I, Jung C, Schick F, Wegner F, Kiessling F. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. ROFO-FORTSCHR RONTG 2024; 196:354-362. [PMID: 37944934 DOI: 10.1055/a-2175-4446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). METHOD This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. RESULTS AND CONCLUSION Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. KEY POINTS · Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas.. CITATION FORMAT · Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.
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Affiliation(s)
- Tobias Bäuerle
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Matthias Dietzel
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Kevin S Zhang
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Bannas
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Clemens C Cyran
- Institute of Radiology, University Medical Center München (LMU), München, Germany
| | - Michel Eisenblätter
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum OWL, Universität Bielefeld Campus Klinikum Lippe, 32756 Detmold, Germany
| | - Ingrid Hilger
- Experimental Radiology, University Medical Center Jena, Germany
| | - Caroline Jung
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Schick
- Experimental Radiology, University Medical Center Tübingen, Germany
| | - Franz Wegner
- Department of Radiology, University Hospital Schleswig-Holstein Campus Lübeck, Germany
| | - Fabian Kiessling
- Experimental Molecular Imaging, University Medical Center Aachen, Germany
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van Nijnatten TJA, Morscheid S, Baltzer PAT, Clauser P, Alcantara R, Kuhl CK, Wildberger JE. Contrast-enhanced breast imaging: Current status and future challenges. Eur J Radiol 2024; 171:111312. [PMID: 38237520 DOI: 10.1016/j.ejrad.2024.111312] [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: 12/21/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Contrast-enhanced breast MRI and recently also contrast-enhanced mammography (CEM) are available for breast imaging. The aim of the current overview is to explore existing evidence and ongoing challenges of contrast-enhanced breast imaging. METHODS This narrative provides an introduction to the contrast-enhanced breast imaging modalities breast MRI and CEM. Underlying principle, techniques and BI-RADS reporting of both techniques are described and compared, and the following indications and ongoing challenges are discussed: problem-solving, high-risk screening, supplemental screening in women with extremely dense breast tissue, breast implants, neoadjuvant systemic therapy (NST) response monitoring, MRI-guided and CEM- guided biopsy. RESULTS Technique and reporting for breast MRI are standardised, for the newer CEM standardisation is in progress. Similarly, compared to other modalities, breast MRI is well established as superior for problem-solving, screening women at high risk, screening women with extremely dense breast tissue or with implants; and for monitoring response to NST. Furthermore, MRI-guided biopsy is a reliable technique with low long-term false negative rates. For CEM, data is as yet either absent or limited, but existing results in these settings are promising. CONCLUSION Contrast-enhanced breast imaging achieves highest diagnostic performance and should be considered essential. Of the two contrast-enhanced modalities, evidence of breast MRI superiority is ample, and preliminary results on CEM are promising, yet CEM warrants further study.
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Affiliation(s)
- T J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands; GROW - School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
| | - S Morscheid
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - P A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - P Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - R Alcantara
- Radiology and Nuclear Medicine Department, Hospital del Mar, Barcelona, Spain
| | - C K Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - J E Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+, Maastricht, the Netherlands
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Aslan O, Oktay A. Diagnostic accuracy of the breast MRI Kaiser score in suspected architectural distortions and its comparison with mammography. Sci Rep 2024; 14:447. [PMID: 38172557 PMCID: PMC10764901 DOI: 10.1038/s41598-023-50798-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
Suspicious architectural distortion is an elusive finding in breast cancer diagnosis. This study aimed to evaluate the diagnostic accuracy of the Kaiser score for suspicious architectural distortions observed on mammography and compare it with the BI-RADS score of the lesion. Mammograms performed between January 2013 and March 2023 were retrospectively analyzed for the presence of suspicious architectural distortion. Forty-one patients, who had at least 1 year of radiological follow-up or pathology results, and underwent breast MRI, were included in the study. Mammography findings and the BI-RADS category of the lesion were assessed. MRI findings were evaluated and Kaiser scoring was performed according to the tree flowchart. Ninety-one percent of the enhanced lesions had a Kaiser score of 5 and above. In the diagnosis of malignancy, the Kaiser score yielded an accuracy of 75.61% (AUC 0.833). A statistically significant correlation was observed indicating that a malignant diagnosis was more prevalent in patients with a Kaiser score of 5 and above (p < 0.05). Additionally statistically significant relationship was also observed between the BI-RADS category of architectural distortions on mammography and the Kaiser score (p = 0.007). The combined utilization of mammography findings and the evidence-based Kaiser score in suspected architectural distortions provides more accurate results in the differential diagnosis of breast cancer.
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Affiliation(s)
- Ozge Aslan
- Department of Radiology, Ege University Faculty of Medicine, 35100, Bornova, Izmir State, Turkey.
| | - Aysenur Oktay
- Department of Radiology, Ege University Faculty of Medicine, 35100, Bornova, Izmir State, Turkey
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Klein K, Gabriel Schafigh D, Schömig-Markiefka B, Campbell GM, Weiss K, Malter W, Maintz D, Hellmich M, Barbara Krug K. Intermodal correlation of quantitative CT-data and MRI-biomarkers derived from synchronous spectral CT-maps and breast MRI-examinations with molecular biomarkers in invasive ductal breast carcinomas. Eur J Radiol 2023; 165:110919. [PMID: 37302338 DOI: 10.1016/j.ejrad.2023.110919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/19/2023] [Accepted: 05/31/2023] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To asses the correlation of data derived from dual-layer (DL)-CT material-maps and breast MRI data with molecular biomarkers in invasive breast carcinomas. METHODS All patients at the University Breast Cancer Center who underwent a clinically indicated DLCT-scan and a breast MRI for staging of invasive ductal breast cancer from 2016 to 2020 were prospectively included. Iodine concentration-maps, and Zeffective-maps were reconstructed from the CT-datasets. T1w- and T2w-signal intensities, ADC and the clustered shapes of the dynamic-curves (washout, plateau, persistent) were derived from the MRI-datasets. ROI-based evaluations of the cancers and the reference "musculature" were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Statistical analysis was essentially descriptive using Spearmańs rank correlation and (multivariable) partial correlation. RESULTS The signal intensities measured in the 3rd phase of the contrast dynamics correlated at an intermediate level of significance with the iodine content and the Zeffective-values derived from the breast target lesions (Spearmańs rank correlation-coefficient r = 0.237/0.236, p = 0.002/0.003). The bivariate and the multivariate analyses displayed correlations of an intermediate significance level of the iodine content and the Zeff-values measured in the breast target lesions with immunhistochemical subtyping (r = 0.211-0.243, p = 0.002-0.009, respectively). The Zeff-values showed the strongest correlations when normalized to the values measured in the musculature and in the aorta (r = -0.237 to -0.305, r=<0.001-0.003). The MRI-assessments showed correlations of intermediate to high significance and low to intermediate significance between the ratios of the T2w-signal intensities and the trends of the dynamic curves measured in the breast target lesions and in musculature and immunohistochemical cancer subtyping, respectively (T2w: r = 0.232-0.249, p = 0.003/0.002; dynamics: r = -0.322/-0.245, p=<0.001/0.002). The ratios of the clustered trends of the dynamic curves measured in the breast target lesions and in musculature correlated with tumor grading on intermediate significance level (r = -0.213 and -0.194, p = 0.007/0.016) and with Ki-67 on a low significance level (bivariate analysis: r = -0.160, p = 0.040). There was only a weak correlation between the ADC-values measured in the breast target lesions and HER2-expression (bivariate ansalysis: r = 0.191, p = 0.030). CONCLUSIONS Our preliminary results indicate that evaluation of perfusion based DLCT-data and MRI-biomarkers show correlations with the immunhistochemical subtyping of invasive ductal breast carcinomas. Further clinical research is warranted in order to validate the value of the results and define clinical situations in which the use of the described DLCT-biomaker and MRI biomarkers may be helpful in clinical patient care.
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Affiliation(s)
- Konstantin Klein
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Darius Gabriel Schafigh
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany; Dept. of ENT Surgery, University Hospital of Cologne, Cologne, Germany
| | | | | | | | - Wolfram Malter
- Breast Cancer Center, Department of Gynecology and Obstetrics, University of Cologne, Cologne, Germany
| | - David Maintz
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Martin Hellmich
- Institute of Medical Statistics and Bioinformatics, Medical Faculty, University of Cologne, Germany
| | - Kathrin Barbara Krug
- Dept. of Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany.
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Klarić K, Šribar A, Budisavljević A, Labinac L, Valković Zujić P. Evaluation of Contrast-Enhanced Mammography and Development of Flowchart for BI-RADS Classification of Breast Lesions. Diagnostics (Basel) 2023; 13:diagnostics13111958. [PMID: 37296810 DOI: 10.3390/diagnostics13111958] [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: 04/28/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
This study aimed to evaluate contrast-enhanced mammography (CEM) and to compare breast lesions on CEM and breast magnetic resonance imaging (MRI) using 5 features. We propose a flowchart for BI-RADS classification of breast lesions on CEM based on the Kaiser score (KS) flowchart for breast MRI. Sixty-eight subjects (women and men; median age 61.4 ± 11.6 years) who were suspected of having a malignant process in the breast based on digital mammography (MG) findings were included in the study. The patients underwent breast ultrasound (US), CEM, MRI and biopsy of the suspicious lesion. There were 47 patients with malignant lesions confirmed by biopsy and 21 patients with benign lesions, for each of which a KS was calculated. In the patients with malignant lesions, the MRI-derived KS was 9 (IQR 8-9); its CEM equivalent was 9 (IQR 8-9); and BI-RADS was 5 (IQR 4-5). In patients with benign lesions, MRI-derived KS was 3 (IQR 2-3); its CEM equivalent was 3 (IQR 1.7-5); and BI-RADS was 3 (IQR 0-4). There was no significant difference between the ROC-AUC of CEM and MRI (p = 0.749). In conclusion, there were no significant differences in KS results between CEM and breast MRI. The KS flowchart is useful for evaluating breast lesions on CEM.
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Affiliation(s)
- Kristina Klarić
- Department of Radiology, Pula General Hospital, 52100 Pula, Croatia
| | - Andrej Šribar
- Clinic for Anesthesiology, Resuscitation and Intensive Care Medicine, Dubrava Clinical Hospital, 10000 Zagreb, Croatia
- School of Dental Medicine, Zagreb University, 10000 Zagreb, Croatia
| | | | - Loredana Labinac
- Department of Pathology and Cytology, Pula General Hospital, 52100 Pula, Croatia
| | - Petra Valković Zujić
- Department of Radiology, Clinical Hospital Center Rijeka, 51000 Rijeka, Croatia
- Faculty of Medicine, University of Rijeka, Brace Branchetta 20, 51000 Rijeka, Croatia
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Avdan Aslan A, Gültekin S. Diagnostic performance of Kaiser score in patients with newly diagnosed breast cancer: Factors associated with false-negative results. Eur J Radiol 2023; 164:110864. [PMID: 37209464 DOI: 10.1016/j.ejrad.2023.110864] [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: 01/29/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE To investigate the factors associated with false-negative results in the diagnosis of breast cancer via breast magnetic resonance imaging (MRI) using the Kaiser score (KS). METHODS This institutional review board (IRB)-approved, single-center, retrospective study enrolled 219 consecutive histopathologically proven breast cancer lesions in 205 women who underwent preoperative breast MRI. Two breast radiologists evaluated each lesion according to the KS. The clinicopathological characteristics and imaging findings were also analyzed. Interobserver variability was assessed using the intraclass correlation coefficient (ICC). Multivariate regression analysis was used to investigate factors associated with false-negative KS results for breast cancer diagnosis. RESULTS Of 219 breast cancers, KS yielded 200 (91.3%) true-positive and 19 (8.7%) false-negative results. The interobserver ICC for the KS between the two readers was good, with a value of 0.804 (95% CI 0.751-0.846). Multivariate regression analysis revealed that small lesion size (≤1 cm) (adjusted OR 6.86, 95% CI 2.14-21.94, p = 0.001) and personal breast cancer history (adjusted OR 7.59, 95% CI, 1.55-37.23, p = 0.012) were significantly associated with false-negative KS results. CONCLUSION Small lesion size (≤1 cm) and presence of personal breast cancer history are factors significantly associated with false-negative KS results. Our results suggest that radiologists should consider these factors in clinical practice as potential pitfalls of KS, which may be compensated for by a multimodal approach combined with clinical evaluation.
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Affiliation(s)
- Aydan Avdan Aslan
- Department of Radiology, Faculty of Medicine, Gazi University, Ankara, Turkey.
| | - Serap Gültekin
- Department of Radiology, Faculty of Medicine, Gazi University, Ankara, Turkey
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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: 7] [Impact Index Per Article: 3.5] [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.
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