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Varga R, Fueger BJ, Ferrara F, Kapetas P, Pötsch N, Helbich TH, Clauser P, Baltzer PAT. Evaluation of apparent diffuse coefficient (ADC) with regards to reproducibility and diagnostic accuracy as well as possible significance of pre - and post - contrast acquisition and employment of different b values. Eur J Radiol 2024; 181:111730. [PMID: 39303393 DOI: 10.1016/j.ejrad.2024.111730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/23/2024] [Accepted: 09/05/2024] [Indexed: 09/22/2024]
Abstract
PURPOSE Ongoing efforts are focusing on optimizing diffusion-weighted imaging (DWI) as an essential part of breast MRI protocol. Our study aimed to evaluate the effect of contrast media (CM) on the apparent diffusion coefficients (ADC) acquired following current recommendations. PATIENT AND METHODS Patients who underwent 3 T breast MRI with a histologically verified suspicious lesion were included in this IRB-approved, single-center, cross-sectional retrospective study. Breast MRI protocol included a DWI sequence with multiple b-values, which was acquired before and after CM administration. ADC maps were calculated by in-line monoexponential fitting with b-values 0 /800 and 50/800. Two independent readers (R1, R2) reviewed the images in separate sessions for b values 0/800 and 50/800, pre- and post-CM. Bland Altmann plots as well as intraclass correlation coefficients (ICCs) for inter-reader agreement, different b-values, and pre- and post-CM were calculated. Diagnostic accuracy was evaluated and compared by calculating the area under the receiver operating characteristics curve (AUC). RESULTS 91 lesions in 89 patients were examined (mean age 50.7 years, standard deviation 13.9). ADC values were significantly (P<0.05) lower post-CM (mean ranging from 1.28 x10-3 mm2/s to 1.30 x10-3 mm2/s) compared to pre-CM (mean ranging from 1.32 x10-3 mm2/s to 1.37 x10-3 mm2/s) for both b-values combinations (0/800 and 50/800 s/mm2). We found an almost perfect inter-reader agreement pre-/post-CM with b values 0/800 and 50/800 (ICC ranging from 0.853 to 0.939). Bland Altman plot demonstrated no systematic difference between readers. ROC analysis revealed good diagnostic performance without significant differences (P>0.05) between b values 0/800 and 50/800 s/mm2 as well as pre- and post-CM with areas under the ROC curve between 0.834 and 0.877. CONCLUSION ADC values are slightly lower when acquiring b values 0/800 and post-CM. This effect does not reduce the diagnostic performance but may be relevant in case of definite cut-offs in medical decision making.
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Affiliation(s)
- Raoul Varga
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Barbara J Fueger
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Francesca Ferrara
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Nina Pötsch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
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Wenkel E, Wunderlich P, Fallenberg EM, Platz Batista da Silva N, Preibsch H, Sauer S, Siegmann-Luz K, Weigel S, Wessling D, Wilpert C, Baltzer PAT. Recommendations of the German Radiological Society's breast imaging working group regarding breast MRI. ROFO-FORTSCHR RONTG 2024; 196:939-944. [PMID: 38237631 DOI: 10.1055/a-2216-0782] [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: 08/17/2024]
Abstract
· Breast MRI is an essential part of breast imaging. · The recommendations for performing breast MRI have been updated. · A table provides a compact and quick overview. More detailed comments supplement the table.. · The "classic" breast MRI can be performed based on the recommendations. Tips for special clinical questions, such as implant rupture, mammary duct pathology or local lymph node status, are included.. CITATION FORMAT: · Wenkel E, Wunderlich P, Fallenberg E et al. Aktualisierung der Empfehlungen der AG Mammadiagnostik der Deutschen Röntgengesellschaft zur Durchführung der Mamma-MRT. Fortschr Röntgenstr 2024; 196: 939 - 944.
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Affiliation(s)
- Evelyn Wenkel
- Radiology, Radiology München, München, Germany
- Medical Faculty, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Petra Wunderlich
- Radiologische Gemeinschaftspraxis Radebeul, Elblandklinikum Radebeul, Germany
| | | | | | - Heike Preibsch
- Diagnostic and Interventional Radiology, University Hospital Tübingen, Germany
| | - Stephanie Sauer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany
| | - Katja Siegmann-Luz
- Referenzzentrum Mammographie Berlin, German Breast Cancer Screening Program, Berlin, Germany
| | - Stefanie Weigel
- Clinic for Radiology and Reference Center for Mammography, University of Münster Faculty of Medicine, Münster, Germany
| | - Daniel Wessling
- Diagnostic and Interventional Radiology, University Hospital Tübingen, Germany
| | - Caroline Wilpert
- Diagnostic and Interventional Radiology, University Hospital Freiburg Department of Radiology, Freiburg, Germany
| | - Pascal Andreas Thomas Baltzer
- Unit of General Radiology and Paediatric Radiology, Medical University of Vienna Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria
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Zha H, Wu T, Zhang M, Cai M, Diao X, Li F, Wu R, Du Y. Combining Potential Strain Elastography and Radiomics for Diagnosing Breast Lesions in BI-RADS 4: Construction and Validation a Predictive Nomogram. Acad Radiol 2024; 31:3106-3116. [PMID: 38378324 DOI: 10.1016/j.acra.2024.01.038] [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: 12/22/2023] [Revised: 01/21/2024] [Accepted: 01/27/2024] [Indexed: 02/22/2024]
Abstract
RATIONALE AND OBJECTIVES To develop a nomogram by integrating B-mode ultrasound (US), strain ratio (SR), and radiomics signature (RS) effectively differentiating between benign and malignant lesions in the Breast Imaging Reporting and Data System (BI-RADS) 4. MATERIALS AND METHODS We retrospectively recruited 709 consecutive patients who were assigned a BI-RADS 4 and underwent curative resection or biopsy between 2017 and 2022. US images were collected before surgery. A RS was developed through a multistep feature selection and construction process. Histology findings served as the gold standard. Univariate and multivariate regression analysis were employed to analyze the clinical and US characteristics and identify variables for developing a nomogram. The calibration and discrimination of the nomogram were conducted to evaluate its performance. RESULTS The study included a total of 709 patients, with 497 in the training set and 212 in the validation set. In the training set, the B-mode US had an AUC of 0.84 (95% confidence interval [CI], 0.80, 0.87). The SR demonstrated an AUC of 0.78 (95% CI, 0.74, 0.82), while the RS showed an AUC of 0.85 (95% CI, 0.81, 0.88). Notably, the nomogram exhibited superior performance compared to the conventional US, SR, and RS (AUC=0.93, both p < 0.05, as per the Delong test). The clinical usefulness of the nomogram was favorable. CONCLUSION The calibrated nomogram can be specifically designed to predict the malignancy of breast lesions in the BI-RADS 4 category.
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Affiliation(s)
- Hailing Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tingting Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Manqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mengjun Cai
- Department of Ultrasound, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xuehong Diao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Zhou J, Liu H, Miao H, Ye S, He Y, Zhao Y, Chen Z, Zhang Y, Liu YL, Pan Z, Su MY, Wang M. Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. Eur Radiol 2024:10.1007/s00330-024-10922-1. [PMID: 38990324 DOI: 10.1007/s00330-024-10922-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 03/23/2024] [Accepted: 05/28/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions. METHODS A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed. RESULTS When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity. CONCLUSION The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved. CLINICAL RELEVANCE STATEMENT KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity. KEY POINTS KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.
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Affiliation(s)
- Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Huiru Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiwei Miao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuxin Ye
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yun He
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Youfan Zhao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA, US
| | - Zhifang Pan
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, US.
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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Iima M, Kataoka M, Honda M, Le Bihan D. Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer. Korean J Radiol 2024; 25:623-633. [PMID: 38942456 PMCID: PMC11214919 DOI: 10.3348/kjr.2023.1188] [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: 03/02/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/30/2024] Open
Abstract
This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.
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Affiliation(s)
- Mami Iima
- Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
| | - Denis Le Bihan
- NeuroSpin, Joliot Institute, Department of Fundamental Research, Commissariat à l'Energie Atomique (CEA)-Saclay, Gif-sur-Yvette, France
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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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Grażyńska A, Niewiadomska A, Owczarek AJ, Winder M, Hołda J, Zwolińska O, Barczyk-Gutkowska A, Modlińska S, Lorek A, Kuźbińska A, Steinhof-Radwańska K. Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population. Pol J Radiol 2024; 89:e240-e248. [PMID: 38938658 PMCID: PMC11210381 DOI: 10.5114/pjr/186180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/17/2024] [Indexed: 06/29/2024] Open
Abstract
Purpose To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population. Material and methods 792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories. Results Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%. Conclusions CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.
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Affiliation(s)
- Anna Grażyńska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Agnieszka Niewiadomska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Aleksander J. Owczarek
- Health Promotion and Obesity Management Unit, Department of Pathophysiology, Medical University of Silesia, Katowice, Poland
| | - Mateusz Winder
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Jakub Hołda
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
- Department of Anatomy, Jagiellonian University Medical College, Cracow, Poland
| | - Olga Zwolińska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Anna Barczyk-Gutkowska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Sandra Modlińska
- Department of Radiology and Nuclear Medicine, Medical University of Silesia, Katowice, Poland
| | - Andrzej Lorek
- Department of Oncological Surgery, Prof. Kornel Gibiński Independent Public Central Clinical Hospital, Katowice, Poland
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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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Kim JH, Kim SY, Cui C, Ji H, Yoen H, Cho N, Kim DH. Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI. J Magn Reson Imaging 2024; 59:1218-1228. [PMID: 37477575 DOI: 10.1002/jmri.28884] [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: 04/10/2023] [Revised: 06/21/2023] [Accepted: 06/21/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND While breast ultrasound (US) is a useful tool for diagnosing breast masses, it can entail false-positive biopsy results because of some overlapping features between benign and malignant breast masses and subjective interpretation. PURPOSE To evaluate the performance of conductivity imaging for reducing false-positive biopsy results related to breast US, as compared to diffusion-weighted imaging (DWI) and abbreviated MRI consisting of one pre- and one post-contrast T1-weighted imaging. STUDY TYPE Prospective. SUBJECTS Seventy-nine women (median age, 44 years) with 86 Breast Imaging Reporting and Data System (BI-RADS) category 4 masses as detected by breast US. FIELD STRENGTH/SEQUENCE 3-T, T2-weighted turbo spin echo sequence, DWI, and abbreviated contrast-enhanced MRI (T1-weighted gradient echo sequence). ASSESSMENT US-guided biopsy (reference standard) was obtained on the same day as MRI. The maximum and mean conductivity parameters from whole and single regions of interest (ROIs) were measured. Apparent diffusion coefficient (ADC) values were obtained from an area with the lowest signal within a lesion on the ADC map. The performance of conductivity, ADC, and abbreviated MRI for reducing false-positive biopsies was evaluated using the following criteria: lowest conductivity and highest ADC values among malignant breast lesions and BI-RADS categories 2 or 3 on abbreviated MRI. STATISTICAL TESTS One conductivity parameter with the maximum area under the curve (AUC) from receiver operating characteristics was selected. A P-value <0.05 was considered statistically significant. RESULTS US-guided biopsy revealed 65 benign lesions and 21 malignant lesions. The mean conductivity parameter of the single ROI method was selected (AUC = 0.74). Considering conductivity (≤0.10 S/m), ADC (≥1.60 × 10-3 mm2 /sec), and BI-RADS categories 2 or 3 reduced false-positive biopsies by 23% (15 of 65), 38% (25 of 65), and 43% (28 of 65), respectively, without missing malignant lesions. DATA CONCLUSION Conductivity imaging may show lower performance than DWI and abbreviated MRI in reducing unnecessary biopsies. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jun-Hyeong Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Chuanjiang Cui
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hye Ji
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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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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11
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Youn I, Biswas D, Hippe DS, Winter AM, Kazerouni AS, Javid SH, Lee JM, Rahbar H, Partridge SC. Diagnostic Performance of Point-of-Care Apparent Diffusion Coefficient Measures to Reduce Biopsy in Breast Lesions at MRI: Clinical Validation. Radiology 2024; 310:e232313. [PMID: 38349238 PMCID: PMC10902596 DOI: 10.1148/radiol.232313] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 02/15/2024]
Abstract
Background The Eastern Cooperative Oncology Group-American College of Radiology Imaging Network Cancer Research Group multicenter A6702 trial identified an optimal apparent diffusion coefficient (ADC) cutoff to potentially reduce biopsies by 21% without affecting sensitivity. Whether this performance can be achieved in clinical settings has not yet been established. Purpose To validate the performance of point-of-care ADC measurements with the A6702 trial ADC cutoff for reducing unnecessary biopsies in lesions detected at breast MRI. Materials and Methods Consecutive breast MRI examinations performed from May 2015 to January 2019 at a single medical center and showing biopsy-confirmed Breast Imaging Reporting and Data System category 4 or 5 lesions, without ipsilateral cancer, were identified. Point-of-care lesion ADC measurements collected at clinical interpretation were retrospectively evaluated. MRI examinations included axial T2-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences. Sensitivity and biopsy reduction rates were calculated by applying the A6702 optimal (ADC, 1.53 × 10-3 mm2/sec) and alternate conservative (1.68 × 10-3 mm2/sec) cutoffs. Lesion pathologic outcomes were the reference standard. To assess reproducibility, one radiologist repeated ADC measurements, and agreement was summarized using the intraclass correlation coefficient. Results A total of 240 lesions in 201 women (mean age, 49 years ± 13 [SD]) with pathologic outcomes (63 malignant and 177 benign) were included. Applying the optimal ADC cutoff produced an overall biopsy reduction rate of 15.8% (38 of 240 lesions [95% CI: 11.2, 20.9]), with a sensitivity of 92.1% (58 of 63 lesions [95% CI: 82.4, 97.4]; sensitivity was 97.2% [35 of 36 lesions] [95% CI: 82.7, 99.6] for invasive cancers). Results were similar for screening versus diagnostic examinations (P = .92 and .40, respectively). Sensitivity was higher for masses than for nonmass enhancements (NMEs) (100% vs 85.3%; P = .009). Applying the conservative ADC cutoff achieved a sensitivity of 95.2% (60 of 63 lesions [95% CI: 86.7, 99.0]), with a biopsy reduction rate of 10.4% (25 of 240 lesions [95% CI: 6.7, 14.5]). Repeated single-reader measurements showed good agreement with clinical ADCs (intraclass correlation coefficient, 0.72 [95% CI: 0.58, 0.81]). Conclusion This study validated the clinical use of ADC cutoffs to reduce MRI-prompted biopsies by up to 16%, with a suggested tradeoff of lowered sensitivity for in situ and microinvasive disease manifesting as NME. Clinical trial registration no. NCT02022579 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Honda and Iima in this issue.
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Affiliation(s)
| | - Debosmita Biswas
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Daniel S. Hippe
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Andrea M. Winter
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Anum S. Kazerouni
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Sara H. Javid
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Janie M. Lee
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Habib Rahbar
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
| | - Savannah C. Partridge
- From the Departments of Radiology (I.Y., D.B., A.M.W., A.S.K.,
J.M.L., H.R., S.C.P.) and Surgery (S.H.J.), University of Washington School of
Medicine, 1144 Eastlake Ave E, LG2-200, Seattle, WA 98109; and Clinical Research
Division, Fred Hutchinson Cancer Center (D.S.H.)
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Honda M, Iima M. It Is Time to Use Apparent Diffusion Coefficient in Breast MRI Diagnostics. Radiology 2024; 310:e240125. [PMID: 38349245 DOI: 10.1148/radiol.240125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Affiliation(s)
- Maya Honda
- From the Department of Diagnostic Radiology, Kansai Electric Power Hospital, 2-1-7 Fukushima, Fukushima-ku, Osaka 553-0003, Japan (M.H.); Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.H., M.I.); and Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan (M.I.)
| | - Mami Iima
- From the Department of Diagnostic Radiology, Kansai Electric Power Hospital, 2-1-7 Fukushima, Fukushima-ku, Osaka 553-0003, Japan (M.H.); Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.H., M.I.); and Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan (M.I.)
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Gullo RL, Partridge SC, Shin HJ, Thakur SB, Pinker K. Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring. AJR Am J Roentgenol 2024; 222:e2329933. [PMID: 37850579 PMCID: PMC11196747 DOI: 10.2214/ajr.23.29933] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA, USA 98109, USA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Tollens F, Baltzer PA, Froelich MF, Kaiser CG. Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses. Front Oncol 2023; 13:1292268. [PMID: 38130995 PMCID: PMC10733447 DOI: 10.3389/fonc.2023.1292268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening. Purpose To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening. Materials and methods The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed. Results Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes. Conclusion Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.
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Affiliation(s)
- Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Pascal A.T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Clemens G. Kaiser
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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15
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van der Hoogt KJJ, Schipper RJ, Wessels R, Ter Beek LC, Beets-Tan RGH, Mann RM. Breast DWI Analyzed Before and After Gadolinium Contrast Administration-An Intrapatient Analysis on 1.5 T and 3.0 T. Invest Radiol 2023; 58:832-841. [PMID: 37389456 DOI: 10.1097/rli.0000000000000999] [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: 07/01/2023]
Abstract
OBJECTIVES Diffusion-weighted magnetic resonance imaging (MRI) is gaining popularity as an addition to standard dynamic contrast-enhanced breast MRI. Although adding diffusion-weighted imaging (DWI) to the standard protocol design would require increased scanning-time, implementation during the contrast-enhanced phase could offer a multiparametric MRI protocol without any additional scanning time. However, gadolinium within a region of interest (ROI) might affect assessments of DWI. This study aims to determine if acquiring DWI postcontrast, incorporated in an abbreviated MRI protocol, would statistically significantly affect lesion classification. In addition, the effect of postcontrast DWI on breast parenchyma was studied. MATERIALS AND METHODS Screening or preoperative MRIs (1.5 T/3 T) were included for this study. Diffusion-weighted imaging was acquired with single-shot spin echo-echo planar imaging before and at approximately 2 minutes after gadoterate meglumine injection. Apparent diffusion coefficients (ADCs) based on 2-dimensional ROIs of fibroglandular tissue, as well as benign and malignant lesions at 1.5 T/3.0 T, were compared with a Wilcoxon signed rank test. Diffusivity levels were compared between precontrast and postcontrast DWI with weighted κ. An overall P ≤ 0.05 was considered statistically significant. RESULTS No significant changes were observed in ADC mean after contrast administration in 21 patients with 37 ROI of healthy fibroglandular tissue and in the 93 patients with 93 (malignant and benign) lesions. This effect remained after stratification on B 0 . In 18% of all lesions, a diffusion level shift was observed, with an overall weighted κ of 0.75. CONCLUSIONS This study supports incorporating DWI at 2 minutes postcontrast when ADC is calculated based on b150-b800 with 15 mL 0.5 M gadoterate meglumine in an abbreviated multiparametric MRI protocol without requiring extra scan time.
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Affiliation(s)
- Kay J J van der Hoogt
- From the Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands (K.J.J.H., R.-J.S., R.W., R.G.H.B., R.M.M.); GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (K.J.J.H., R.G.H.B.); Department of Surgery, Catharina Hospital Eindhoven, Eindhoven, the Netherlands (R.-J.S.); Department of Medical Physics, the Netherlands Cancer Institute, Amsterdam, the Netherlands (L.C.B.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (R.M.M.); and Danish Colorectal Cancer Unit South, Vejle University Hospital, Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark (R.G.H.B.)
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Pesapane F, Rotili A, Dominelli V, Cassano E. The potential role of contrast-enhanced mammography in downgrading BI-RADS 4 lesions: A balanced perspective. Eur J Radiol 2023; 168:111111. [PMID: 37788518 DOI: 10.1016/j.ejrad.2023.111111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/03/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023]
Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy(1).
| | - Anna Rotili
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy(1).
| | - Valeria Dominelli
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy(1).
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy(1).
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17
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Li X, Fan Z, Jiang H, Niu J, Bian W, Wang C, Wang Y, Zhang R, Zhang H. Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status. Sci Rep 2023; 13:17978. [PMID: 37864025 PMCID: PMC10589282 DOI: 10.1038/s41598-023-45079-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.
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Affiliation(s)
- Xiaojun Li
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Radiology, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China
| | - Zhichang Fan
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hongnan Jiang
- Department of Breast Surgery, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China
| | - Jinliang Niu
- Department of Radiology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Wenjin Bian
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Chen Wang
- Department of Pathology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ying Wang
- Department of Pathology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Runmei Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, No. 85, South Jiefang Road, Yingze District, Taiyuan, 030001, Shanxi, China.
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Reig B, Kim E, Chhor CM, Moy L, Lewin AA, Heacock L. Problem-solving Breast MRI. Radiographics 2023; 43:e230026. [PMID: 37733618 DOI: 10.1148/rg.230026] [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: 09/23/2023]
Abstract
Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Beatriu Reig
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Eric Kim
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Chloe M Chhor
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Linda Moy
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Alana A Lewin
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Laura Heacock
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
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19
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Huang Z, Chen X, Jiang N, Hu S, Hu C. A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study. BMC Med Imaging 2023; 23:118. [PMID: 37679713 PMCID: PMC10483851 DOI: 10.1186/s12880-023-01092-5] [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: 01/02/2023] [Accepted: 08/30/2023] [Indexed: 09/09/2023] Open
Abstract
PURPOSE To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). METHODS Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. RESULTS The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59-0.94), 0.77 (95%CI 0.59-0.95), 0.74 (95%CI 0.55-0.93), 0.87 (95%CI 0.73-1), 0.8 (95%CI 0.63-0.96), and 0.93 (95%CI 0.84-1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P < 0.05) set and showed the greatest overall net benefit for upstaging according to decision curve analysis (DCA). CONCLUSION A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS preoperatively.
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Affiliation(s)
- Zhou Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Xue Chen
- Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou City, Jiangsu Province, 215002, PR China
| | - Nan Jiang
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Su Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China.
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Saccenti L, Mellon CDM, Scholer M, Jolibois Z, Stemmer A, Weiland E, de Bazelaire C. Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI. Diagn Interv Imaging 2023; 104:410-418. [PMID: 37208291 DOI: 10.1016/j.diii.2023.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/21/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions. MATERIALS AND METHODS This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis. RESULTS A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001). CONCLUSION Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.
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Affiliation(s)
- Laetitia Saccenti
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France.
| | - Constance de Margerie Mellon
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - Margaux Scholer
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Zoe Jolibois
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Alto Stemmer
- Siemens Healthineers GMBH, 91052 Erlanger, Germany
| | | | - Cedric de Bazelaire
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
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Li Y, Yang Z, Lv W, Qin Y, Tang C, Yan X, Yin T, Ai T, Xia L. Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence. Quant Imaging Med Surg 2023; 13:5974-5985. [PMID: 37711822 PMCID: PMC10498242 DOI: 10.21037/qims-22-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 07/13/2023] [Indexed: 09/16/2023]
Abstract
Background In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma. Methods This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence. Results The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440). Conclusions The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.
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Affiliation(s)
- Yan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenlu Yang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, China
| | - Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yan
- Scientific Marketing, Siemens Healthcare Ltd., Shanghai, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Shanghai, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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22
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Meng L, Zhao X, Guo J, Lu L, Cheng M, Xing Q, Shang H, Zhang B, Chen Y, Zhang P, Zhang X. Improved Differential Diagnosis Based on BI-RADS Descriptors and Apparent Diffusion Coefficient for Breast Lesions: A Multiparametric MRI Analysis as Compared to Kaiser Score. Acad Radiol 2023; 30 Suppl 2:S93-S103. [PMID: 37236897 DOI: 10.1016/j.acra.2023.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 05/28/2023]
Abstract
RATIONALE AND OBJECTIVES To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions. MATERIALS AND METHODS A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 × 10-3 mm2/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models' discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared. RESULTS In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929-0.976 and AUC, 0.958; 95% CI: 0.931-0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885-0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS. CONCLUSION The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.
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Affiliation(s)
- Lingsong Meng
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.); Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China (L.M., P.Z.).
| | - Xin Zhao
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Jinxia Guo
- General Electric (GE) Healthcare, Beijing, China (J.G.).
| | - Lin Lu
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Meiying Cheng
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Qingna Xing
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Honglei Shang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Bohao Zhang
- Henan Key Laboratory of Child Brain Injury, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (B.Z.).
| | - Yan Chen
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
| | - Penghua Zhang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.); Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China (L.M., P.Z.).
| | - Xiaoan Zhang
- Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (L.M., X.Z., L.L., M.C., Q.X., H.S., Y.C., P.Z., X.Z.).
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Li Y, Chen J, Yang Z, Fan C, Qin Y, Tang C, Yin T, Ai T, Xia L. Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment. J Magn Reson Imaging 2023; 58:963-974. [PMID: 36738118 DOI: 10.1002/jmri.28600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable. PURPOSE To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images. STUDY TYPE Retrospective. SUBJECTS A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results. FIELD STRENGTH/SEQUENCE A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence. ASSESSMENT In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort. STATISTICAL TESTS Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant. RESULTS For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635. DATA CONCLUSION Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Zhenlu Yang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Chanyuan Fan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Shanghai, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [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: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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25
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Bickel H, Clauser P, Pinker K, Helbich T, Biondic I, Brkljacic B, Dietzel M, Ivanac G, Krug B, Moschetta M, Neuhaus V, Preidler K, Baltzer P. Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database. Eur Radiol 2023; 33:5400-5410. [PMID: 37166495 PMCID: PMC10326122 DOI: 10.1007/s00330-023-09675-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVES To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. METHODS This was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. RESULTS A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached. CONCLUSIONS The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. CLINICAL RELEVANCE STATEMENT The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. KEY POINTS • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.
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Affiliation(s)
- Hubert Bickel
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Paola Clauser
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker
- Evelyn H. Lauder Breast Center, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Thomas Helbich
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Iva Biondic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Boris Brkljacic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Matthias Dietzel
- Dpt. of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Gordana Ivanac
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Barbara Krug
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Marco Moschetta
- Dpt. of Emergency and Organ Transplantation-Breast Care Unit, Aldo Moro University of Bari Medical School, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Victor Neuhaus
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Klaus Preidler
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Pascal Baltzer
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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26
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Zhou XZ, Liu LH, He S, Yao HF, Chen LP, Deng C, Li SL, Zhang XY, Lai H. Diagnostic value of Kaiser score combined with breast vascular assessment from breast MRI for the characterization of breast lesions. Front Oncol 2023; 13:1165405. [PMID: 37483510 PMCID: PMC10359820 DOI: 10.3389/fonc.2023.1165405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/06/2023] [Indexed: 07/25/2023] Open
Abstract
Objectives The Kaiser scoring system for breast magnetic resonance imaging is a clinical decision-making tool for diagnosing breast lesions. However, the Kaiser score (KS) did not include the evaluation of breast vascularity. Therefore, this study aimed to use KS combined with breast vascular assessment, defined as KS*, and investigate the effectiveness of KS* in differentiating benign from malignant breast lesions. Methods This retrospective study included 223 patients with suspicious breast lesions and pathologically verified results. The histopathological diagnostic criteria were according to the fifth edition of the WHO classification of breast tumors. The KS* was obtained after a joint evaluation combining the original KS and breast vasculature assessment. The receiver operating characteristic (ROC) curve was used for comparing differences in the diagnostic performance between KS* and KS, and the area under the receiver operating characteristic (AUC) was compared. Results There were 119 (53.4%) benign and 104 (46.6%) malignant lesions in total. The overall sensitivity, specificity, and accuracy of increased ipsilateral breast vascularity were 69.2%, 76.5%, and 73.1%, respectively. The overall sensitivity, specificity, and accuracy of AVS were 82.7%, 76.5%, and 79.4%, respectively. For all lesions included the AUC of KS* was greater than that of KS (0.877 vs. 0.858, P = 0.016). The largest difference in AUC was observed in the non-mass subgroup (0.793 vs. 0.725, P = 0.029). Conclusion Ipsilaterally increased breast vascularity and a positive AVS sign were significantly associated with malignancy. KS combined with breast vascular assessment can effectively improve the diagnostic ability of KS for breast lesions, especially for non-mass lesions.
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Affiliation(s)
- Xin-zhu Zhou
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lian-hua Liu
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuang He
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui-fang Yao
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Li-ping Chen
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chen Deng
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuang-Ling Li
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Hua Lai
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Kubota K, Mori M, Fujioka T, Watanabe K, Ito Y. Magnetic resonance imaging diagnosis of non-mass enhancement of the breast. J Med Ultrason (2001) 2023; 50:361-366. [PMID: 36801992 PMCID: PMC10353960 DOI: 10.1007/s10396-023-01290-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/11/2023] [Indexed: 02/21/2023]
Abstract
Breast Imaging Reporting and Data System magnetic resonance imaging (BI-RADS-MRI) classifies lesions as mass, non-mass enhancement (NME), or focus. BI-RADS ultrasound does not currently have the concept of non-mass. Additionally, knowing the concept of NME in MRI is significant. Thus, this study aimed to provide a narrative review of NME diagnosis in breast MRI. Lexicons are defined with distribution (focal, linear, segmental, regional, multiple regions, and diffuse) and internal enhancement patterns (homogenous, heterogeneous, clumped, and clustered ring) in the case of NME. Among these, linear, segmental, clumped, clustered ring, and heterogeneous are the terms that suggest malignancy. Hence, a hand search was conducted for reports of malignancy frequencies. The malignancy frequency in NME is widely distributed, ranging from 25 to 83.6%, and the frequency of each finding varies. Latest techniques, such as diffusion-weighted imaging and ultrafast dynamic MRI, are attempted to differentiate NME. Additionally, attempts are made in the preoperative setting to determine the concordance of lesion spread based on findings and the presence of invasion.
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Affiliation(s)
- Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343-8555, Japan.
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kaoru Watanabe
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343-8555, Japan
| | - Yuko Ito
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamikoshigaya, Koshigaya, Saitama, 343-8555, Japan
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Ecanow JS, Ecanow DB, Hack B, Leloudas N, Prasad PV. Feasibility of Diffusion Tensor Imaging for Decreasing Biopsy Rates in Breast Imaging: Interim Analysis of a Prospective Study. Diagnostics (Basel) 2023; 13:2226. [PMID: 37443620 DOI: 10.3390/diagnostics13132226] [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: 05/22/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Because of the limited specificity of diagnostic imaging, many breast lesions referred for biopsy turn out to be benign. The objective of this study was to evaluate whether diffusion tensor MRI (DTI) parametric maps can be used to safely avoid biopsy of breast lesions. Individuals referred for breast biopsy based on mammogram (MG), ultrasound (US), and/or contrast enhanced (CE)-MRI were recruited. Scans consisting of T2-weighted and DTI sequences were performed. Multiple DTI-derived parametric color maps were evaluated semi-quantitatively to characterize lesions as "definitely benign," "not definitely benign," or "suspicious." All patients subsequently underwent biopsy. In this moderately-sized prospective study, 21 out of 47 pathologically proven benign lesions were characterized by both readers as "definitely benign," which would have precluded the need for biopsy. Biopsy was recommended for 11 out of 13 cancers that were characterized as "suspicious." In the remaining two cancers and 26 of 47 benign lesions, the scans were characterized as "not definitely benign" and hence required biopsy. The main causes for "not definitely benign" scans were small lesion sizes and noise. The results suggest that in appropriately selected patients, DTI may be used to safely reduce the number of unnecessary breast biopsies.
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Affiliation(s)
- Jacob S Ecanow
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - David B Ecanow
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Bradley Hack
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nondas Leloudas
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Pottumarthi V Prasad
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
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Lang M, Liang P, Shen H, Li H, Yang N, Chen B, Chen Y, Ding H, Yang W, Ji X, Zhou P, Cui L, Wang J, Xu W, Ye X, Liu Z, Yang Y, Wei T, Wang H, Yan Y, Wu C, Wu Y, Shi J, Wang Y, Fang X, Li R, Yu J. Head-to-head comparison of perfluorobutane contrast-enhanced US and multiparametric MRI for breast cancer: a prospective, multicenter study. Breast Cancer Res 2023; 25:61. [PMID: 37254149 DOI: 10.1186/s13058-023-01650-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 04/22/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. PATIENTS AND METHODS This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. RESULTS In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. CONCLUSIONS PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.
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Affiliation(s)
- Manlin Lang
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital & Chinese PLA Medical School, Beijing, 100039, China
| | - Ping Liang
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China
| | - Huiming Shen
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, 210009, China
| | - Hang Li
- Department of Breast Surgery, Affiliated Hospital of Putian University, Putian, 351100, China
| | - Ning Yang
- Department of Ultrasound, Xingcheng People's Hospital, Xingcheng, 125100, China
| | - Bo Chen
- Department of Ultrasound Medicine, Lu'an People's Hospital of Anhui Province, Liuan, 237000, China
| | - Yixu Chen
- Department of Ultrasound, The Fifth People's Hospital of Chengdu, Chengdu, 611130, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Shanghai, 200040, China
| | - Weiping Yang
- Department of Ultrasound, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Xiaohui Ji
- Department of Ultrasound, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Changsha, 410000, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, 100191, China
| | - Jiandong Wang
- General Surgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Wentong Xu
- General Surgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xiuqin Ye
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical College of Jinan University, Shenzhen Medical Ultrasound Engineering Center, Shenzhen People's Hospital, Shenzhen, 518020, China
| | - Zhixing Liu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Yu Yang
- Department of Ultrasound, Beijing Friendship Hospital, Beijing, 100050, China
| | - Tianci Wei
- Department of Ultrasound, The 2nd Affiliated Hospital of Harbin, Harbin, 150001, China
| | - Hui Wang
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, 130033, China
| | - Yuanyuan Yan
- Department of Ultrasound, Zhengzhou Central Hospital, Zhengzhou, 450000, China
| | - Changjun Wu
- Department of Ultrasonography, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Yiyun Wu
- Department of Ultrasound, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Jingwen Shi
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, China
| | - Yaxi Wang
- Department of Ultrasound, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Xiuxia Fang
- Department of Ultrasound, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Ran Li
- Department of Ultrasound, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453100, China
| | - Jie Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.
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Yao FF, Zhang Y. A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker. Clin Imaging 2023; 98:36-58. [PMID: 36996598 DOI: 10.1016/j.clinimag.2023.03.016] [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: 01/10/2023] [Revised: 03/03/2023] [Accepted: 03/21/2023] [Indexed: 04/01/2023]
Abstract
Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.
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Affiliation(s)
- Fei-Fei Yao
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China.
| | - Yan Zhang
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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31
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Milon A, Flament V, Gueniche Y, Kermarrec E, Chabbert-Buffet N, Darai É, Touboul C, Razakamanantsoa L, Thomassin-Naggara I. How to optimize MRI breast protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences. Diagn Interv Imaging 2023; 104:284-291. [PMID: 36801096 DOI: 10.1016/j.diii.2023.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
PURPOSE The purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI. MATERIAL AND METHODS Women who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10-3 mm2/s to classify the lesions based on morphology and these two functional criteria only. RESULTS Two hundred fifty-seven women (median age: 51 years; range: 16-92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10-3 mm2/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively. CONCLUSION BI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.
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Affiliation(s)
- Audrey Milon
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France.
| | - Vincent Flament
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Yoram Gueniche
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Edith Kermarrec
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Nathalie Chabbert-Buffet
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Émile Darai
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Cyril Touboul
- Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France; Department of Gynecology and Obstetrics, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Leo Razakamanantsoa
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France
| | - Isabelle Thomassin-Naggara
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Sorbonne Université, Institut Universitaire de Cancérologie, 75005, Paris, France
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Kazerouni AS, Rahbar H, Partridge SC. Is NME the enemy of breast DWI? Eur J Radiol 2023; 159:110648. [PMID: 36571925 PMCID: PMC10601596 DOI: 10.1016/j.ejrad.2022.110648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Affiliation(s)
- Anum S Kazerouni
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, WA, United States
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Evaluation of pretreatment ADC values as predictors of treatment response to neoadjuvant chemotherapy in patients with breast cancer - a multicenter study. Cancer Imaging 2022; 22:68. [PMID: 36494872 PMCID: PMC9733082 DOI: 10.1186/s40644-022-00501-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/25/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) can be used to diagnose breast cancer. Diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can reflect tumor microstructure in a non-invasive manner. The correct prediction of response of neoadjuvant chemotherapy (NAC) is crucial for clinical routine. Our aim was to compare ADC values between patients with pathological complete response (pCR) and non-responders based upon a multi-center design to improve the correct patient selection, which patient would more benefit from NAC and which patient would not. METHODS For this study, data from 4 centers (from Japan, Brazil, Spain and United Kingdom) were retrospectively acquired. The time period was overall 2003-2019. The patient sample comprises 250 patients (all female; median age, 50.5). In every case, pretreatment breast MRI with DWI was performed. pCR was assessed by experienced pathologists in every center using the surgical specimen in the clinical routine work up. pCR was defined as no residual invasive disease in either breast or axillary lymph nodes after NAC. ADC values between the group with pCR and those with no pCR were compared using the Mann-Whitney U test (two-group comparisons). Univariable and multivariabe logistic regression analysis was performed to predict pCR status. RESULTS Overall, 83 patients (33.2%) achieved pCR. The ADC values of the patient group with pCR were lower compared with patients without pCR (0.98 ± 0.23 × 10- 3 mm2/s versus 1.07 ± 0.24 × 10- 3 mm2/s, p = 0.02). The ADC value achieved an odds ratio of 4.65 (95% CI 1.40-15.49) in univariable analysis and of 3.0 (95% CI 0.85-10.63) in multivariable analysis (overall sample) to be associated with pCR status. The odds ratios differed in the subgroup analyses in accordance with the molecular subtype. CONCLUSIONS The pretreatment ADC-value is associated with pathological complete response after NAC in breast cancer patients. This could aid in clinical routine to reduce treatment toxicity for patients, who would not benefit from NAC. However, this must be tested in further studies, as the overlap of the ADC values in both groups is too high for clinical prediction.
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Marino MA, Avendano D, Sevilimedu V, Thakur S, Martinez D, Lo Gullo R, Horvat JV, Helbich TH, Baltzer PAT, Pinker K. Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors. Eur J Radiol 2022; 156:110523. [PMID: 36122521 PMCID: PMC10014485 DOI: 10.1016/j.ejrad.2022.110523] [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: 04/18/2022] [Revised: 08/14/2022] [Accepted: 09/09/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors. METHOD Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader. RESULTS The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %). CONCLUSION mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.
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Affiliation(s)
- Maria Adele Marino
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Nuevo Leon, Mexico
| | - Varadan Sevilimedu
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, NY, USA
| | - Sunitha Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Danny Martinez
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Joao V Horvat
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA.
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Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling. Eur Radiol 2022; 32:6608-6618. [PMID: 35726099 PMCID: PMC9815725 DOI: 10.1007/s00330-022-08899-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/10/2022] [Accepted: 05/19/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.
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Lo Gullo R, Sevilimedu V, Baltzer P, Le Bihan D, Camps-Herrero J, Clauser P, Gilbert FJ, Iima M, Mann RM, Partridge SC, Patterson A, Sigmund EE, Thakur S, Thibault FE, Martincich L, Pinker K. A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice. Eur Radiol 2022; 32:6588-6597. [PMID: 35507050 PMCID: PMC9064723 DOI: 10.1007/s00330-022-08833-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI. METHODS An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test. RESULTS Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10-3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values. CONCLUSION The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform. KEY POINTS • Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, NY, New York, 10017, USA
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Denis Le Bihan
- NeuroSpin/Joliot, CEA-Saclay Center, Paris-Saclay University, Gif-sur-Yvette, France
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- National Institute for Physiological Sciences, Okazaki, Japan
| | | | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
| | - Ritse M Mann
- Department of Diagnostic Imaging, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, 6, 60 1st Avenue, New York, NY, 10016, USA
| | - Sunitha Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Fabienne E Thibault
- Department of Medical Imaging, Institut Curie, 26 Rue d'Ulm, F-75005, Paris, France
| | - Laura Martincich
- Unit of Radiodiagnostics, Ospedale Cardinal G. Massaia -ASL AT, Via Conte Verde 125, 14100, Asti, Italy
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria.
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Penn A, Medved M, Abe H, Dialani V, Karczmar GS, Brousseau D. Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding. BMC Med Imaging 2022; 22:171. [PMID: 36175878 PMCID: PMC9524062 DOI: 10.1186/s12880-022-00897-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 09/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Thresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies.
Purpose We evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10–3 mm2/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics. Study type Prospective. Population 55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy. Field strength/sequence 1.5 T and 3.0 T. DWI. Assessment Analysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images. Statistical tests Improvement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance. Results Using ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies. Data conclusion Supplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.
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Affiliation(s)
- Alan Penn
- Alan Penn and Associates, Inc., Rockville, MD, 20850, USA.
| | | | | | - Vandana Dialani
- Beth Israel Deaconess Medical Center, Boston, MA, 02467, USA
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Wang S, Liu H, Yang T, Huang M, Zheng B, Wu T, Han L, Zhang Y, Ren J. Machine learning based on automated breast volume scanner ( ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1577-1587. [DOI: 10.1002/ima.22724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 02/25/2022] [Indexed: 09/11/2023]
Abstract
AbstractBI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.
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Affiliation(s)
- Shi‐jie Wang
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Hua‐qing Liu
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Tao Yang
- Department of Ultrasound The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Ming‐quan Huang
- Department of Breast Surgery The Affiliated Hospital of Southwest Medical University Sichuan China
| | - Bo‐wen Zheng
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Tao Wu
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Lan‐qing Han
- Artificial Intelligence Innovation Center Research Institute of Tsinghua Guangzhou China
| | - Yong Zhang
- Department of Nuclear Medicine The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Jie Ren
- Department of Medical Ultrasonics The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
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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: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
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Lee SA, Lee Y, Ryu HS, Jang MJ, Moon WK, Moon HG, Lee SH. Diffusion-weighted Breast MRI in Prediction of Upstaging in Women with Biopsy-proven Ductal Carcinoma in Situ. Radiology 2022; 305:307-316. [PMID: 35787199 DOI: 10.1148/radiol.213174] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue.
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Affiliation(s)
- Shin Ae Lee
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Youkyoung Lee
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Han Suk Ryu
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Myoung-Jin Jang
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Woo Kyung Moon
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Hyeong-Gon Moon
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
| | - Su Hyun Lee
- From the Departments of Surgery (S.A.L., H.G.M.), Radiology (Y.L., W.K.M., S.H.L.), and Pathology (H.S.R.), Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; and Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (M.J.J.)
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Yin HL, Jiang Y, Xu Z, Jia HH, Lin GW. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol 2022; 149:2575-2584. [PMID: 35771263 DOI: 10.1007/s00432-022-04142-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023]
Abstract
PURPOSE To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists. METHODS A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance. RESULTS The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively. CONCLUSION Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.
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Affiliation(s)
- Hao-Lin Yin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Yu Jiang
- Department of Radiology, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Zihan Xu
- Lung Cancer Center, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Hui-Hui Jia
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Guang-Wu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China.
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Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. ROFO-FORTSCHR RONTG 2022; 194:966-974. [PMID: 35439830 DOI: 10.1055/a-1775-8572] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND The MRI of the breast is of great importance in the diagnosis of disorders of the breast. This can be stated for the primary diagnosis as well as the follow up. Of special interest is diffusion weighted imaging (DWI), which has an increasingly important role. The present review provides results regarding the diagnostic and prognostic relevance of DWI for disorders of the breast. METHODS Under consideration of the recently published literature, the clinical value of DWI of the breast is discussed. Several diagnostic applications are shown, especially for the primary diagnosis of unclear tumors of the breast, the prediction of the axillary lymph node status and the possibility of a native screening. Moreover, correlations between DWI and histopathology features and treatment prediction with DWI are provided. RESULTS Many studies have shown the diagnostic value of DWI for the primary diagnosis of intramammary lesions. Benign lesions of the breast have significantly higher apparent diffusion coefficients (ADC values) compared to malignant tumors. This can be clinically used to reduce unnecessary biopsies in clinical routine. However, there are inconclusive results for the prediction of the histological subtype of the breast cancer. DWI can aid in the prediction of treatment to neoadjuvant chemotherapy. CONCLUSION DWI is a very promising imaging modality, which should be included in the standard protocol of the MRI of the breast. DWI can provide clinically value in the diagnosis as well as for prognosis in breast cancer. KEY POINTS · DWI can aid in the discrimination between benign and malignant tumors of the breast and therefore avoiding unnecessary biopsies.. · The ADC value cannot discriminate between immunhistochemical subtypes of the breast cancer. · The ADC value of breast cancer increases under neoadjuvant chemotherapy and can by this aid in treatment prediction.. · There is definite need of standardisation for clinical translation. CITATION FORMAT · Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8572.
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Affiliation(s)
- Hans Jonas Meyer
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
| | - Mireille Martin
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
| | - Timm Denecke
- Diagnostic and Interventional Radiology, University of Leipzig Faculty of Medicine, Leipzig, Germany
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Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Thakur SB, Jochelson MS, Thakur N, Baltzer PAT, Helbich TH, Pinker K. Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance. Cancers (Basel) 2022; 14:cancers14071743. [PMID: 35406514 PMCID: PMC8997089 DOI: 10.3390/cancers14071743] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/21/2022] [Accepted: 03/25/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Currently, breast contrast-enhanced MRI is the most sensitive imaging technique for breast cancer detection; however, its specificity is low given the common characteristics shared by benign breast lesions and some cancers. This leads to a high number of false-positive cases and, therefore, unnecessary biopsies. Multiparametric MRI including diffusion-weighted imaging assists in this task by increasing the specificity for breast lesion discrimination. Nevertheless, interpretation of breast MRI is still highly dependent on the reader’s level of experience. Our work combines radiomic features extracted from multiparametric MRI to generate predictive models for breast cancer differentiation. Additionally, decision support models were compared with the performance of two breast dedicated radiologists for lesion differentiation. Our work proves the potential of multiparametric radiomics coupled with machine learning to be implemented in clinical practice for lesion differentiation on breast MRI. AI algorithms show value to assist less experienced readers, improving the accuracy for breast lesion discrimination. Abstract This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
- Correspondence: (I.D.N.); (P.G.)
| | - Peter Gibbs
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Correspondence: (I.D.N.); (P.G.)
| | - Jeffrey S. Reiner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Sunitha B. Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Nikita Thakur
- Touro College of Osteopathic Medicine, Middletown, NY 10940, USA;
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, 1090 Wien, Austria; (P.A.T.B.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, 1090 Wien, Austria; (P.A.T.B.); (T.H.H.)
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
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Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI. Diagnostics (Basel) 2022; 12:diagnostics12020332. [PMID: 35204423 PMCID: PMC8871288 DOI: 10.3390/diagnostics12020332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 11/16/2022] Open
Abstract
Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10−3 mm2/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10−3 mm2/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10−3 mm2/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.
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Vamvakas A, Tsivaka D, Logothetis A, Vassiou K, Tsougos I. Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods. Technol Cancer Res Treat 2022; 21:15330338221087828. [PMID: 35341421 PMCID: PMC8966070 DOI: 10.1177/15330338221087828] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating benign and malignant breast lesions. Methods: The dataset includes mpMR images of 140 female patients with mass-like breast lesions (70 benign and 70 malignant), consisting of Dynamic Contrast Enhanced (DCE) and T2-weighted sequences, and the Apparent Diffusion Coefficient (ADC) calculated from the Diffusion Weighted Imaging (DWI) sequence. Tumor masks were manually defined in all consecutive slices of the respective MRI volumes and 3D radiomic features were extracted with the Pyradiomics package. Feature dimensionality reduction was based on statistical tests and the Boruta wrapper. Hierarchical Clustering on Spearman's rank correlation coefficients between features and Random Forest classification for obtaining feature importance, were implemented for selecting the final feature subset. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) classifiers, were trained and tested with bootstrap validation in differentiating breast lesions. A Support Vector Machine (SVM) classifier was also exploited for comparison. The Receiver Operator Characteristic (ROC) curves and DeLong's test were utilized to evaluate the classification performances. Results: The final feature subset consisted of 5 features derived from the lesion shape and the first order histogram of DCE and ADC images volumes. XGboost and LGBM achieved statistically significantly higher average classification performances [AUC = 0.95 and 0.94 respectively], followed by Adaboost [AUC = 0.90], GB [AUC = 0.89] and SVM [AUC = 0.88]. Conclusion: Overall, the integration of Ensemble Learning methods within mpMRI radiomic analysis can improve the performance of computer-assisted diagnosis of breast cancer lesions.
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Affiliation(s)
- Alexandros Vamvakas
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Dimitra Tsivaka
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Andreas Logothetis
- Medical Physics Laboratory, Medical School, 393206National and Kapodistrian University of Athens, Athens, Greece
| | - Katerina Vassiou
- Department of Anatomy and Radiology, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Ioannis Tsougos
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
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Meng L, Zhao X, Lu L, Xing Q, Wang K, Guo Y, Shang H, Chen Y, Huang M, Sun Y, Zhang X. A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value. Front Oncol 2021; 11:779642. [PMID: 34926290 PMCID: PMC8675081 DOI: 10.3389/fonc.2021.779642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI. Methods This was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test. Results The study involved 243 women (mean age, 43.1 years; age range, 18-67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity. Conclusion For MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.
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Affiliation(s)
- Lingsong Meng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Lu
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingna Xing
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- Magnetic Resonance (MR) Research China, General Electric (GE) Healthcare, Beijing, China
| | - Yafei Guo
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Honglei Shang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengyue Huang
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongbing Sun
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoan Zhang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Li Y, Yang ZL, Lv WZ, Qin YJ, Tang CL, Yan X, Guo YH, Xia LM, Ai T. Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses. Front Oncol 2021; 11:738330. [PMID: 34631572 PMCID: PMC8493069 DOI: 10.3389/fonc.2021.738330] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/07/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose We aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI). Methods In this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses. Results The routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis. Conclusions Our study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.
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Affiliation(s)
- Yan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenlu L Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhi Z Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, China
| | - Yanjin J Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Caili L Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yan
- Scientific Marketing, Siemens Healthcare Ltd., Shanghai, China
| | - Yihao H Guo
- Magnetic Resonance (MR) Collaboration, Siemens Healthcare, Guangzhou, China
| | - Liming M Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Vassiou K, Fanariotis M, Tsougos I, Fezoulidis I. Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography. Acta Radiol 2021; 63:1332-1343. [PMID: 34605311 DOI: 10.1177/02841851211041822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. PURPOSE To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. MATERIAL AND METHODS A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. RESULTS In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. CONCLUSION We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.
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Affiliation(s)
- Katerina Vassiou
- Department of Anatomy, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
| | - Michael Fanariotis
- Department of Radiology, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
- Department of Radiology, Sykehuset Telemark HF, Skien, Telemark, Norway
| | - Ioannis Tsougos
- Department of Medical Physics, Faculty of Medicine, University of Thessaly, Larissa, Greece
| | - Ioannis Fezoulidis
- Department of Radiology, Faculty of Medicine, University of Thessaly, Biopolis, Larissa, Greece
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Abstract
Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.
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Affiliation(s)
- Anabel M Scaranelo
- Medical Imaging Department, 12366University of Toronto, Ontario, Canada.,Breast Imaging Division, Joint Department of Medical Imaging, University of Health Network, Sinai Health and Women's College Hospital, Toronto, Ontario, Canada
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Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Sooknanan C, Thakur SB, Jochelson MS, Sevilimedu V, Morris EA, Baltzer PAT, Helbich TH, Pinker K. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics (Basel) 2021; 11:diagnostics11060919. [PMID: 34063774 PMCID: PMC8223779 DOI: 10.3390/diagnostics11060919] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/11/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
- Correspondence: (I.D.N.); (P.G.)
| | - Peter Gibbs
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Correspondence: (I.D.N.); (P.G.)
| | - Jeffrey S. Reiner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Caleb Sooknanan
- Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, New York, NY 10065, USA;
| | - Sunitha B. Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA;
| | - Elizabeth A. Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
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