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Karabulut UE, Celik Yabul F, Polat YB, Donmez Z, Toprak H, Alkan A, Yildiz S. Contrıbutıon of Kaıser score in non-mass enhanced breast lesions. Eur J Radiol 2025; 185:112002. [PMID: 39970546 DOI: 10.1016/j.ejrad.2025.112002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 01/06/2025] [Accepted: 02/12/2025] [Indexed: 02/21/2025]
Abstract
OBJECTIVE Our study aimed to investigate the effectiveness of Kaiser Score (KS) in diagnosing Non-mass enhanced (NME) lesions and its impact on the inter-reader agreement between experienced and inexperienced readers. MATERIALS AND METHODS A retrospective analysis was conducted on 189 NME lesions from 182 MRIs. Two readers (an experienced radiologist and a radiology resident) independently evaluated lesions using the KS, blinded to clinical and pathological data. The KS was modified (MKS) by adding 2 points for microcalcifications on mammography and subtracting 4 points for ADC values > 1.4 x 10^-3 mm2/s. Interobserver agreement was assessed with the Intraclass Correlation Coefficient (ICC), and diagnostic performance was evaluated via ROC analysis, with sensitivity and specificity calculated at > 4 and > 5 cut-offs. RESULTS Interobserver agreement improved with MKS (ICC: 0.763) compared to KS (ICC: 0.667). For the experienced reader, both KS and MKS achieved high sensitivity (>94 %) at a cut-off of > 4. At > 5, specificity improved from 40.5 % to 58.7 % for KS and 39.1 % to 55.8 % for MKS without significantly affecting sensitivity. For the inexperienced reader, MKS improved sensitivity (96.8 %) and specificity (39 %) at > 4. At > 5, specificity increased to 55.8 %, with a non-significant decrease in sensitivity (86.2 %). CONCLUSION The Kaiser Score is a quick and systematic tool that enhances diagnostic accuracy and reduces biopsy rates, particularly benefiting inexperienced readers. While higher thresholds improve specificity for experienced readers, they may reduce sensitivity for inexperienced readers, potentially missing malignancies. As a complement to BI-RADS, the Kaiser Score helps standardize evaluations and bridge experience gaps in MRI interpretation.
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Affiliation(s)
- Ummuhan Ebru Karabulut
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey.
| | - Fatma Celik Yabul
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
| | - Yagmur Basak Polat
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
| | - Zeynep Donmez
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
| | - Huseyin Toprak
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
| | - Alpay Alkan
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
| | - Seyma Yildiz
- Bezmialem Vakif University, Faculty of Medicine, Department of Radiology, Fatih, Istanbul, Turkey
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Vatteroni G, Dietzel M, Baltzer PAT. Can structured integration of BI-RADS criteria by a clinical decision rule reduce the number of unnecessary biopsies in BI-RADS 4 lesions? A systematic review and meta-analysis. Eur Radiol 2025; 35:1504-1513. [PMID: 39694886 PMCID: PMC11836227 DOI: 10.1007/s00330-024-11274-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 09/12/2024] [Accepted: 11/04/2024] [Indexed: 12/20/2024]
Abstract
AIM This systematic review and meta-analysis investigate the added value of structured integration of Breast Imaging Reporting and Data System (BI-RADS) criteria using the Kaiser score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions. MATERIAL AND METHODS A systematic review and meta-analysis were conducted using predefined criteria. Eligible articles, published in English until May 2024, dealt with KS in the context of BI-RADS 4 MRI. Two reviewers extracted study characteristics, including true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were calculated using bivariate random effects. Fagan nomograms identified the maximum pre-test probability at which post-test probabilities of a negative MRI aligned with the 2% malignancy rate benchmark for downgrading BI-RADS 4 to BI-RADS 3. I² statistics and meta-regression explored sources of heterogeneity. p-values < 0.05 were considered significant. RESULTS Seven studies with 1877 lesions (833 malignant, 1044 benign) were included. The average breast cancer prevalence was 47.3%. Pooled sensitivity was 94.3% (95%-CI 88.9%-97.1%), and pooled specificity was 68.1% (95%-CI 56.6%-77.7%) using a random effects model. Overall, 52/833 cases were FNs (6.2%). Fagan nomograms showed that KS could rule out breast cancer in BI-RADS 4 lesions at a pre-test probability of 20.3% for all lesions, 25.4% for masses, and 15.2% for non-mass lesions. CONCLUSIONS In MRI-assessed BI-RADS 4 lesions, applying structured BI-RADS criteria with the KS reduces unnecessary biopsies by 70% with a 6.2% FN rate. Breast cancer can be ruled out up to pre-test probabilities of 20.3%. KEY POINTS Question What, if any, value is added by structured integration of BI-RADS criteria using the Kaiser Score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions? Findings The structured integration of BI-RADS criteria using the Kaiser Score (KS) reduces unnecessary biopsies in BI-RADS 4 lesions by 70%. Clinical relevance The structured approach offered by the Kaiser Score (KS) avoids unnecessary recalls, potentially reducing patient anxiety, lessening the burden on medical personnel, and the need for further imaging and biopsies due to more objective and thus efficient clinical decision-making in evaluating BI-RADS 4 lesions.
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Affiliation(s)
- Giulia Vatteroni
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Milan, Pieve Emanuele, Italy
| | - Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna and General Hospital, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Kayadibi Y, Saracoglu MS, Kurt SA, Deger E, Boy FNS, Ucar N, Icten GE. Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models. Acad Radiol 2024; 31:3511-3523. [PMID: 38641449 DOI: 10.1016/j.acra.2024.03.025] [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: 02/16/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/21/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate the effectiveness of machine learning-based clinical, radiomics, and combined models in differentiating idiopathic granulomatous mastitis (IGM) from malignancy, both presenting as non-mass enhancement (NME) lesions on magnetic resonance imaging (MRI), and to compare these models with radiological evaluation. MATERIAL AND METHODS A total of 178 patients (69 IGM and 109 breast cancer patients) with NME on breast MRI evaluated between March 2018 and April 2022, were included in this two-center study. Age, skin changes, presence of fistula, and abscess were recorded from hospital records. Two experienced radiologists evaluated MRI images according to the breast imaging reporting and data system 2013 lexicon. Lesions were segmented independently on T2-weighted, apparent diffusion coefficient, and post-contrast-T1-weighted sequences. Data were split into training and external testing sets. Machine learning models were built using Light GBM (light gradient-boosting machine). Radiological, clinical, radiomics, and clinical-radiomics models were created and compared. Decision curve analysis was performed. Quality of reporting and that of methodology were evaluated using CLEAR and METRICS tools. RESULTS IGM group was younger (p = 0.014). Abscesses (p < 0.001), fistulas (p < 0.001), and skin changes (p < 0.001) were significantly more common in the IGM group. No significant difference was detected in terms of lesion size (p = 0.213). In the evaluation of NME, the lowest performance belonged to the radiologists' evaluation (AUC for training, 0.740; for testing, 0.737), while the highest AUC was achieved by the model developed by combined clinical and radiomics features (AUC for training, 0.979; for testing, 0.942). CONCLUSION Our study has shown that the machine learning-based clinical-radiomics model might have the potential to accurately discriminate IGM and malignant lesions in evaluating NME areas.
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Affiliation(s)
- Yasemin Kayadibi
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye.
| | - Mehmet Sakıpcan Saracoglu
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Seda Aladag Kurt
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Enes Deger
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Fatma Nur Soylu Boy
- Fatih Sultan Mehmet Education and Research Hospital, Department of Radiology, Atasehir, Istanbul, Türkiye
| | - Nese Ucar
- Gaziosmanspasa Education and Research Hospital, Department of Radiology, Gaziosmanpasa, Istanbul, Türkiye
| | - Gul Esen Icten
- Senology Research Institute, Acibadem Mehmet Ali Aydinlar University, Maslak, Istanbul, Türkiye
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Hijazi M, Chahine R, Berjawi G, Jabbour Y, El Annan T, Ibrahim R, Nassar L. Reliability of Kaiser Score in Assessing Additional Breast Lesions Identified on Staging MRI in Patients with Breast Cancer. Diagnostics (Basel) 2024; 14:1726. [PMID: 39202214 PMCID: PMC11353333 DOI: 10.3390/diagnostics14161726] [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: 06/29/2024] [Revised: 07/28/2024] [Accepted: 08/01/2024] [Indexed: 09/03/2024] Open
Abstract
(1) Background: The Kaiser score is a user-friendly tool that evaluates lesions on breast MRI and has been studied in the general population and a few specific clinical scenarios. We aim to evaluate the performance of the Kaiser score in the characterization of additional lesions identified on staging breast MRI. (2) Methods: The Kaiser score of the biopsied additional lesions identified on staging MRI in recently diagnosed breast cancer patients was retrospectively determined. Statistical analysis was performed to evaluate the diagnostic capability of the Kaiser score and whether it is affected by different imaging and pathological parameters of the additional and the index lesion. (3) Results: Seventy-six patients with ninety-two MRI-detected lesions constitute the studied population. There was a statistically significant difference in the Kaiser score between benign and malignant lesions, irrespective of the pathology of the index cancer (p = 0.221) or the size and the imaging features of the additional lesion. Using a cutoff of 5 and above for suspicious lesions, biopsy could have been avoided in 34/92 lesions. (4) Conclusions: The Kaiser score can assist radiologists in the evaluation of additional MRI lesions identified in recently diagnosed breast cancer patients, thus decreasing the number of unneeded biopsies and delays in definitive surgical management.
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Affiliation(s)
- Madiha Hijazi
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
| | - Reve Chahine
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
| | - Ghina Berjawi
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
| | - Yara Jabbour
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
| | - Tamara El Annan
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
| | - Roy Ibrahim
- Department of Diagnostic Radiology, Lebanese American University Medical Center, Beirut 1100, Lebanon;
| | - Lara Nassar
- Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 1107, Lebanon; (M.H.); (R.C.); (G.B.); (Y.J.); (T.E.A.)
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Dietzel M, Bernathova M, Clauser P, Kapetas P, Uder M, Baltzer PAT. Added value of clinical decision rules for the management of enhancing breast MRI lesions: A systematic comparison of the Kaiser score and the Göttingen score. Eur J Radiol 2023; 169:111185. [PMID: 37939606 DOI: 10.1016/j.ejrad.2023.111185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/16/2023] [Accepted: 11/02/2023] [Indexed: 11/10/2023]
Abstract
PURPOSE We investigated the added value of two internationally used clinical decision rules in the management of enhancing lesions on breast MRI. METHODS This retrospective, institutional review board approved study included consecutive patients from two different populations. Patients received breast MRI according to the recommendations of the European Society of Breast Imaging (EUSOBI). Initially, all examinations were assessed by expert readers without using clinical decision rules. All lesions rated as category 4 or 5 according to the Breast Imaging Reporting and Data System were histologically confirmed. These lesions were re-evaluated by an expert reader blinded to the histology. He assigned each lesion a Göttingen score (GS) and a Kaiser score (KS) on different occasions. To provide an estimate on inter-reader agreement, a second fellowship-trained reader assessed a subset of these lesions. Subgroup analyses based on lesion type (mass vs. non-mass), size (>1 cm vs. ≤ 1 cm), menopausal status, and significant background parenchymal enhancement were conducted. The areas under the ROC curves (AUCs) for the GS and KS were compared, and the potential to avoid unnecessary biopsies was determined according to previously established cutoffs (KS > 4, GS > 3) RESULTS: 527 lesions in 506 patients were included (mean age: 51.8 years, inter-quartile-range: 43.0-61.0 years). 131/527 lesions were malignant (24.9 %; 95 %-confidence-interval: 21.3-28.8). In all subgroups, the AUCs of the KS (median = 0.91) were higher than those of the GS (median = 0.83). Except for "premenopausal patients" (p = 0.057), these differences were statistically significant (p ≤ 0.01). Kappa agreement was higher for the KS (0.922) than for the GS (0.358). CONCLUSION Both the KS and the GS provided added value for the management of enhancing lesions on breast MRI. The KS was superior to the GS in terms of avoiding unnecessary biopsies and showed superior inter-reader agreement; therefore, it may be regarded as the clinical decision rule of choice.
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Affiliation(s)
- Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer-Guertel 18-20, Vienna, Austria.
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Wu T, Alikhassi A, Curpen B. How Does Diagnostic Accuracy Evolve with Increased Breast MRI Experience? Tomography 2023; 9:2067-2078. [PMID: 37987348 PMCID: PMC10661242 DOI: 10.3390/tomography9060162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/22/2023] Open
Abstract
Introduction: Our institution is part of a provincial program providing annual breast MRI screenings to high-risk women. We assessed how MRI experience, background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) affect the biopsy-proven predictive value (PPV3) and accuracy for detecting suspicious MRI findings. Methods: From all high-risk screening breast MRIs conducted between 1 July 2011 and 30 June 2020, we reviewed all BI-RADS 4/5 observations with pathological tissue diagnoses. Overall and annual PPV3s were computed. Radiologists with fewer than ten observations were excluded from performance analyses. PPV3s were computed for each radiologist. We assessed how MRI experience, BPE, and FGT impacted diagnostic accuracy using logistic regression analyses, defining positive cases as malignancies alone (definition A) or malignant or high-risk lesions (definition B). Findings: There were 536 BI-RADS 4/5 observations with tissue diagnoses, including 77 malignant and 51 high-risk lesions. A total of 516 observations were included in the radiologist performance analyses. The average radiologist's PPV3 was 16 ± 6% (definition A) and 25 ± 8% (definition B). MRI experience in years correlated significantly with positive cases (definition B, OR = 1.05, p = 0.03), independent of BPE or FGT. Diagnostic accuracy improved exponentially with increased MRI experience (definition B, OR of 1.27 and 1.61 for 5 and 10 years, respectively, p = 0.03 for both). Lower levels of BPE significantly correlated with increased odds of findings being malignant, independent of FGT and MRI experience. Summary: More extensive MRI reading experience improves radiologists' diagnostic accuracy for high-risk or malignant lesions, even in MRI studies with increased BPE.
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Affiliation(s)
| | - Afsaneh Alikhassi
- Breast Imaging Division, Medical Imaging Department, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (T.W.); (B.C.)
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Goto M, Sakai K, Toyama Y, Nakai Y, Yamada K. Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels. Jpn J Radiol 2023; 41:1094-1103. [PMID: 37071250 PMCID: PMC10543141 DOI: 10.1007/s11604-023-01435-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) and conduct a comparison with radiologists with various levels of experience. MATERIALS AND METHODS A total of 84 consecutive patients with 86 lesions (51 malignant, 35 benign) presenting NME on breast MRI were analyzed. Three radiologists with different levels of experience evaluated all examinations, based on the Breast Imaging-Reporting and Data System (BI-RADS) lexicon and categorization. For the deep learning method, one expert radiologist performed lesion annotation manually using the early phase of dynamic contrast-enhanced (DCE) MRI. Two segmentation methods were applied: a precise segmentation was carefully set to include only the enhancing area, and a rough segmentation covered the whole enhancing region, including the intervenient non-enhancing area. ResNet50 was implemented using the DCE MRI input. The diagnostic performance of the radiologists' readings and deep learning were then compared using receiver operating curve analysis. RESULTS The ResNet50 model from precise segmentation achieved diagnostic accuracy equivalent [area under the curve (AUC) = 0.91, 95% confidence interval (CI) 0.90, 0.93] to that of a highly experienced radiologist (AUC = 0.89, 95% CI 0.81, 0.96; p = 0.45). Even the model from rough segmentation showed diagnostic performance equivalent to a board-certified radiologist (AUC = 0.80, 95% CI 0.78, 0.82 vs. AUC = 0.79, 95% CI 0.70, 0.89, respectively). Both ResNet50 models from the precise and rough segmentation exceeded the diagnostic accuracy of a radiology resident (AUC = 0.64, 95% CI 0.52, 0.76). CONCLUSION These findings suggest that the deep learning model from ResNet50 has the potential to ensure accuracy in the diagnosis of NME on breast MRI.
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Affiliation(s)
- Mariko Goto
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan.
| | - Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan
| | - Yasuchiyo Toyama
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan
| | - Yoshitomo Nakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan
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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: 6] [Impact Index Per Article: 3.0] [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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Marino MA, Avendano D, Pinker K. Response to "Comment on the value of multiparametric MRI in breast non-mass lesions". Eur J Radiol 2023; 163:110805. [PMID: 37086705 DOI: 10.1016/j.ejrad.2023.110805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/24/2023]
Affiliation(s)
- Maria Adele Marino
- 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
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, New York, NY, USA, Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Nuevo Leon, Mexico
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10
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An Y. Comment on the value of multiparametric MRI in breast non-mass lesions. Eur J Radiol 2023; 163:110806. [PMID: 37015156 DOI: 10.1016/j.ejrad.2023.110806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023]
Affiliation(s)
- Yongyu An
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), No. 54, Youdian Road, Hangzhou 310006, Zhejiang Province, China.
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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: 1.3] [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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Can DWI provide additional value to Kaiser score in evaluation of breast lesions. Eur Radiol 2022; 32:5964-5973. [PMID: 35357535 DOI: 10.1007/s00330-022-08674-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). METHODS Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10-3mm2/s, 1.4 × 10-3mm2/s, 1.53 × 10-3mm2/s, and 1.6 × 10-3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. RESULTS The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). CONCLUSION KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. KEY POINTS • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.
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14
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Baltzer PAT, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. ROFO-FORTSCHR RONTG 2022; 194:1216-1228. [PMID: 35613905 DOI: 10.1055/a-1829-5985] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis. METHOD Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed. RESULTS The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios. KEY POINTS · The KS as an evidence-based decision rule to objectively distinguish benign from malignant breast lesions is based on information contained in T2w und dynamic contrast-enhanced T1w sequences and is largely independent of specific examination protocols.. · The KS diagnostic criteria are in line with the MRI BI-RADS lexicon. We focused on defining a default category to be applied in the case of equivocal imaging criteria.. · The KS reflects increasing probabilities of malignancy and, together with the clinical context, assists individual decision making.. CITATION FORMAT · Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1829-5985.
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Affiliation(s)
- Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Medical University of Vienna, Wien, Austria
| | - Kathrin Barbara Krug
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Köln, Germany
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Kapetas P, Clauser P, Milos RI, Vigano S, Bernathova M, Helbich TH, Baltzer PAT. Microstructural breast tissue characterization: A head-to-head comparison of Diffusion Weighted Imaging and Acoustic Radiation Force Impulse elastography with clinical implications. Eur J Radiol 2021; 143:109926. [PMID: 34438330 DOI: 10.1016/j.ejrad.2021.109926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE Head-to-head comparison of Diffusion Weighted Imaging (DWI) and Acoustic Radiation Force Impulse (ARFI) elastography regarding the characterization of breast lesions in an assessment setting. METHOD Patients undergoing an ultrasound examination including ARFI and an MRI protocol including DWI for the characterization of a BI-RADS 3-5 breast lesion between 06/2013 and 10/2016 were eligible for inclusion in this retrospective, IRB-approved study. 60 patients (30-84 years, median 50) with a median lesion size of 16 mm (range 5-55 mm) were included. The maximum shear wave velocity (SWVmax) and mean apparent diffusion coefficient (ADCmean) for each lesion were retrospectively evaluated by a radiologist experienced in the technique. Histology was the reference standard. Diagnostic performances of ARFI and DWI were assessed using ROC curve analysis. Spearman's rank correlation coefficient and multivariate logistic regression were used to investigate the independence of both tests regarding their diagnostic information to distinguish benign from malignant lesions. RESULTS Corresponding areas under the ROC curve for differentiation of benign (n = 16) and malignant (n = 49) lesions were 0.822 (ARFI) and 0.871 (DWI, p-value = 0.48). SWVmax and ADCmean values showed a significant negative correlation (ρ = -0.501, p-value < 0.001). In multivariate analysis, combination of ARFI and DWI did not improve the results of each single modality, thus no significant independent diagnostic information was present. CONCLUSION Significant correlation between quantitative findings of ARFI and DWI in breast lesions exists. Thus, ARFI provides similar diagnostic information as a DWI-including protocol of an additional "problem-solving" MRI for the characterization of a sonographically evident breast lesion, improving the immediate patient management in the assessment setting.
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Affiliation(s)
- Panagiotis Kapetas
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Paola Clauser
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Ruxandra-Iulia Milos
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Sara Vigano
- Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via della Commenda 10, 20122 Milan, Italy
| | - Maria Bernathova
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Thomas H Helbich
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Pascal A T Baltzer
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Waehringer Guertel 18-20, 1090 Vienna, Austria.
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Dietzel M, Krug B, Clauser P, Burke C, Hellmich M, Maintz D, Uder M, Bickel H, Helbich T, Baltzer PAT. A Multicentric Comparison of Apparent Diffusion Coefficient Mapping and the Kaiser Score in the Assessment of Breast Lesions. Invest Radiol 2021; 56:274-282. [PMID: 33122603 DOI: 10.1097/rli.0000000000000739] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
MATERIALS AND METHODS In this multicentric study, individual patient data from 3 different centers were analyzed. Consecutive patients receiving standardized multiparametric breast magnetic resonance imaging for standard nonscreening indications were included. At each center, 2 experienced radiologists with more than 5 years of experience retrospectively interpreted the examinations in consensus and applied the KS to every histologically verified lesion. The corresponding mean ADC of each lesion was measured using a Wielema type 4 region of interest. According to established methods, the KS and ADC were combined, yielding the KS+ score. Diagnostic accuracy was evaluated by the area under the receiver operating characteristics curve (AUROC) and compared between the KS, ADC, and KS+ (DeLong test). Likewise, the potential to help avoid unnecessary biopsies was compared between the KS, ADC, and KS+ based on established high sensitivity thresholds (McNemar test). RESULTS A total of 450 lesions in 414 patients (mean age, 51.5 years; interquartile range, 42-60.8 years) were included, with 219 lesions being malignant (48.7%; 95% confidence interval [CI], 44%-53.4%). The performance of the KS (AUROC, 0.915; CI, 0.886-0.939) was significantly better than that of the ADC (AUROC, 0.848; CI, 0.811-0.880; P < 0.001). The largest difference between these parameters was observed when assessing subcentimeter lesions (AUROC, 0.909 for KS; CI, 0.849-0.950 vs 0.811 for ADC; CI, 0.737-0.871; P = 0.02).The use of the KS+ (AUROC, 0.918; CI, 0.889-0.942) improved the performance slightly, but without any significant difference relative to a single KS or ADC reading (P = 0.64).When applying high sensitivity thresholds for avoiding unnecessary biopsies, the KS and ADC achieved equal sensitivity (97.7% for both; cutoff values, >4 for KS and ≤1.4 × 10-3 mm2/s for ADC). However, the rate of potentially avoidable biopsies was higher when using the KS (specificity: 65.4% for KS vs 32.9% for ADC; P < 0.0001). The KS was superior to the KS+ in avoiding unnecessary biopsies. CONCLUSIONS Both the KS and ADC may be used to distinguish benign from malignant breast lesions. However, KS proved superior in this task including, most of all, when assessing small lesions less than 1 cm. Using the KS may avoid twice as many unnecessary biopsies, and the combination of both the KS and ADS does not improve diagnostic performance.
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Affiliation(s)
- Matthias Dietzel
- From the Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Barbara Krug
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Paola Clauser
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christina Burke
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Martin Hellmich
- Institute of Medical Statistics and Bioinformatics, University Cologne, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Michael Uder
- From the Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Hubert Bickel
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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Clauser P, Krug B, Bickel H, Dietzel M, Pinker K, Neuhaus VF, Marino MA, Moschetta M, Troiano N, Helbich TH, Baltzer PAT. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy. Clin Cancer Res 2021; 27:1941-1948. [PMID: 33446565 PMCID: PMC8406278 DOI: 10.1158/1078-0432.ccr-20-3037] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies. EXPERIMENTAL DESIGN This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10-3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis. RESULTS There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282). CONCLUSIONS An ADC cutoff of ≥1.5 × 10-3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Krug
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Hubert Bickel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, Friedrich-Alexander-University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor-Frederic Neuhaus
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy
| | - Marco Moschetta
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Nicoletta Troiano
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
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Grippo C, Jagmohan P, Helbich TH, Kapetas P, Clauser P, Baltzer PAT. Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI. Eur J Radiol 2021; 138:109630. [PMID: 33744507 DOI: 10.1016/j.ejrad.2021.109630] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES the Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score. METHODS This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis. RESULTS Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P < 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P < 0.5). CONCLUSIONS Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.
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Affiliation(s)
- Cristina Grippo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Istituto di Radiologia, Fondazione Policlinico Universitario A.Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Pooja Jagmohan
- Department of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University and General Hospital of Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University and General Hospital of Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University and General Hospital of Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University and General Hospital of Vienna, Austria.
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Ellmann S, Wenkel E, Dietzel M, Bielowski C, Vesal S, Maier A, Hammon M, Janka R, Fasching PA, Beckmann MW, Schulz Wendtland R, Uder M, Bäuerle T. Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses. PLoS One 2020; 15:e0228446. [PMID: 31999755 PMCID: PMC6992224 DOI: 10.1371/journal.pone.0228446] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 01/15/2020] [Indexed: 12/16/2022] Open
Abstract
We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81–0.98) with variable diagnostic accuracy (AUC: 0.65–0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies.
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Affiliation(s)
- Stephan Ellmann
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Evelyn Wenkel
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias Dietzel
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christian Bielowski
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sulaiman Vesal
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias Hammon
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rolf Janka
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Peter A. Fasching
- Comprehensive Cancer Center Erlangen-EMW, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Matthias W. Beckmann
- Comprehensive Cancer Center Erlangen-EMW, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rüdiger Schulz Wendtland
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tobias Bäuerle
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Wada T, Yokota H, Horikoshi T, Starkey J, Hattori S, Hashiba J, Uno T. Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach. Jpn J Radiol 2019; 38:207-214. [PMID: 31820265 DOI: 10.1007/s11604-019-00908-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 11/30/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE The purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability. MATERIALS AND METHODS One hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model. RESULTS AUCs of the imaging features from whole-tumor varied between readers (0.50-0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82-0.87) than for one-point (0.66-0.79) in all readers. CONCLUSION Some imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.
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Affiliation(s)
- Takeshi Wada
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan.
| | - Takuro Horikoshi
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan
| | - Jay Starkey
- Department of Radiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR, 97239, USA
| | - Shinya Hattori
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan
| | - Jun Hashiba
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan
| | - Takashi Uno
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan
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Karlsson A, Gonzalez V, Jaraj SJ, Bottai M, Sandelin K, Arver B, Eriksson S. The accuracy of incremental pre-operative breast MRI findings – Concordance with histopathology in the Swedish randomized multicenter POMB trial. Eur J Radiol 2019; 114:185-191. [DOI: 10.1016/j.ejrad.2019.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 02/12/2019] [Accepted: 03/11/2019] [Indexed: 11/28/2022]
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22
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Dietzel M, Wenkel E, Hammon M, Clauser P, Uder M, Schulz-Wendtland R, Baltzer PA. Does higher field strength translate into better diagnostic accuracy? A prospective comparison of breast MRI at 3 and 1.5 Tesla. Eur J Radiol 2019; 114:51-56. [DOI: 10.1016/j.ejrad.2019.02.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 02/22/2019] [Accepted: 02/25/2019] [Indexed: 12/20/2022]
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Taşkın F, Polat Y, Erdoğdu İH, Türkdoğan FT, Öztürk VS, Özbaş S. Problem-solving breast MRI: useful or a source of new problems? ACTA ACUST UNITED AC 2019; 24:255-261. [PMID: 30211678 DOI: 10.5152/dir.2018.17504] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
PURPOSE We aimed to evaluate the findings and results from breast magnetic resonance imaging (MRI) examinations performed for problem-solving purposes due to inconclusive conventional imaging findings. METHODS Imaging findings, biopsy and follow-up results were retrospectively evaluated for breast MRI performed for problem-solving purposes at our department between January 2011 and December 2016 for cases whose mammography, tomosynthesis, or ultrasonography findings were inconclusive. RESULTS Lesions were identified in 414 of 986 problem-solving MRI examinations, and 13.3% of these lesions were diagnosed as malignant. A total of 124 lesions were additionally found by MRI, and 9.7% of these lesions were diagnosed as malignant. MRI produced false-negative results in four cases. In cases whose conventional imaging methods yielded indefinite results, the sensitivity, specificity, negative and positive predictive values of MRI were found to be 96.3%, 83%, 99.3%, and 46.5%, respectively. For the additional lesions identified, the sensitivity, specificity, negative and positive predictive values of MRI were found to be 91.7%, 69%, 98.7%, and 24%, respectively. CONCLUSION Breast MRI is a reliable problem-solving method for excluding malignancy that cannot be confirmed by conventional imaging. In such cases, additional findings from MRI may help identify new cancers that cannot be detected with conventional methods. However, it has moderately low specificity which may cause unnecessary biopsies, follow-ups, and anxiety to patients.
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Affiliation(s)
- Füsun Taşkın
- Department of Radiology Adnan Menderes University School of Medicine, Aydın, Turkey
| | - Yasemin Polat
- Department of Radiology Adnan Menderes University School of Medicine, Aydın, Turkey
| | - İbrahim H Erdoğdu
- Department of Pathology Adnan Menderes University School of Medicine, Aydın, Turkey
| | - Figen T Türkdoğan
- Department of Radiology Adnan Menderes University School of Medicine, Aydın, Turkey
| | - Veli Suha Öztürk
- Department of Radiology Adnan Menderes University School of Medicine, Aydın, Turkey
| | - Serdar Özbaş
- Department of Breast-Endocrine Surgery Güven Hospital Breast Center, Ankara, Turkey
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24
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Girometti R, Nitti A, Lorenzon M, Greco F, Londero V, Zuiani C. Comparison between an abbreviated and full MRI protocol for detecting additional disease when doing breast cancer staging. J Magn Reson Imaging 2018; 49:e222-e230. [DOI: 10.1002/jmri.26339] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/25/2018] [Accepted: 08/27/2018] [Indexed: 01/17/2023] Open
Affiliation(s)
- Rossano Girometti
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
| | - Adriana Nitti
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
| | - Michele Lorenzon
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
| | - Franco Greco
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
| | - Viviana Londero
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
| | - Chiara Zuiani
- Institute of Radiology, Department of MedicineUniversity of Udine – “S. Maria della Misericordia” University Hospital Udine Italy
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Spick C, Szolar DHM, Preidler KW, Reittner P, Rauch K, Brader P, Tillich M, Baltzer PA. 3 Tesla breast MR imaging as a problem-solving tool: Diagnostic performance and incidental lesions. PLoS One 2018; 13:e0190287. [PMID: 29293582 PMCID: PMC5749752 DOI: 10.1371/journal.pone.0190287] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 12/11/2017] [Indexed: 12/01/2022] Open
Abstract
PURPOSE To investigate the diagnostic performance and incidental lesion yield of 3T breast MRI if used as a problem-solving tool. METHODS This retrospective, IRB-approved, cross-sectional, single-center study comprised 302 consecutive women (mean: 50±12 years; range: 20-79 years) who were undergoing 3T breast MRI between 03/2013-12/2014 for further workup of conventional and clinical breast findings. Images were read by experienced, board-certified radiologists. The reference standard was histopathology or follow-up ≥ two years. Sensitivity, specificity, PPV, and NPV were calculated. Results were stratified by conventional and clinical breast findings. RESULTS The reference standard revealed 53 true-positive, 243 true-negative, 20 false-positive, and two false-negative breast MRI findings, resulting in a sensitivity, specificity, PPV, and NPV of 96.4% (53/55), 92.4% (243/263), 72.6% (53/73), and 99.2% (243/245), respectively. In 5.3% (16/302) of all patients, incidental MRI lesions classified BI-RADS 3-5 were detected, 37.5% (6/16) of which were malignant. Breast composition and the imaging findings that had led to referral had no significant influence on the diagnostic performance of breast MR imaging (p>0.05). CONCLUSION 3T breast MRI yields excellent diagnostic results if used as a problem-solving tool independent of referral reasons. The number of suspicious incidental lesions detected by MRI is low, but is associated with a substantial malignancy rate.
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Affiliation(s)
- Claudio Spick
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna (AKH), Vienna, Austria
| | | | | | | | | | | | | | - Pascal A. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna (AKH), Vienna, Austria
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Bennani-Baiti B, Dietzel M, Baltzer PA. MRI for the assessment of malignancy in BI-RADS 4 mammographic microcalcifications. PLoS One 2017; 12:e0188679. [PMID: 29190656 PMCID: PMC5708819 DOI: 10.1371/journal.pone.0188679] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 11/11/2017] [Indexed: 12/11/2022] Open
Abstract
Purpose Assess the performance of breast MRI to diagnose breast cancer in BI-RADS 4 microcalcifications detected by mammography. Materials and methods This retrospective, IRB-approved study included 248 consecutive contrast-enhanced breast MRI (1.5T, protocol in accordance with EUSOBI recommendations) performed to further diagnose BI-RADS 4 microcalcifications detected at mammography during a 3-year period. Standard of reference had to be established by histopathology. Routine consensus reading results by two radiologists were dichotomized as positive or negative and compared with the reference standard (benign vs malignant) to calculate diagnostic parameters. Results There were 107 malignant and 141 benign microcalcifications. Malignancy rates were 18.3% (23/126 BI-RADS 4a), 41.7% (25/60 BI-RADS 4b) and 95% (59/62 BI-RADS 4c). There were 103 true-positive, 116 true-negative, 25 false-positive, and 4 false-negative (one invasive cancer, three DCIS; 2 BI-RADS 4c, 1 BI-RADS 4b on mammography) breast MRI findings, effecting a sensitivity, specificity, PPV, and NPV of 96.3% (95%-CI 90.7–99.0%), 82.3% (95%-CI 75.0–88.2%), 80.5% (95%-CI 72.5–87.0%) and 96.7% (95%-CI 91.7–99.1%), respectively. Conclusion MRI is an accurate tool to further diagnose BI-RADS 4a and 4b microcalcifications and may be helpful to avoid unnecessary biopsies in BI-RADS 4a and 4b lesions. BI-RADS 4c microcalcifications should be biopsied irrespective of MRI findings.
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Affiliation(s)
- Barbara Bennani-Baiti
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital (AKH), Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, University of Erlangen-Nürnberg, Nürnberg, Germany
| | - Pascal A Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Vienna General Hospital (AKH), Medical University of Vienna, Vienna, Austria
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Baltzer PAT, Kapetas P, Marino MA, Clauser P. New diagnostic tools for breast cancer. MEMO-MAGAZINE OF EUROPEAN MEDICAL ONCOLOGY 2017; 10:175-180. [PMID: 28989543 PMCID: PMC5605595 DOI: 10.1007/s12254-017-0341-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 06/13/2017] [Indexed: 12/21/2022]
Abstract
Imaging plays a major role in the diagnosis, treatment, and follow-up of breast cancer. Findings that require further assessment will be detected both at screening and curative mammography. Most findings that are further worked up tend to yield benign diagnoses. Consequently, there is an ongoing search for new tools to reduce recalls and unnecessary biopsies while maintaining or improving cancer detection rates. The clinically most promising methods in this respect are described and discussed in this review.
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Affiliation(s)
- Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Maria Adele Marino
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
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Saha A, Grimm LJ, Harowicz M, Ghate SV, Kim C, Walsh R, Mazurowski MA. Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics. Med Phys 2017; 43:4558. [PMID: 27487872 DOI: 10.1118/1.4955435] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI. METHODS Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers' annotations. The segmented tumors were then compared between readers using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson's correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement. RESULTS The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96. CONCLUSIONS There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.
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Affiliation(s)
- Ashirbani Saha
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Michael Harowicz
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Sujata V Ghate
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Connie Kim
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Ruth Walsh
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705
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Lallemand M, Barron M, Bingham J, Mosier A, Hardin M, Sohn V. The true impact of breast magnetic resonance imaging on the management of in situ disease: more is not better. Am J Surg 2016; 213:127-131. [PMID: 27842732 DOI: 10.1016/j.amjsurg.2016.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 05/04/2016] [Accepted: 05/16/2016] [Indexed: 12/28/2022]
Abstract
BACKGROUND The optimal role of breast magnetic resonance imaging (MRI) in the management of ductal carcinoma in situ (DCIS) remains controversial. We sought to better define the impact of breast MRIs when utilized during the workup of DCIS. METHODS Patients with biopsy-proven DCIS without any additional invasive disease were prospectively enrolled in the multidisciplinary breast cancer pathway and comprised the study group. Patients who met any additional criteria for MRI screening were excluded. RESULTS From 2008 to 2014, 93 women met the inclusion criteria. 81 patients underwent MRI as part of their workup. One patient benefited from MRI via identification of occult malignancy not previously identified. 35 MRIs identified no additional information whereas 46 had additional findings. These findings led to 23 procedures and 16 negative biopsies; recommendations for 16 radiographic studies that were normal; and influenced nodal sampling in 7 women with 1 positive metastatic focus. CONCLUSIONS The routine use of breast MRI for women diagnosed with DCIS has limited benefit. Often, it leads to multiple procedures and studies that are clinically insignificant and delays surgical treatment.
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Affiliation(s)
- Michael Lallemand
- Department of General Surgery, Madigan Army Medical Center, 9040 Fitzsimmons Dr., Joint Base Lewis-McChord, Tacoma, WA 98431, USA.
| | - Morgan Barron
- Department of General Surgery, Madigan Army Medical Center, 9040 Fitzsimmons Dr., Joint Base Lewis-McChord, Tacoma, WA 98431, USA
| | - Jason Bingham
- Department of General Surgery, Madigan Army Medical Center, 9040 Fitzsimmons Dr., Joint Base Lewis-McChord, Tacoma, WA 98431, USA
| | - Andrew Mosier
- Department of Radiology, Madigan Army Medical Center, Tacoma, WA, USA
| | - Mark Hardin
- Department of General Surgery, Madigan Army Medical Center, 9040 Fitzsimmons Dr., Joint Base Lewis-McChord, Tacoma, WA 98431, USA
| | - Vance Sohn
- Department of General Surgery, Madigan Army Medical Center, 9040 Fitzsimmons Dr., Joint Base Lewis-McChord, Tacoma, WA 98431, USA
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De Decker S, Gomes SA, Packer RMA, Kenny PJ, Beltran E, Parzefall B, Fenn J, Nair D, Nye G, Volk HA. EVALUATION OF MAGNETIC RESONANCE IMAGING GUIDELINES FOR DIFFERENTIATION BETWEEN THORACOLUMBAR INTERVERTEBRAL DISK EXTRUSIONS AND INTERVERTEBRAL DISK PROTRUSIONS IN DOGS. Vet Radiol Ultrasound 2016; 57:526-33. [DOI: 10.1111/vru.12394] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 04/04/2016] [Accepted: 05/25/2016] [Indexed: 11/30/2022] Open
Affiliation(s)
- Steven De Decker
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Sergio A. Gomes
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Rowena MA Packer
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Patrick J. Kenny
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Elsa Beltran
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Birgit Parzefall
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Joe Fenn
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Devi Nair
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - George Nye
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
| | - Holger A. Volk
- Department of Veterinary Clinical Science and Services; Royal Veterinary College; University of London; Hawkshead lane, AL9 7TA North Mymms Hatfield England
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BIRADS 3 MRI lesions: Was the initial score appropriate and what is the value of the blooming sign as an additional parameter to better characterize these lesions? Eur J Radiol 2016; 85:337-45. [DOI: 10.1016/j.ejrad.2015.11.032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 11/20/2015] [Accepted: 11/25/2015] [Indexed: 11/19/2022]
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Marino MA, Clauser P, Woitek R, Wengert GJ, Kapetas P, Bernathova M, Pinker-Domenig K, Helbich TH, Preidler K, Baltzer PAT. A simple scoring system for breast MRI interpretation: does it compensate for reader experience? Eur Radiol 2015; 26:2529-37. [PMID: 26511631 DOI: 10.1007/s00330-015-4075-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 10/12/2015] [Accepted: 10/16/2015] [Indexed: 12/26/2022]
Abstract
PURPOSE To investigate the impact of a scoring system (Tree) on inter-reader agreement and diagnostic performance in breast MRI reading. MATERIALS AND METHODS This IRB-approved, single-centre study included 100 patients with 121 consecutive histopathologically verified lesions (52 malignant, 68 benign). Four breast radiologists with different levels of MRI experience and blinded to histopathology retrospectively evaluated all examinations. Readers independently applied two methods to classify breast lesions: BI-RADS and Tree. BI-RADS provides a reporting lexicon that is empirically translated into likelihoods of malignancy; Tree is a scoring system that results in a diagnostic category. Readings were compared by ROC analysis and kappa statistics. RESULTS Inter-reader agreement was substantial to almost perfect (kappa: 0.643-0.896) for Tree and moderate (kappa: 0.455-0.657) for BI-RADS. Diagnostic performance using Tree (AUC: 0.889-0.943) was similar to BI-RADS (AUC: 0.872-0.953). Less experienced radiologists achieved AUC: improvements up to 4.7 % using Tree (P-values: 0.042-0.698); an expert's performance did not change (P = 0.526). The least experienced reader improved in specificity using Tree (16 %, P = 0.001). No further sensitivity and specificity differences were found (P > 0.1). CONCLUSION The Tree scoring system improves inter-reader agreement and achieves a diagnostic performance similar to that of BI-RADS. Less experienced radiologists, in particular, benefit from Tree. KEY POINTS • The Tree scoring system shows high diagnostic accuracy in mass and non-mass lesions. • The Tree scoring system reduces inter-reader variability related to reader experience. • The Tree scoring system improves diagnostic accuracy in non-expert readers.
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Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria.,Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria.,Department of Medical and Biological Sciences, Institute of Diagnostic Radiology, Azienda Ospedaliero-Universitaria, "S. Maria della Misericordia", P.le Santa Maria della Misericordia, University of Udine, Udine, Italy
| | - Ramona Woitek
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Georg J Wengert
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker-Domenig
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria
| | | | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna General Hospital, Floor 7F Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Clinical utility of 18F-FDG-PET/MR for preoperative breast cancer staging. Eur Radiol 2015; 26:2297-307. [DOI: 10.1007/s00330-015-4054-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 09/28/2015] [Accepted: 10/01/2015] [Indexed: 02/08/2023]
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