Supplemental screening using breast MRI in women with mammographically dense breasts

Baltzer, Pascal

doi:10.1016/j.ejrad.2020.109513

Cited by 12 publications

(6 citation statements)

References 43 publications

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“…Thus, lesions with a rating < 3 were considered benign and lesions with a rating ≥ 3 were considered malignant KS as a decision algorithm, seemingly does not require expert training. This is important considering the increasing demand on MRIb examinations because of the medical and economic evidence in favor of extended MRIb indications [3,[31][32][33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Breast MRI: does a clinical decision algorithm outweigh reader experience?

et al. 2022

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Objectives Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. Methods Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. Results A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). Conclusion The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. Key Points • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance.

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Section: Discussionmentioning

confidence: 99%

Breast MRI: does a clinical decision algorithm outweigh reader experience?

et al. 2022

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“…Even though the method was long overshadowed by X-ray mammography as an easy, cost-efficient, and effective early detection method, it has become an indispensable part of most areas of breast diagnostics. Recent studies have confirmed the method's long suspected added value in the early detection of breast cancer in women with dense breast tissue [5][6][7][8]. X-ray mammography is not sufficiently sensitive in these women and the additional benefit of tomosynthesis, ultrasound, and other methods is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Stand sie lange auch aus politischen Gründen im Schatten der Röntgen-Mammografie als einfacher, kostengünstiger und effektiver Früherkennungsmethode, ist sie heute aus den meisten Anwendungsfeldern der Mammadiagnostik nicht mehr wegzudenken. Neueste Arbeiten haben den lange vermuteten Mehrwert in der Brustkrebs-Früherkennung bei Frauen mit dichter Brust bestätigt [5][6][7][8]. Bei diesen ist die Röntgen-Mammografie unzureichend sensitiv, der zusätzliche Nutzen von Tomosynthese, Ultraschall und anderen Verfahren limitiert.…”

Section: Introductionunclassified

Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score

Baltzer

Krug

Dietzel

2022

Rofo

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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: Citation Format

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“…Die MRT der Brust, auch MR-Mammografie (MRM), gilt als das sensitivste Verfahren zum Nachweis von Brustkrebs 1 . Bei sachgemäßer Anwendung ist die MRM hochspezifisch und kann im Fall unklarer konventioneller Befunde herangezogen werden.…”

Section: Introductionunclassified

“…Die hohe internationale Akzeptanz der MRM spiegelt sich in den Ergebnissen einer Umfrage der European Society of Breast Imaging (EUSOBI) wider 4 . Zunehmendes Interesse erfährt die MRM infolge vielversprechender Daten für die Anwendung im Screening von Patientinnen mit stark erhöhter Brustdichte 1 .…”

Section: Introductionunclassified