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Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database

Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database


Titill: Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database
Höfundur: Bickel, Hubert
Clauser, Paola
Pinker, Katja
Helbich, Thomas
Biondic, Iva
Brkljacic, Boris
Dietzel, Matthias
Ivanac, Gordana
Krug, Barbara
Moschetta, Marco
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Útgáfa: 2023-05-11
Tungumál: Enska
Umfang: 11
Deild: Faculty of Medicine
Birtist í: European Radiology; 33(8)
ISSN: 0938-7994
DOI: 10.1007/s00330-023-09675-0
Efnisorð: Breast neoplasms; Classification; Diffusion magnetic resonance imaging; Diagnosis, Differential; Breast Neoplasms/pathology; Humans; Middle Aged; Breast/diagnostic imaging; Magnetic Resonance Imaging; Contrast Media; Sensitivity and Specificity; Diffusion Magnetic Resonance Imaging; Female; Retrospective Studies; Radiology, Nuclear Medicine and Imaging
URI: https://hdl.handle.net/20.500.11815/4830

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Tilvitnun:

Bickel , H , Clauser , P , Pinker , K , Helbich , T , Biondic , I , Brkljacic , B , Dietzel , M , Ivanac , G , Krug , B , Moschetta , M , Neuhaus , V , Preidler , K & Baltzer , P 2023 , ' Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database ' , European Radiology , vol. 33 , no. 8 , pp. 5400-5410 . https://doi.org/10.1007/s00330-023-09675-0

Útdráttur:

Objectives: To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. Methods: This was a multicentric, retrospective analysis of 11 independently conducted institutional review board–approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. Results: A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10−3 mm2/s) differed significantly between benign (1.45, SD.40) and malignant lesions (.95, SD.39), and between invasive (.92, SD.22) and in situ carcinomas (1.18, SD.30) (p <.001). The following ADC-B categories were identified: ADC-B0—ADC cannot be assessed; ADC-B1—no contrast-enhancing lesion; ADC-B2—ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3—ADC 1.5 to < 1.9 (0.1–1.7%); ADC-B4—ADC 1.0 to < 1.5 (10–24.5%); and ADC-B5—ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94–0.97) for invasive versus non-invasive breast carcinomas was reached. Conclusions: The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. Clinical relevance statement: The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. Key Points: • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.

Athugasemdir:

Funding Information: The authors would like to thank Joanne Chin, MFA, ELS, for manuscript editing. Publisher Copyright: © 2023, The Author(s).

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