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Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy

Paola Clauser, B. Krug, Hubert Bickel, Matthias Dietzel, Katja Pinker, Victor Neuhaus, Maria Adele Marino, Marco Moschetta, Nicoletta Troiano, Thomas H. Helbich, Pascal Baltzer

2021Clinical Cancer Research93 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

MedicineCutoffEffective diffusion coefficientRadiologyBreast MRIBiopsyBI-RADSMagnetic resonance imagingDiffusion MRINuclear medicineBreast cancerBreast imagingLesionMammographyCancerPathologyInternal medicineQuantum mechanicsPhysicsMRI in cancer diagnosisBreast Cancer Treatment StudiesRadiomics and Machine Learning in Medical Imaging
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