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Eur J Radiol. 2017 Mar;88:141-147. doi: 10.1016/j.ejrad.2017.01.006. Epub 2017 Jan 8.

3D quantitative breast ultrasound analysis for differentiating fibroadenomas and carcinomas smaller than 1cm.

Author information

1
Department of Biomechanical Engineering, MIRA-Institute, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands; Medical UltraSound Imaging Center (MUSIC), department of Radiology and Nuclear Medicine, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands. Electronic address: aisha.vandenabeelen@radboudumc.nl.
2
Medical UltraSound Imaging Center (MUSIC), department of Radiology and Nuclear Medicine, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
3
Radboud University Nijmegen Medical Centre, Department of Radiology and Nuclear Medicine, PO Box 9101, 6500 HB Nijmegen, The Netherlands.

Abstract

PURPOSE:

In (3D) ultrasound, accurate discrimination of small solid masses is difficult, resulting in a high frequency of biopsies for benign lesions. In this study, we investigate whether 3D quantitative breast ultrasound (3DQBUS) analysis can be used for improving non-invasive discrimination between benign and malignant lesions.

METHODS AND MATERIALS:

3D US studies of 112 biopsied solid breast lesions (size <1cm), were included (34 fibroadenomas and 78 invasive ductal carcinomas). The lesions were manually delineated and, based on sonographic criteria used by radiologists, 3 regions of interest were defined in 3D for analysis: ROI (ellipsoid covering the inside of the lesion), PER (peritumoural surrounding: 0.5mm around the lesion), and POS (posterior-tumoural acoustic phenomena: region below the lesion with the same size as delineated for the lesion). After automatic gain correction (AGC), the mean and standard deviation of the echo level within the regions were calculated. For the ROI and POS also the residual attenuation coefficient was estimated in decibel per cm [dB/cm]. The resulting eight features were used for classification of the lesions by a logistic regression analysis. The classification accuracy was evaluated by leave-one-out cross-validation. Receiver operating characteristic (ROC) curves were constructed to assess the performance of the classification. All lesions were delineated by two readers and results were compared to assess the effect of the manual delineation.

RESULTS:

The area under the ROC curve was 0.86 for both readers. At 100% sensitivity, a specificity of 26% and 50% was achieved for reader 1 and 2, respectively. Inter-reader variability in lesion delineation was marginal and did not affect the accuracy of the technique. The area under the ROC curve of 0.86 was reached for the second reader when the results of the first reader were used as training set yielding a sensitivity of 100% and a specificity of 40%. Consequently, 3DQBUS would have achieved a 40% reduction in biopsies for benign lesions for reader 2, without a decrease in sensitivity.

CONCLUSION:

This study shows that 3DQBUS is a promising technique to classify suspicious breast lesions as benign, potentially preventing unnecessary biopsies.

KEYWORDS:

Biopsies; Breast cancer; Diagnosis; Quantitative analysis; Ultrasound

PMID:
28189199
DOI:
10.1016/j.ejrad.2017.01.006
[Indexed for MEDLINE]

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