Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images

European Radiology Experimental
Eduardo Fleury, Karem D Marcomini

Abstract

The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667-0.9762), with 71.4% sensitivity (95% CI 0.6479-0.8616) and 76.9% specificity (95% CI 0.6148-0.8228). The best AUC for each method was 0.744 (95% CI 0.677-0.774) for DT, 0.818 (95% ...Continue Reading

References

Mar 5, 2003·Radiology·Nico KarssemeijerRoland Holland
Feb 5, 2005·Breast Cancer Research and Treatment·Ruey-Feng ChangDar-Ren Chen
Mar 25, 2005·AJR. American Journal of Roentgenology·Andrea S HongJay A Baker
Jul 31, 2007·Academic Radiology·Wei-Chih ShenChiun-Sheng Huang
Sep 4, 2012·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Wen-Jie WuWoo Kyung Moon
Nov 17, 2012·Clinical Imaging·Afsaneh JalalianBabak Karasfi
May 4, 2013·Computer Methods and Programs in Biomedicine·Woo Kyung MoonRuey-Feng Chang
Aug 19, 2015·Scientific Reports·Chintan ParmarHugo J W L Aerts
Mar 28, 2017·Ultrasonics·Woo Kyung MoonRuey-Feng Chang
Aug 2, 2017·Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine·Eun ChoJung Hyun Yoon

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Methods Mentioned

BETA
biopsy
feature extraction

Software Mentioned

WEKA Package
Smart Detect
MATLAB
Med Calc

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