Abstract
BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.
Highlights
Breast cancer is increasingly acknowledged as a serious, worldwide public concern in women [1, 2]
We examined the discriminative ability of magnetic resonance imaging (MRI)-based texture analysis (TA) by combining five different extracted MRI-based texture feature datasets with a supervised pattern recognition technique to establish five Linear discriminant analysis (LDA)-based models
A combination of gradient boosting decision tree (GBDT) selection method for TA and LDA algorithm for classification exhibited a better performance by statistics among the others
Summary
Breast cancer is increasingly acknowledged as a serious, worldwide public concern in women [1, 2]. Several researchers have reported the incidence of breast cancer increases with age [3]. This malignant and complex lesion, with a spectrum of its different subtypes, has resulted in various treatment modality, followed with heterogeneous responses and clinical outcomes [4]. A considerable problem with this kind of application is that there is hardly any competent method to separate the MRI patterns of benign breast lesions from the patterns of malignant breast lesions due to modest specificity, which usually leads to over- or undertreatment and unnecessary biopsy [9, 10]. Differential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem
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