Open AccessBreast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods
Author(s) -
Alexandros Vamvakas,
Dimitra Tsivaka,
Andreas Logothetis,
Katerina Vassiou,
Ioannis Tsougos
Publication year - 2022
Publication title -
technology in cancer research and treatment
Language(s) - English
Resource type - Journals
eISSN - 1533-0346
pISSN - 1533-0338
DOI - 10.1177/15330338221087828
Subject(s) - adaboost , artificial intelligence , boosting (machine learning) , pattern recognition (psychology) , support vector machine , receiver operating characteristic , gradient boosting , histogram , computer science , magnetic resonance imaging , breast mri , mathematics , random forest , classifier (uml) , mammography , breast cancer , medicine , machine learning , radiology , cancer , image (mathematics)
Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating benign and malignant breast lesions. Methods: The dataset includes mpMR images of 140 female patients with mass-like breast lesions (70 benign and 70 malignant), consisting of Dynamic Contrast Enhanced (DCE) and T2-weighted sequences, and the Apparent Diffusion Coefficient (ADC) calculated from the Diffusion Weighted Imaging (DWI) sequence. Tumor masks were manually defined in all consecutive slices of the respective MRI volumes and 3D radiomic features were extracted with the Pyradiomics package. Feature dimensionality reduction was based on statistical tests and the Boruta wrapper. Hierarchical Clustering on Spearman's rank correlation coefficients between features and Random Forest classification for obtaining feature importance, were implemented for selecting the final feature subset. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) classifiers, were trained and tested with bootstrap validation in differentiating breast lesions. A Support Vector Machine (SVM) classifier was also exploited for comparison. The Receiver Operator Characteristic (ROC) curves and DeLong's test were utilized to evaluate the classification performances. Results: The final feature subset consisted of 5 features derived from the lesion shape and the first order histogram of DCE and ADC images volumes. XGboost and LGBM achieved statistically significantly higher average classification performances [AUC = 0.95 and 0.94 respectively], followed by Adaboost [AUC = 0.90], GB [AUC = 0.89] and SVM [AUC = 0.88]. Conclusion: Overall, the integration of Ensemble Learning methods within mpMRI radiomic analysis can improve the performance of computer-assisted diagnosis of breast cancer lesions.