1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Breast cancer classification on multiparametric MRI : increased performance of boosting ensemble methods
Συγγραφέας:	[EL] Βαμβακάς, Αλέξανδρος-Χρυσοβαλάντης[EN] Vamvakas, Alexandros-Chrysovalantis [EL] Τσιβάκα, Δήμητρα[EN] Tsivaka, Dimitra [EL] Λογοθέτης, Ανδρέας[EN] Logothetis, Andreas [EL] Βάσιου, Αικατερίνη[EN] Vassiou, Katerina [EL] Τσούγκος, Ιωάννης[EN] Tsougos, Ioannis
Ημερομηνία:	28/03/2022
Περίληψη:	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.
Γλώσσα:	Αγγλικά
Σελίδες:	12
DOI:	10.1177/15330338221087828
EISSN:	1533-0338
Θεματική κατηγορία:	[EL] Ιατρική, άλλοι τομείς[EN] Medicine, miscellaneous
Λέξεις-κλειδιά:	AdaBoost; Boruta; Gradient Boosting; LightGBM; Radiomics; XGBoost
Κάτοχος πνευματικών δικαιωμάτων:	© The Author(s) 2022
Όροι και προϋποθέσεις δικαιωμάτων:	Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://journals.sagepub.com/doi/full/10.1177/15330338221087828
Ηλεκτρονική διεύθυνση περιοδικού:	https://journals.sagepub.com/home/tct
Τίτλος πηγής δημοσίευσης:	Technology in Cancer Research & Treatment
Τόμος:	21
Σελίδες τεκμηρίου (στην πηγή):	1-12
Σημειώσεις:	The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme (Human Resources Development, Education and Lifelong Learning 2014-2020) in the context of the project “Breast cancer assessment through advanced multiparametric imaging techniques and development of differential diagnosis software using artificial intelligence systems” [MIS5048948].
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες