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

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Diagnostic value of apparent diffusion coefficient lesion texture biomarkers in breast MRI
Συγγραφέας:	[EL] Τσαρούχη, Μαριαλένα[EN] Tsarouchi, Marialena [EL] Βλαχόπουλος, Γεώργιος[EN] Vlachopoulos, Georgios [EL] Καραχάλιου, Άννα[EN] Karahaliou, Anna [EL] Κωσταρίδου, Ελένη[EN] Costaridou, Lena
Ημερομηνία:	31/07/2020
Περίληψη:	Quantitative assessment of breast intra-lesion heterogeneity in terms of contrast agent free Magnetic Resonance Imaging (MRI) approaches hold potential in breast cancer diagnosis. This study focuses on an Apparent Diffusion Coefficient (ADC) based approach, investigating the diagnostic role of 1st and 2nd order ADC statistics features, in differentiating benign from malignant breast lesion status. A total of 67 patients with 78 histologically verified breast lesions (40 benign and 38 malignant) was analyzed. ADC maps were generated for a slice representative of lesion largest diameter, considering intra Diffusion Weighted Imaging (DWI) sequence non rigid registration scheme. Lesion segments were defined by semi-automated Fuzzy C-Means (FCM) segmentation on high b-value diffusion images and propagated on ADC maps. 27 (11 1st order statistics and 16 2nd order statistics (texture) features were derived. To avoid overfitting a stepwise feature selection method was employed, while the discriminating ability of features was evaluated with univariate and multivariate Logistic Regression classification. The classification performance of the diagnostic model was evaluated by means of the Area Under Receiver Operating Characteristic curve (Az index). A combination of two features, one from 1st order statistics (25th Percentile) and one from 2nd order statistics, (texture Entropy), achieved high classification performance (Az = 0.965 ± 0.024), suggesting both the diagnostic significance of 1st order statistics and texture biomarkers of the ADC map representation
Γλώσσα:	Αγγλικά
Σελίδες:	10
DOI:	10.1007/s12553-020-00452-3
ISSN:	2190-7188
Θεματική κατηγορία:	[EL] Ραδιολογία, Πυρηνική ιατρική και Απεικονιστική[EN] Radiology, Nuclear Medicine and Imaging [EL] Ιατρική και Επιστήμες Υγείας[EN] Medical and Health Sciences
Λέξεις-κλειδιά:	ADC map; Breast Cancer Diagnosis; DWI; Imaging lesion heterogeneity; Texture Analysis
Κάτοχος πνευματικών δικαιωμάτων:	© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://link.springer.com/article/10.1007%2Fs12553-020-00452-3
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.springer.com/journal/12553?IFA
Τίτλος πηγής δημοσίευσης:	Journal of Health and Technology
Τεύχος:	Issue 4
Τόμος:	Volume 10
Σελίδες τεκμηρίου (στην πηγή):	969–978
Σημειώσεις:	Support by Operational Program “Human Resources Development, Education and Lifelong Learning” and is co-financed by the European Union (European Social Fund) and Greek national funds (MIS: 5005772).
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες