Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.
Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Me...
| Publicado en: | Current Oncology Vol. 29; no. 3; pp. 1947 - 1967 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156002895&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156002895 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11980052 5EKK jtl: Current Oncology issn: 11980052 maglogo: N pubinfo: dt: Mar2022 vid: 29 iid: 3 pid: 97109 pub: MDPI artinfo: ui: 156002895 156002895 156002895 10.3390/curroncol29030159 156002895 ppf: 1947 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions. aug: au: Fusco, Roberta Di Bernardo, Elio Piccirillo, Adele Rubulotta, Maria Rosaria Petrosino, Teresa Barretta, Maria Luisa Mattace Raso, Mauro Vallone, Paolo Raiano, Concetta Di Giacomo, Raimondo Siani, Claudio Avino, Franca Scognamiglio, Giosuè Di Bonito, Maurizio Granata, Vincenza Petrillo, Antonella affil: Medical Oncolody Division, Igea SpA, 80013 Naples, Italy sug: subj: Breast Neoplasms Diagnosis Artificial Intelligence Magnetic Resonance Imaging Methods Mammography Methods Human Histocytochemistry Machine Learning Sensitivity and Specificity Retrospective Design ROC Curve Discriminant Analysis Descriptive Statistics ab: Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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