Roberta Fusco1, Vincenza Granata2, Paolo Pariante1, Vincenzo Cerciello3, Claudio Siani4, Maurizio Di Bonito5, Marika Valentino6, Mario Sansone6, Gerardo Botti7, Antonella Petrillo1. 1. Radiology Division, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy. 2. Radiology Division, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy. Electronic address: v.granata@istitutotumori.na.it. 3. Health Physics Unit, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy. 4. Senology Surgical Division, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy. 5. Pathology Division, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy. 6. Department, Electrical Engineering and Information Technologies, UNIVERSITA' DEGLI STUDI DI NAPOLI FEDERICO II, Naples, Italy. 7. Scientific Director, ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Naples, Italy.
Abstract
PURPOSE: The purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions. METHODS: Fifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S0 and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered. RESULTS: A significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%. CONCLUSIONS: Good performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.
PURPOSE: The purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions. METHODS: Fifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S0 and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered. RESULTS: A significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%. CONCLUSIONS: Good performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.
Authors: Vincenza Granata; Roberta Grassi; Roberta Fusco; Andrea Belli; Carmen Cutolo; Silvia Pradella; Giulia Grazzini; Michelearcangelo La Porta; Maria Chiara Brunese; Federica De Muzio; Alessandro Ottaiano; Antonio Avallone; Francesco Izzo; Antonella Petrillo Journal: Infect Agent Cancer Date: 2021-07-19 Impact factor: 2.965