Publications by authors named "R Galatola"

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.

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The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, = 19), heterogeneous (Group 2, = 23), and no (Group 3, = 30) signal drop.

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Purpose: To perform anintra-patient comparison betweena single-pass protocol (SP) and a portal venous phase (PVP) by means ofboth quantitative and qualitative analysis of image quality.

Methods: Forty patients (31 M; 9F; aged 20-77 years; BMI 23 ± 4 Kg/m) underwent both a SP and a PVP using a 64-rows multi-detector CT with a median interval time of 56 days (range5-903). All patients underwent i.

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Purpose: To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.

Method: 55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction.

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Objective: The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol.

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