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Your search keyword '"Cutaia G."' showing total 4 results
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4 results on '"Cutaia G."'

1. Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications

Author

Sabrina Maria Milo, Patrizia Toia, Federico Midiri, Luigi D’Alessandro, Giulia Sollami, Aldo Panci, Vincenzo Pernice, Francesco Violante, Khalil Fattouch, Giuseppe Cutaia, Giuseppe Salvaggio, Giuseppe Lo Re, Emanuele Grassedonio, Massimo Galia, Ludovico La Grutta, Milo S.M., Toia P., Midiri F., D'Alessandro L., Sollami G., Panci A., Pernice V., Violante F., Fattouch K., Cutaia G., Salvaggio G., Lo Re G., Grassedonio E., Galia M., and La Grutta L.

Subjects
Transcatheter aortic valve implantation, Radiology, Nuclear Medicine and imaging, General Medicine, Computed tomography angiography, Settore MED/36 - Diagnostica Per Immagini E Radioterapia, Complication, Calcium score
Abstract

Purpose The aim of our study was to evaluate the prevalence of early complications after Transcatheter Aortic Valve Implantation (TAVI) and their correlation with the Calcium Score (CS) of the aortic valve, aorta and ilio-femoral arteries derived from pre-procedural computed tomography (CT). Materials and methods We retrospectively reviewed 226 patients (100 males, mean age 79.4 ± 6.7 years) undergoing 64-slice CT for pre-TAVI evaluation from January 2018 to April 2021. The population was divided into CS quartiles. Results Overall, 173 patients underwent TAVI procedure, of whom 61% presented paravalvular leak after the procedure, 28% presented bleeding or vascular complications, 25% presented atrioventricular block, and 8% developed acute kidney injury. The prevalence of paravalvular leak and vascular complications was higher in the upper CS quartiles for aortic valve and ilio-femoral arteries. Conclusions Aortic valve and vascular CS could help to predict post-TAVI early complications.

Published
2023

2. Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography

Author

Giuseppe Salvaggio, Giuseppe Cutaia, Antonio Greco, Mario Pace, Leonardo Salvaggio, Federica Vernuccio, Roberto Cannella, Laura Algeri, Lorena Incorvaia, Alessandro Stefano, Massimo Galia, Giuseppe Badalamenti, Albert Comelli, Salvaggio G., Cutaia G., Greco A., Pace M., Salvaggio L., Vernuccio F., Cannella R., Algeri L., Incorvaia L., Stefano A., Galia M., Badalamenti G., and Comelli A.

Subjects
Fluid Flow and Transfer Processes, Technology, Artificial intelligence, Soft tissue sarcoma, QH301-705.5, Process Chemistry and Technology, Physics, QC1-999, General Engineering, Deep learning, Engineering (General). Civil engineering (General), Computer Science Applications, Chemistry, Segmentation, Volume estimation, General Materials Science, soft tissue sarcoma, volume estimation, segmentation, artificial intelligence, TA1-2040, Biology (General), Instrumentation, QD1-999
Abstract

The volume estimation of retroperitoneal sarcoma (RPS) is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. This study aimed to evaluate two fully automated deep learning networks (ENet and ERFNet) for RPS segmentation. This retrospective study included 20 patients with RPS who received an abdominal computed tomography (CT) examination. Forty-nine CT examinations, with a total of 72 lesions, were included. Manual segmentation was performed by two radiologists in consensus, and automatic segmentation was performed using ENet and ERFNet. Significant differences between manual and automatic segmentation were tested using the analysis of variance (ANOVA). A set of performance indicators for the shape comparison (namely sensitivity), positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric differences (VD) were calculated. There were no significant differences found between the RPS volumes obtained using manual segmentation and ENet (p-value = 0.935), manual segmentation and ERFNet (p-value = 0.544), or ENet and ERFNet (p-value = 0.119). The sensitivity, PPV, DSC, VOE, and VD for ENet and ERFNet were 91.54% and 72.21%, 89.85% and 87.00%, 90.52% and 74.85%, 16.87% and 36.85%, and 2.11% and −14.80%, respectively. By using a dedicated GPU, ENet took around 15 s for segmentation versus 13 s for ERFNet. In the case of CPU, ENet took around 2 min versus 1 min for ERFNet. The manual approach required approximately one hour per segmentation. In conclusion, fully automatic deep learning networks are reliable methods for RPS volume assessment. ENet performs better than ERFNet for automatic segmentation, though it requires more time.

Published
2022
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3. Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications.

Author

Milo SM, Toia P, Midiri F, D'Alessandro L, Sollami G, Panci A, Pernice V, Violante F, Fattouch K, Cutaia G, Salvaggio G, Lo Re G, Grassedonio E, Galia M, and La Grutta L

Subjects
Male, Humans, Aged, Aged, 80 and over, Aortic Valve diagnostic imaging, Aortic Valve surgery, Calcium, Retrospective Studies, Tomography, X-Ray Computed, Treatment Outcome, Transcatheter Aortic Valve Replacement adverse effects, Transcatheter Aortic Valve Replacement methods, Aortic Valve Stenosis diagnostic imaging, Aortic Valve Stenosis surgery
Abstract

Purpose: The aim of our study was to evaluate the prevalence of early complications after Transcatheter Aortic Valve Implantation (TAVI) and their correlation with the Calcium Score (CS) of the aortic valve, aorta and ilio-femoral arteries derived from pre-procedural computed tomography (CT)., Materials and Methods: We retrospectively reviewed 226 patients (100 males, mean age 79.4 ± 6.7 years) undergoing 64-slice CT for pre-TAVI evaluation from January 2018 to April 2021. The population was divided into CS quartiles., Results: Overall, 173 patients underwent TAVI procedure, of whom 61% presented paravalvular leak after the procedure, 28% presented bleeding or vascular complications, 25% presented atrioventricular block, and 8% developed acute kidney injury. The prevalence of paravalvular leak and vascular complications was higher in the upper CS quartiles for aortic valve and ilio-femoral arteries., Conclusions: Aortic valve and vascular CS could help to predict post-TAVI early complications., (© 2023. The Author(s).)

Published
2023
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4. Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method.

Author

Salvaggio G, Comelli A, Portoghese M, Cutaia G, Cannella R, Vernuccio F, Stefano A, Dispensa N, La Tona G, Salvaggio L, Calamia M, Gagliardo C, Lagalla R, and Midiri M

Subjects
Humans, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging methods, Male, Neural Networks, Computer, Retrospective Studies, Deep Learning, Prostate diagnostic imaging
Abstract

Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation., Materials and Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate volumes obtained with ellipsoid formula, manual segmentation, and automated segmentation. To provide an evaluation of the similarity or difference to manual segmentation, sensitivity, positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric difference (VD) were calculated., Results: Differences between prostate volume obtained from ellipsoid formula versus manual segmentation and versus automatic segmentation were statistically significant (P < 0.049318 and P < 0.034305, respectively), while no statistical difference was found between volume obtained from manual versus automatic segmentation (P = 0.438045). The performance of ENet versus manual segmentations was good providing a sensitivity of 93.51%, a PPV of 87.93%, a DSC of 90.38%, a VOE of 17.32% and a VD of 6.85%., Conclusion: The presence of median lobe enlargement may lead to MRI volume overestimation when using the ellipsoid formula so that a segmentation method is recommended. ENet volume estimation showed great accuracy in evaluation of prostate volume similar to that of manual segmentation., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Published
2022
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