Search

Your search keyword '"Perrotta, Paolo"' showing total 17 results
Start Over Author "Perrotta, Paolo"
17 results on '"Perrotta, Paolo"'

7. IER-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Thrombectomy for Stroke

Author

Cappellari, Manuel, Mangiafico, Salvatore, Saia, Valentina, Pracucci, Giovanni, Nappini, Sergio, Nencini, Patrizia, Konda, Daniel, Sallustio, Fabrizio, Vallone, Stefano, Zini, Andrea, Bracco, Sandra, Tassi, Rossana, Bergui, Mauro, Cerrato, Paolo, Pitrone, Antonio, Grillo, Francesco, Saletti, Andrea, De Vito, Alessandro, Gasparotti, Roberto, Magoni, Mauro, Puglielli, Edoardo, Casalena, Alfonsina, Causin, Francesco, Baracchini, Claudio, Castellan, Lucio, Malfatto, Laura, Menozzi, Roberto, Scoditti, Umberto, Comelli, Chiara, Duc, Enrica, Comai, Alessio, Franchini, Enrica, Cosottini, Mirco, Mancuso, Michelangelo, Peschillo, Simone, De Michele, Manuela, Giorgianni, Andrea, Delodovici, Maria Luisa, Lafe, Elvis, Denaro, Maria Federica, Burdi, Nicola, Internò, Saverio, Cavasin, Nicola, Critelli, Adriana, Chiumarulo, Luigi, Petruzzellis, Marco, Doddi, Marco, Carolei, Antonio, Auteri, William, Petrone, Alfredo, Padolecchia, Riccardo, Tassinari, Tiziana, Pavia, Marco, Invernizzi, Paolo, Turcato, Gianni, Forlivesi, Stefano, Ciceri, Elisa Francesca Maria, Bonetti, Bruno, Inzitari, Domenico, Toni, Danilo, Limbucci, Nicola, Consoli, Arturo, Renieri, Leonardo, Fainardi, Enrico, Gandini, Roberto, Pampana, Enrico, Diomedi, Marina, Koch, Giacomo, Verganti, Luca, Sacchetti, Federico, Zelent, Gabriele, Bigliardi, Guido, Picchetto, Livio, Vandelli, Laura, Romano, Daniele Giuseppe, Cioni, Samuele, Gennari, Paola, Cerase, Alfonso, Martini, Giuseppe, Stura, Guido, Daniele, Dino, Naldi, Andrea, Papa, Rosario, Vinci, Sergio Lucio, Bernava, Gianmarco, Velo, Mariano, Caragliano, Antonio, Tessitore, Agostino, Buonomo, Orazio, Musolino, Rossella, La Spina, Paolino, Casella, Carmela, Carolina Fazio, Maria, Cotroneo, Masina, Onofrio, Marcello, Azzini, Cristiano, Casetta, Ilaria, Mardighian, Dikran, Frigerio, Michele, Costa, Angelo, Di Egidio, Vincenzo, Lattanzi, Ruggero, Assetta, Maurizio, Cester, Giacomo, Mavilio, Nicola, Serrati, Carlo, Piazza, Paolo, Epifani, Enrico, Andreone, Andrea, Castellini, Paola, Latte, Lilia, Grisendi, Ilaria, Vaudano, Giacomo, Comelli, Simone, Cavallo, Roberto, Chianale, Gigliola, Simonetti, Luigi, Taglialatela, Francesco, Isceri, Salvatore, Procaccianti, Gaetano, Zaniboni, Anna, Borghi, Annamaria, Bonatti, Giampietro, Ferro, Federica, Bonatti, Matteo, Dall’Ora, Elisa, Currò Dossi, Roberto, Turri, Emanuela, Turri, Mara, Puglioli, Michele, Lazzarotti, Guido, Lauretti, DARIO LUCA, Giannini, Nicola, Maccarone, Miriam, Orlandi, Giovanni, Chiti, Alberto, Guidetti, Giulio, Biraschi, Francesco, Falcou, Anne, Anzini, Alexia, Mancini, Alessandra, Fausti, Silvia, Di Mascio, Maria Teresa, Durastanti, Laura, Sbardella, Emilia, Mellina, Vittorio, Baruzzi, Fabio, Pellegrino, Carlo, Terrana, Alberto, Carimati, Federico, Ruggiero, Maria, Sanna, Antioco, Passarin, Maria Grazia, Colosimo, Cesare, Pedicelli, Alessandro, D’Argento, Francesco, Alexandre, Andrea, Frisullo, Giovanni, Zappoli, Federico, Martignoni, Alessandra, Cavallini, Anna, Persico, Alessandra, Valvassori, Luca, Piano, Mariangela, Agostoni, Elio, Motto, Cristina, Gatti, Antonella, Longoni, Marco, Guccione, Angelo, Tortorella, Rachele, Zampieri, Piergiuseppe, Zimatore, Domenico Sergio, Grazioli, Andrea, Ricciardi, Giuseppe Kenneth, Augelli, Raffaele, Bovi, Paolo, Tomelleri, Giampaolo, Micheletti, Nicola, Semeraro, Vittorio, Lucarelli, Nicola, Ganimede, Maria, Tinelli, Angelica, Pia Prontera, Maria, Pesare, Angela, Cagliari, Enrico, Quatrale, Rocco, Federico, Francesco, Passalacqua, Giovanni, Filauri, Pietro, Orlandi, Berardino, De Santis, Federica, Gabriele, Amleto, Tiseo, Cindy, Armentano, Antonio, Di Benedetto, Olindo, Silvagni, Umberto, Perrotta, Paolo, Crispino, Emanuela, Stancati, Furio, Rizzuto, Stefano, Pugliese, Pierfrancesco, Pisani, Ermanno, Siniscalchi, Antonio, Gaudiano, Carmen, Pirritano, Domenico, Del Giudice, Francesco, Calia, Stefano, Ganci, Giuseppe, Sugo, Annalisa, Scomazzoni, Francesco, Simionato, Franco, Roveri, Luisa, De Nicola, Maurizio, Giannoni, Mirko, Bruni, Stefano, Gambelli, Elena, Provinciali, Leandro, Carriero, Alessandro, Coppo, Lorenzo, Baldan, Julika, Paolo Nuzzi, Nunzio, Marcheselli, Simona, Corato, Manuel, Cotroneo, Enrico, Ricciardi, Fabrizio, Gigli, Renato, Pozzessere, Claudio, Pezzella, Francesca Romana, Corsi, Fabio, Squassina, Guido, Cobelli, Milena, Morassi, Mauro, Magni, Eugenio, Pepe, Fulvio, Bigni, Barbara, Costa, Paolo, Crabbio, Massimo, Griffini, Simona, Palmerini, Francesco, Piras, Maria Paola, Natrella, Massimiliano, Fanelli, Gianluca, Cristoferi, Massimo, Bottacchi, Edo, Corso, Giovanni, Tosi, Piera, Amistà, Pietro, Russo, Monia, Tettoni, Serena, Gallesio, Ivan, Mascolo, Maria Carmela, Meloni, Giovanni Battista, Fabio, Claudio, Maiore, Mario, Pintus, Francesco, Pischedda, Aldo, Manca, Antonio, Mongili, Claudia, Zanda, Bastianina, Sanna, Antonella, Baule, Antonio, Pappalardo, Maria Pia, Craparo, Giuseppe, Gallo, Cristina, Monaco, Serena, Mannino, Marina, Terruso, Valeria, Muto, Mario, Guarnieri, Gianluigi, Andreone, Vincenzo, Dui, Giovanni, Ticca, Anna, Salmaggi, Andrea, Iannucci, Giuseppe, Pinna, Vittore, Di Clemente, Loris, Perini, Francesco, De Boni, Antonella, De Luca, Cristina, De Giorgi, Franco, Corraine, Simona, Enne, Paolo, Ganau, Claudio, and Piras, Valeria

Subjects
Male, medicine.medical_specialty, Fibrinolytic Agents/therapeutic use Humans Male Middle Aged *Nomograms Postoperative Complications/etiology Risk Assessment Risk Factors Stroke/complications/drug therapy/*surgery Thrombectomy/*adverse effects Thrombolytic Therapy/adverse effects, medicine.medical_treatment, contraindications, Risk Assessment, NO, Brain Ischemia, Brain ischemia, Postoperative Complications, Fibrinolytic Agents, Risk Factors, contraindications ◼ logistic models ◼ nomograms ◼ standard of care ◼ thrombectomy, medicine, 80 and over, Humans, Thrombolytic Therapy, Stroke, contraindications, logistic models, nomograms, standard of care, thrombectomy, Aged, Cerebral Hemorrhage, Aged, 80 and over, Advanced and Specialized Nursing, Intracerebral hemorrhage, Receiver operating characteristic, business.industry, Thrombolysis, Nomogram, Middle Aged, medicine.disease, logistic models, nomograms, standard of care, thrombectomy, Female, Thrombectomy, Tissue Plasminogen Activator, Nomograms, Settore MED/26 - NEUROLOGIA, Neurology (clinical), Radiology, Cardiology and Cardiovascular Medicine, business, Fibrinolytic agent, Cohort study
Abstract

Background and Purpose— As a reliable scoring system to detect the risk of symptomatic intracerebral hemorrhage after thrombectomy for ischemic stroke is not yet available, we developed a nomogram for predicting symptomatic intracerebral hemorrhage in patients with large vessel occlusion in the anterior circulation who received bridging of thrombectomy with intravenous thrombolysis (training set), and to validate the model by using a cohort of patients treated with direct thrombectomy (test set). Methods— We conducted a cohort study on prospectively collected data from 3714 patients enrolled in the IER (Italian Registry of Endovascular Stroke Treatment in Acute Stroke). Symptomatic intracerebral hemorrhage was defined as any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline ≤24 hours or death. Based on multivariate logistic models, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve. Results— National Institutes of Health Stroke Scale score, onset-to-end procedure time, age, unsuccessful recanalization, and Careggi collateral score composed the IER-SICH nomogram. After removing Careggi collateral score from the first model, a second model including Alberta Stroke Program Early CT Score was developed. The area under the receiver operating characteristic curve of the IER-SICH nomogram was 0.778 in the training set (n=492) and 0.709 in the test set (n=399). The area under the receiver operating characteristic curve of the second model was 0.733 in the training set (n=988) and 0.685 in the test set (n=779). Conclusions— The IER-SICH nomogram is the first model developed and validated for predicting symptomatic intracerebral hemorrhage after thrombectomy. It may provide indications on early identification of patients for more or less postprocedural intensive management.

Published
2019

10. The corticospinal tract profile in amyotrophic lateral sclerosis

Author

Sarica, Alessia, Cerasa, Antonio, Valentino, Paola, Yeatman, Jason, Trotta, Maria, Barone, Stefania, Granata, Alfredo, Nisticò, Rita, Perrotta, Paolo, Pucci, Franco, and Quattrone, Aldo

Subjects
Adult, Male, Amyotrophic Lateral Sclerosis, Models, Neurological, Statistics as Topic, Pyramidal Tracts, Middle Aged, Nerve Fibers, Myelinated, Machine Learning, Disability Evaluation, Diffusion Tensor Imaging, Case-Control Studies, Image Processing, Computer-Assisted, Anisotropy, Humans, Female, Research Articles, Aged
Abstract

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Published
2016

11. The corticospinal tract profile in amyotrophic lateral sclerosis

Author

Sarica, Alessia, primary, Cerasa, Antonio, additional, Valentino, Paola, additional, Yeatman, Jason, additional, Trotta, Maria, additional, Barone, Stefania, additional, Granata, Alfredo, additional, Nisticò, Rita, additional, Perrotta, Paolo, additional, Pucci, Franco, additional, and Quattrone, Aldo, additional

Published
2016
Full Text
View/download PDF

13. The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing–remitting multiple sclerosis

Author

Caligiuri, Maria Eugenia, primary, Barone, Stefania, additional, Cherubini, Andrea, additional, Augimeri, Antonio, additional, Chiriaco, Carmelina, additional, Trotta, Maria, additional, Granata, Alfredo, additional, Filippelli, Enrica, additional, Perrotta, Paolo, additional, Valentino, Paola, additional, and Quattrone, Aldo, additional

Published
2015
Full Text
View/download PDF

14. Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

Author

Cerasa, Antonio, primary, Castiglioni, Isabella, additional, Salvatore, Christian, additional, Funaro, Angela, additional, Martino, Iolanda, additional, Alfano, Stefania, additional, Donzuso, Giulia, additional, Perrotta, Paolo, additional, Gioia, Maria Cecilia, additional, Gilardi, Maria Carla, additional, and Quattrone, Aldo, additional

Published
2015
Full Text
View/download PDF

15. Tractography in amyotrophic lateral sclerosis using a novel probabilistic tool: A study with tract-based reconstruction compared to voxel-based approach

Author

Sarica, Alessia, primary, Cerasa, Antonio, additional, Vasta, Roberta, additional, Perrotta, Paolo, additional, Valentino, Paola, additional, Mangone, Graziella, additional, Guzzi, Pietro H., additional, Rocca, Federico, additional, Nonnis, Matteo, additional, Cannataro, Mario, additional, and Quattrone, Aldo, additional

Published
2014
Full Text
View/download PDF

16. Fully Automated Segmentation of the Pons and Midbrain Using Human T1 MR Brain Images.

Author

Nigro, Salvatore, Cerasa, Antonio, Zito, Giancarlo, Perrotta, Paolo, Chiaravalloti, Francesco, Donzuso, Giulia, Fera, Franceso, Bilotta, Eleonora, Pantano, Pietro, and Quattrone, Aldo

Subjects
MAGNETIC resonance imaging of the brain, MESENCEPHALON, BRAIN stem, BRAIN diseases, PONS test, BRAIN anatomy, CLINICAL trials, PATIENTS
Abstract

Purpose: This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction. Methods: This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer's Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. Results: The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15). Conclusions: Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

17. The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing-remitting multiple sclerosis.

Author

Caligiuri ME, Barone S, Cherubini A, Augimeri A, Chiriaco C, Trotta M, Granata A, Filippelli E, Perrotta P, Valentino P, and Quattrone A

Subjects
Adult, Anisotropy, Cognition Disorders etiology, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Multiple Sclerosis, Relapsing-Remitting complications, Neuropsychological Tests, Young Adult, Cognition Disorders pathology, Corpus Callosum pathology, Multiple Sclerosis, Relapsing-Remitting pathology
Abstract

Significant corpus callosum (CC) involvement has been found in relapsing-remitting multiple sclerosis (RRMS), even if conventional magnetic resonance imaging measures have shown poor correlation with clinical disability measures. In this work, we tested the potential of multimodal imaging of the entire CC to explain physical and cognitive disability in 47 patients with RRMS. Values of thickness, fractional anisotropy (FA) and mean diffusivity (MD) were extracted from 50 regions of interest (ROIs) sampled along the bundle. The relationships between clinical, neuropsychological and imaging variables were assessed by using Spearman's correlation. Multiple linear regression analysis was employed in order to identify the relative importance of imaging metrics in modeling different clinical variables. Regional fiber composition of the CC differentially explained the response variables (Expanded Disability Status Scale [EDSS], cognitive impairment). Increases in EDSS were explained by reductions in CC thickness and MD. Cognitive impairment was mainly explained by FA reductions in the genu and splenium. Regional CC imaging properties differentially explained disability within RRMS patients revealing strong, distinct patterns of correlation with clinical and cognitive status of patients affected by this specific clinical phenotype.

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results