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Your search keyword '"Pedrocchi Alessandra"' showing total 23 results
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23 results on '"Pedrocchi Alessandra"'

2. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

Author

Prelaj, Arsela, Galli, Edoardo Gregorio, Miskovic, Vanja, Pesenti, Mattia, Viscardi, Giuseppe, Pedica, Benedetta, Mazzeo, Laura, Bottiglieri, Achille, Provenzano, Leonardo, Spagnoletti, Andrea, Marinacci, Roberto, De Toma, Alessandro, Proto, Claudia, Ferrara, Roberto, Brambilla, Marta, Occhipinti, Mario, Manglaviti, Sara, Galli, Giulia, Signorelli, Diego, Giani, Claudia, Beninato, Teresa, Pircher, Chiara Carlotta, Rametta, Alessandro, Kosta, Sokol, Zanitti, Michele, Di Mauro, Maria Rosa, Rinaldi, Arturo, Di Gregorio, Settimio, Antonia, Martinetti, Garassino, Marina Chiara, de Braud, Filippo G.M., Restelli, Marcello, Lo Russo, Giuseppe, Ganzinelli, Monica, Trovò, Francesco, and Pedrocchi, Alessandra Laura Giulia

Subjects
Cancer Research, machine learning, explainable artificial intelligence, treatment, Oncology, immunotherapy, non-small cell lung cancer
Abstract

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

Published
2023
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5. Reproducing a decision-making network in a virtual visual discrimination task

Author

Trapani, Alessandra, primary, Sheiban, Francesco Jamal, additional, Bertone, Elisa, additional, Chiosso, Serena, additional, Colombo, Luca, additional, D'Andrea, Matteo, additional, De Santis, Francesco, additional, Fati, Francesca, additional, Fossati, Veronica, additional, Gonzalez, Victor, additional, and Pedrocchi, Alessandra, additional

Published
2022
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10. Resolvin E1 and Cytokines Environment in Skeletally Immature and Adult ACL Tears

Author

Turati, Marco, primary, Franchi, Silvia, additional, Leone, Giulio, additional, Piatti, Massimiliano, additional, Zanchi, Nicolò, additional, Gandolla, Marta, additional, Rigamonti, Luca, additional, Sacerdote, Paola, additional, Rizzi, Laura, additional, Pedrocchi, Alessandra, additional, Omeljaniuk, Robert J., additional, Zatti, Giovanni, additional, Torsello, Antonio, additional, and Bigoni, Marco, additional

Published
2021
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20. Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

Author

D’Angelo, Egidio, primary, Antonietti, Alberto, additional, Casali, Stefano, additional, Casellato, Claudia, additional, Garrido, Jesus A., additional, Luque, Niceto Rafael, additional, Mapelli, Lisa, additional, Masoli, Stefano, additional, Pedrocchi, Alessandra, additional, Prestori, Francesca, additional, Rizza, Martina Francesca, additional, and Ros, Eduardo, additional

Published
2016
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22. Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation

Author

Klauer, Christian, primary, Schauer, Thomas, additional, Reichenfelser, Werner, additional, Karner, Jakob, additional, Zwicker, Sven, additional, Gandolla, Marta, additional, Ambrosini, Emilia, additional, Ferrante, Simona, additional, Hack, Marco, additional, Jedlitschka, Andreas, additional, Duschau-Wicke, Alexander, additional, Gföhler, Margit, additional, and Pedrocchi, Alessandra, additional

Published
2014
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23. Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders (ERI-SIBS): a controlled trial of an innovative and ecological intervention for siblings of children with autism spectrum disorder.

Author

Annunziata S, Purpura G, Piazza E, Meriggi P, Fassina G, Santos L, Ambrosini E, Marchetti A, Manzi F, Massaro D, Tacci AL, Bolognesi E, Agostini S, La Rosa F, Pedrocchi APG, Molina P, and Cavallini A

Abstract

Background: It has been widely demonstrated that siblings of children with autism spectrum disorder (ASD) have an increased risk of abnormal developmental trajectories. In response to this, early recognition protocols have been developed worldwide, aiming to promote early interventions that can positively impact the neurodevelopment of this population. This paper presents the protocol of a controlled trial: ERI-SIBS (Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders) is an innovative and ecological early recognition and intervention program designed specifically for siblings of children with ASD., Methods: We aim to recruit siblings at low risk and high risk of neurodevelopmental disorders. Based on clinical evaluation at T0, we will allocate the infants into three groups: Group 1, infants at low risk without any signs of neurodevelopmental disorders; Group 2, infants at high risk without any signs of neurodevelopmental disorders; Group 3: infants at low or high risk with signs suggestive of neurodevelopmental disorders. Children of Group 2 will undergo Active Monitoring (one 90 min session once a month for 6 months), while children of Group 3 will undergo Early Intervention (one 90 min session once a week for 6 months). In both cases, the ERI-SIBS contents are based on a multidimensional and naturalistic approach and always involve caregivers. All recruited children will be evaluated at three different time points (T0 within the 8 months of life of the child, T1 after 6 months and T2 after 12 months) using behavioural, technological, and biological techniques to assess infants' neurodevelopmental functions, parent-infant interaction, and early ASD markers., Discussion: The ERI-SIBS study will expand knowledge regarding the impact of early intervention on families of infants at risk of neurodevelopmental disorders for the presence of a child with a diagnosis of ASD. The study will have the potential to significantly contribute to future research and the scientific and clinical debate on the best way to implement early intervention in at-risk populations., Clinical Trial Registration: Clinicaltrials.gov identifier (NCT06512649)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2025 Annunziata, Purpura, Piazza, Meriggi, Fassina, Santos, Ambrosini, Marchetti, Manzi, Massaro, Tacci, Bolognesi, Agostini, La Rosa, Pedrocchi, Molina and Cavallini.)

Published
2025
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