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149 results on '"Rampado, Osvaldo"'

1. Optimisation of protection in the medical exposure of recurrent adult patients due to computed tomography procedures: development of recurrent exposures reference levels

Author

Bramilla, Marco, Berton, Luca, Balzano, Rosario F., Cannillo, Barbara, Carriero, Alessandro, Chauvie, Stephane, Gallo, Teresa, Cornacchia, Samantha, Cutaia, Claudia, D’Alessio, Andrea, Emanuele, Roberto, Fonio, Paolo, Matheoud, Roberta, Stasi, Michele, Talenti, Alberto, and Rampado, Osvaldo

Published
2024
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3. Effective Dose Estimation in Computed Tomography by Machine Learning.

Author

Ferrante, Matteo, De Marco, Paolo, Rampado, Osvaldo, Gianusso, Laura, and Origgi, Daniela

Abstract

Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning. Methods: In total, 69,037 CT acquisitions were collected with the dose-tracking software (DTS) available at our institution. E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R2 were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions. Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R2: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%. Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach. [ABSTRACT FROM AUTHOR]

Published
2025
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5. Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Author

Avanzo, Michele, Porzio, Massimiliano, Lorenzon, Leda, Milan, Lisa, Sghedoni, Roberto, Russo, Giorgio, Massafra, Raffaella, Fanizzi, Annarita, Barucci, Andrea, Ardu, Veronica, Branchini, Marco, Giannelli, Marco, Gallio, Elena, Cilla, Savino, Tangaro, Sabina, Lombardi, Angela, Pirrone, Giovanni, De Martin, Elena, Giuliano, Alessia, Belmonte, Gina, Russo, Serenella, Rampado, Osvaldo, and Mettivier, Giovanni

Published
2021
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8. Optimisation of protection in the medical exposure of recurrent adult patients due to computed tomography procedures: development of recurrent exposures reference levels

Author

Bramilla, Marco, primary, Berton, Luca, additional, Balzano, Rosario F., additional, Cannillo, Barbara, additional, Carriero, Alessandro, additional, Chauvie, Stephane, additional, Gallo, Teresa, additional, Cornacchia, Samantha, additional, Cutaia, Claudia, additional, D’Alessio, Andrea, additional, Emanuele, Roberto, additional, Fonio, Paolo, additional, Matheoud, Roberta, additional, Stasi, Michele, additional, Talenti, Alberto, additional, and Rampado, Osvaldo, additional

Published
2023
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9. National guidelines for dental diagnostic imaging in the developmental age

Author

Firetto, Maria Cristina, Abbinante, Antonella, Barbato, Ersilia, Bellomi, Massimo, Biondetti, Pietro, Borghesi, Andrea, Bossu’, Maurizio, Cascone, Piero, Corbella, Daniela, Di Candido, Vincenzo, Diotallevi, Paolo, Farronato, Giampiero, Federici, Antonio, Gagliani, Massimo, Granata, Claudio, Guerra, Mario, MAGI, Antonio, Maggio, Maria Cristina, Mirenghi, Stefano, Nardone, Michele, Origgi, Daniela, Paglia, Luigi, Preda, Lorenzo, Rampado, Osvaldo, Rubino, Luigi, Salerno, Sergio, Sodano, Angelo, Torresin, Alberto, and Strohmenger, Laura

Published
2019
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10. Quality control in cone-beam computed tomography (CBCT) EFOMP-ESTRO-IAEA protocol (summary report)

Author

de las Heras Gala, Hugo, Torresin, Alberto, Dasu, Alexandru, Rampado, Osvaldo, Delis, Harry, Hernández Girón, Irene, Theodorakou, Chrysoula, Andersson, Jonas, Holroyd, John, Nilsson, Mats, Edyvean, Sue, Gershan, Vesna, Hadid-Beurrier, Lama, Hoog, Christopher, Delpon, Gregory, Sancho Kolster, Ismael, Peterlin, Primož, Garayoa Roca, Julia, Caprile, Paola, and Zervides, Costas

Published
2017
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11. Sequenze RM: tecniche avanzate

Author

D’Agata, Federico, Coriasco, Mario, Rampado, Osvaldo, Corsico, Marina, Bradac, Gianni Boris, Coriasco, Mario, editor, Rampado, Osvaldo, editor, and Bradac, Gianni Boris, editor

Published
2014
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14. Le basi del contrasto in RM

Author

Coriasco, Mario, Rampado, Osvaldo, Bradac, Gianni Boris, Coriasco, Mario, editor, Rampado, Osvaldo, editor, and Bradac, Gianni Boris, editor

Published
2014
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16. La sicurezza in RM

Author

Rampado, Osvaldo, Coriasco, Mario, editor, Rampado, Osvaldo, editor, and Bradac, Gianni Boris, editor

Published
2014
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20. Elaborazione di immagini

Author

Coriasco, Mario, Balossino, Nello, Rampado, Osvaldo, Rabellino, Sergio, Coriasco, Mario, Balossino, Nello, Rampado, Osvaldo, and Rabellino, Sergio

Published
2013
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22. Luce, immagini e visione

Author

Coriasco, Mario, Balossino, Nello, Rampado, Osvaldo, Rabellino, Sergio, Coriasco, Mario, Balossino, Nello, Rampado, Osvaldo, and Rabellino, Sergio

Published
2013
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24. Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs)

Author

Balma, Michele, primary, Laudicella, Riccardo, additional, Gallio, Elena, additional, Gusella, Sara, additional, Lorenzon, Leda, additional, Peano, Simona, additional, Costa, Renato P., additional, Rampado, Osvaldo, additional, Farsad, Mohsen, additional, Evangelista, Laura, additional, Deandreis, Desiree, additional, Papaleo, Alberto, additional, and Liberini, Virginia, additional

Published
2023
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25. Artificial Intelligence and Radiomics Applications in Neuro-endocrine Neoplasms (NENs): New Tools for Molecular Hybrid Imaging and Theragnostics Approaches

Author

Balma, Michele, primary, Laudicella, Riccardo, additional, Gallio, Elena, additional, Gusella, Sara, additional, Lorenzon, Leda, additional, Peano, Simona, additional, Costa, Renato P., additional, Rampado, Osvaldo, additional, Farsad, Mohsen, additional, Evangelista, Laura, additional, Deandreis, Desiree, additional, Papaleo, Alberto, additional, and Liberini, Virginia, additional

Published
2023
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27. MEDICAL PHYSICS ESCAPE ROOM – UNLOCK THE DISCOVERY OF THE PHYSICS IN MEDICINE

Author

Vignati, Anna, primary, Rampado, Osvaldo, additional, Abujami, Mohammed, additional, Bersani, Davide, additional, Data, Emanuele, additional, Ferrero, Veronica, additional, Fiorina, Elisa, additional, Galeone, Cosimo, additional, Giordanengo, Simona, additional, Martì Villarreal, Oscar Ariel, additional, Milian, Felix Mas, additional, Monaco, Vincenzo, additional, Monti, Valeria, additional, Sacchi, Roberto, additional, Vallero, Maria Beatrice, additional, and Cirio, Roberto, additional

Published
2022
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32. Ultra-Low-Dose Whole-Body Computed Tomography Protocol Optimization for Patients With Plasma Cell Disorders: Diagnostic Accuracy and Effective Dose Analysis From a Reference Center

Author

Tore, Davide, primary, Rampado, Osvaldo, additional, Guarnaccia, Carla, additional, Mina, Roberto, additional, Oronzio, Maria, additional, Santonocito, Ambra, additional, Serafini, Alessandro, additional, Strazzarino, Giulio Antonino, additional, Gianusso, Laura, additional, Bringhen, Sara, additional, Fonio, Paolo, additional, and Depaoli, Alessandro, additional

Published
2021
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33. Prognostic Value of Whole-Body PET Volumetric Parameters Extracted from 68Ga-DOTATOC PET/CT in Well-Differentiated Neuroendocrine Tumors

Author

Thuillier, Philippe, primary, Liberini, Virginia, additional, Grimaldi, Serena, additional, Rampado, Osvaldo, additional, Gallio, Elena, additional, Santi, Bruno De, additional, Arvat, Emanuela, additional, Piovesan, Alessandro, additional, Filippi, Roberto, additional, Abgral, Ronan, additional, Molinari, Filippo, additional, and Deandreis, Désirée, additional

Published
2021
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34. Additional file 1 of Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

Author

Liberini, Virginia, Santi, Bruno De, Rampado, Osvaldo, Gallio, Elena, Dionisi, Beatrice, Ceci, Francesco, Polverari, Giulia, Thuillier, Philippe, Molinari, Filippo, and Deandreis, Désirée

Abstract

Additional file 1: Table S1. LifeX radiomic features description according to the Imaging Biomarker Standardization Initiative (IBSI) description (update 17 December 2019). LifeX version was 4.81. Figure S1. Box plots showing the distribution of SUVmax (panel A) and Volume (panel B). Figure S2. (A) Bar diagrams of intra-class correlation coefficient (ICC) values of RFs for robustness to SUVmax thresholding. Bars show the median ICC between the different segmentations for the absolute intensity rescale factor AR60. Range error bars (in black) encompass the lowest and highest values for different operators. (B) Boxplot of COVL for different threshold (20, 30, 40%) for each RFs, for the first operator (results superposable for the other operators). TLG (total lesion glycolysis) conventional parameter in our study corresponds to the TLSRE (total lesion somatostatin receptor expression). Figure S3. Radiomic features with moderate or poor consistency (ICC < 0.80), but high agreement (median COVL < 10%) to intensity discretization. The RFs were: GLCM_Entropy_log2, GLCM_Entropy_log10 (not shown), GLRLM_SRE, GLRLM_LRE and GLRLM_RP. Value of the RFs for each lesion are presented in the top row; boxplots of COVL for the first operator are presented in the bottom row. Figure S4. Radiomic features with high consistency (ICC > 0.90), but low agreement (median COVL > 10%) to SUVmax thresholds (0, 20, 30 and 40%). The RFs were: GLRLM_LGRE, GLRLM_SRLGE, GLZLM_LGZE and GLZLM_LZLGE. Value of the RFs for each lesion are presented in the top row; boxplots of COVL for the first operator are presented in the bottom row. Figure S5. Boxplot showing the distribution of RF value for each operator. The three RFs chosen are the most representative of the impact of segmentation on ICC. Segmentation did not affect SUVmean (A) and TLG (B) in terms of ICC, although TLG was characterized by not negligible dispersion (percentage of COVL) in our study. In contrast, segmentation had a high impact on GLZLM_SZLGE (C) in terms of both ICC and COVL. Mean COVL of SUVmean, TLG and GLZLM_SZLGE was 8.33±3.96, 13.38±8.52 and 30.67±27.29, respectively.

Published
2021
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35. Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms

Author

Thuillier, Philippe, primary, Liberini, Virginia, additional, Rampado, Osvaldo, additional, Gallio, Elena, additional, De Santi, Bruno, additional, Ceci, Francesco, additional, Metovic, Jasna, additional, Papotti, Mauro, additional, Volante, Marco, additional, Molinari, Filippo, additional, and Deandreis, Désirée, additional

Published
2021
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36. Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study

Author

Homayounieh, Fatemeh, primary, Holmberg, Ola, additional, Umairi, Rashid Al, additional, Aly, Sallam, additional, Basevičius, Algidas, additional, Costa, Paulo Roberto, additional, Darweesh, Adham, additional, Gershan, Vesna, additional, Ilves, Pilvi, additional, Kostova-Lefterova, Desislava, additional, Renha, Simone Kodlulovich, additional, Mohseni, Iman, additional, Rampado, Osvaldo, additional, Rotaru, Natalia, additional, Shirazu, Issahaku, additional, Sinitsyn, Valentin, additional, Turk, Tajana, additional, Van Ngoc Ty, Claire, additional, Kalra, Mannudeep K., additional, and Vassileva, Jenia, additional

Published
2021
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44. 68Ga-DOTATOC PET/CT-Based Radiomic Analysis and PRRT Outcome: A Preliminary Evaluation Based on an Exploratory Radiomic Analysis on Two Patients

Author

Liberini, Virginia, primary, Rampado, Osvaldo, additional, Gallio, Elena, additional, De Santi, Bruno, additional, Ceci, Francesco, additional, Dionisi, Beatrice, additional, Thuillier, Philippe, additional, Ciuffreda, Libero, additional, Piovesan, Alessandro, additional, Fioroni, Federica, additional, Versari, Annibale, additional, Molinari, Filippo, additional, and Deandreis, Désirée, additional

Published
2021
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45. Effects of different levels of CT iterative reconstruction on low-contrast detectability and radiation dose in patients of different sizes: an anthropomorphic phantom study

Author

Rampado, Osvaldo, primary, Depaoli, Alessandro, additional, Marchisio, Filippo, additional, Gatti, Marco, additional, Racine, Damien, additional, Ruggeri, Valeria, additional, Ruggirello, Irene, additional, Darvizeh, Fatemeh, additional, Fonio, Paolo, additional, and Ropolo, Roberto, additional

Published
2020
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46. 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival

Author

Polverari, Giulia, primary, Ceci, Francesco, additional, Bertaglia, Valentina, additional, Reale, Maria Lucia, additional, Rampado, Osvaldo, additional, Gallio, Elena, additional, Passera, Roberto, additional, Liberini, Virginia, additional, Scapoli, Paola, additional, Arena, Vincenzo, additional, Racca, Manuela, additional, Veltri, Andrea, additional, Novello, Silvia, additional, and Deandreis, Désirée, additional

Published
2020
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