Találati lista | Tudóstér

001-es BibID:	BIBFORM116118
035-os BibID:	(cikkazonosító)1082 (Scopus)85176043308
Első szerző:	Lococo, Filippo
Cím:	Correction : Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice : the LANTERN study / Filippo Lococo, Luca Boldrini, Charles-Davies Diepriye, Jessica Evangelista, Camilla Nero, Sara Flamini, Angelo Minucci, Elisa De Paolis, Emanuele Vita, Alfredo Cesario, Salvatore Annunziata, Maria Lucia Calcagni, Marco Chiappetta, Alessandra Cancellieri, Anna Rita Larici, Giuseppe Cicchetti, Esther G. C. Troost, Róza Ádány, Núria Farré, Ece Öztürk, Dominique Van Doorne, Fausto Leoncini, Andrea Urbani, Rocco Trisolini, Emilio Bria, Alessandro Giordano, Guido Rindi, Evis Sala, Giampaolo Tortora, Vincenzo Valentini, Stefania Boccia, Stefano Margaritora, Giovanni Scambia
Dátum:	2023
ISSN:	1471-2407
Tárgyszavak:	Orvostudományok Klinikai orvostudományok hozzászólás folyóiratcikk
Megjelenés:	Bmc Cancer. - 23 : 1 (2023), p. 1-2. -
További szerzők:	Boldrini, Luca Diepriye, Charles-Davies Evangelista, Jessica Nero, Camilla Flamini, Sara Minucci, Angelo Paolis, Elisa De Vita, Emanuele Cesario, Alfredo Annunziata, Salvatore Calcagni, Maria Lucia Chiappetta, Marco Cancellieri, Alessandra Larici, Anna Rita Cicchetti, Giuseppe Troost, Esther G. C. Ádány Róza (1952-) (megelőző orvostan és népegészségtan szakorvos) Farré, Núria Öztürk, Ece Doorne, Dominique Van Leoncini, Fausto Urbani, Andrea Trisolini, Rocco Bria, Emilio Giordano, Alessandro Rindi, Guido Sala, Evis Tortora, Giampaolo Valentini, Vincenzo Boccia, Stefania Margaritora, Stefano Scambia, Giovanni
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM113116
035-os BibID:	(cikkazonosító)540 (Scopus)85161949040 (WOS)001010884400002
Első szerző:	Lococo, Filippo
Cím:	Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice : the LANTERN study / Filippo Lococo, Luca Boldrini, Charles-Davies Diepriye, Jessica Evangelista, Camilla Nero, Sara Flamini, Angelo Minucci, Elisa De Paolis, Emanuele Vita, Alfredo Cesario, Salvatore Annunziata, Maria Lucia Calcagni, Marco Chiappetta, Alessandra Cancellieri, Anna Rita Larici, Giuseppe Cicchetti, Esther G. C. Troost, Ádány Róza, Núria Farré, Ece Öztürk, Dominique Van Doorne, Fausto Leoncini, Andrea Urbani, Rocco Trisolini, Emilio Bria, Alessandro Giordano, Guido Rindi, Evis Sala, Giampaolo Tortora, Vincenzo Valentini, Stefania Boccia, Stefano Margaritora, Giovanni Scambia
Dátum:	2023
ISSN:	1471-2407
Megjegyzések:	Background The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. Methods The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. Discussion The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols.
Tárgyszavak:	Orvostudományok Egészségtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Lung cancer Artificial intelligence (AI) Digital human avatars (DHA) Personalize medicine Machine learning System medicine Precision medicine Genomics Radiomics Big data
Megjelenés:	Bmc Cancer. - 23 : 1 (2023), p. 1-8. -
További szerzők:	Boldrini, Luca Diepriye, Charles-Davies Evangelista, Jessica Nero, Camilla Flamini, Sara Minucci, Angelo Paolis, Elisa De Vita, Emanuele Cesario, Alfredo Annunziata, Salvatore Calcagni, Maria Lucia Chiappetta, Marco Cancellieri, Alessandra Larici, Anna Rita Cicchetti, Giuseppe Troost, Esther G. C. Ádány Róza (1952-) (megelőző orvostan és népegészségtan szakorvos) Farré, Núria Öztürk, Ece Doorne, Dominique Van Leoncini, Fausto Urbani, Andrea Trisolini, Rocco Bria, Emilio Giordano, Alessandro Rindi, Guido Sala, Evis Tortora, Giampaolo Valentini, Vincenzo Boccia, Stefania Margaritora, Stefano Scambia, Giovanni
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: