TY - JOUR
T1 - Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study
AU - Lococo, Filippo
AU - Boldrini, Luca
AU - Diepriye, Charles-Davies
AU - Evangelista, Jessica
AU - Nero, Camilla
AU - Flamini, Sara
AU - Minucci, Angelo
AU - De Paolis, Elisa
AU - Vita, Emanuele
AU - Cesario, Alfredo
AU - Annunziata, Salvatore
AU - Calcagni, Maria Lucia
AU - Chiappetta, Marco
AU - Cancellieri, Alessandra
AU - Larici, Anna Rita
AU - Cicchetti, Giuseppe
AU - Troost, Esther G.C.
AU - Róza, Ádány
AU - Farré, Núria
AU - Öztürk, Ece
AU - Van Doorne, Dominique
AU - Leoncini, Fausto
AU - Urbani, Andrea
AU - Trisolini, Rocco
AU - Bria, Emilio
AU - Giordano, Alessandro
AU - Rindi, Guido
AU - Sala, Evis
AU - Tortora, Giampaolo
AU - Valentini, Vincenzo
AU - Boccia, Stefania
AU - Margaritora, Stefano
AU - Scambia, Giovanni
PY - 2023
Y1 - 2023
N2 - 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. Ethics Committee approval number: 5420 − 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS – Università Cattolica del Sacro Cuore Ethics Committee. Trial registration: clinicaltrial.gov - NCT05802771.
AB - 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. Ethics Committee approval number: 5420 − 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS – Università Cattolica del Sacro Cuore Ethics Committee. Trial registration: clinicaltrial.gov - NCT05802771.
KW - Artificial intelligence (AI)
KW - Big data
KW - Digital human avatars (DHA)
KW - Genomics
KW - Lung cancer
KW - Machine learning
KW - Personalize medicine
KW - Precision medicine
KW - Radiomics
KW - System medicine
KW - Artificial intelligence (AI)
KW - Big data
KW - Digital human avatars (DHA)
KW - Genomics
KW - Lung cancer
KW - Machine learning
KW - Personalize medicine
KW - Precision medicine
KW - Radiomics
KW - System medicine
UR - http://hdl.handle.net/10807/245914
U2 - 10.1186/s12885-023-10997-x
DO - 10.1186/s12885-023-10997-x
M3 - Article
SN - 1471-2407
VL - 23
SP - N/A-N/A
JO - BMC Cancer
JF - BMC Cancer
ER -