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Sebri V, Marzorati C, Dorangricchia P, Monzani D, Grasso R, Prelaj A, Provenzano L, Mazzeo L, Dumitrascu AD, Sonnek J, Szewczyk M, Watermann I, Trovò F, Dollis N, Sarris E, Garassino MC, Bestvina CM, Pedrocchi A, Ambrosini E, Kosta S, Felip E, Soleda M, Roca AA, Rodríguez‐Morató J, Nuara A, Lourie Y, Fernandez‐Pinto M, Aguaron A, Pravettoni G. The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project. Cancer Med 2024; 13:e7159. [PMID: 38741546 PMCID: PMC11091486 DOI: 10.1002/cam4.7159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/17/2024] [Accepted: 03/22/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. METHODS In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. RESULTS The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. CONCLUSION Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.
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Affiliation(s)
- Valeria Sebri
- Applied Research Division for Cognitive and Psychological ScienceIEO, European Institute of Oncology IRCCSMilanItaly
| | - Chiara Marzorati
- Applied Research Division for Cognitive and Psychological ScienceIEO, European Institute of Oncology IRCCSMilanItaly
| | - Patrizia Dorangricchia
- Applied Research Division for Cognitive and Psychological ScienceIEO, European Institute of Oncology IRCCSMilanItaly
| | - Dario Monzani
- Laboratory of Behavioral Observation and Research on Human Development, Department of Psychology, Educational Science and Human MovementUniversity of PalermoPalermoItaly
| | - Roberto Grasso
- Applied Research Division for Cognitive and Psychological ScienceIEO, European Institute of Oncology IRCCSMilanItaly
- Department of Oncology and Hemato‐OncologyUniversity of MilanMilanItaly
| | - Arsela Prelaj
- Thoracic Oncology Unit, Medical Oncology Department 1Fondazione IRCCS Istituto Nazionale TumoriMilanItaly
- Department of Electronics, Information, and BioengineeringPolitecnico di MilanoMilanItaly
| | - Leonardo Provenzano
- Medical Oncology DepartmentFondazione IRCCS Istituto Nazionale dei Tumori di MilanoMilanItaly
| | - Laura Mazzeo
- Thoracic Oncology Unit, Medical Oncology Department 1Fondazione IRCCS Istituto Nazionale TumoriMilanItaly
- Department of Electronics, Information, and BioengineeringPolitecnico di MilanoMilanItaly
| | - Andra Diana Dumitrascu
- Thoracic Oncology Unit, Medical Oncology Department 1Fondazione IRCCS Istituto Nazionale TumoriMilanItaly
| | - Jana Sonnek
- Lungen Clinic Grosshansdorf, Airway Research Center NorthGerman Center for Lung ResearchGrosshansdorfGermany
| | - Marlen Szewczyk
- Lungen Clinic Grosshansdorf, Airway Research Center NorthGerman Center for Lung ResearchGrosshansdorfGermany
| | - Iris Watermann
- Lungen Clinic Grosshansdorf, Airway Research Center NorthGerman Center for Lung ResearchGrosshansdorfGermany
| | | | | | | | - Marina Chiara Garassino
- Knapp Center for Biomedical DiscoveryUniversity of Chicago Medicine & Biological SciencesChicagoIllinoisUSA
| | - Christine M. Bestvina
- Knapp Center for Biomedical DiscoveryUniversity of Chicago Medicine & Biological SciencesChicagoIllinoisUSA
| | - Alessandra Pedrocchi
- Department of Electronics, Information and BioengineeringNeuroengineering and Medical Robotics Laboratory NearLabMilanItaly
| | - Emilia Ambrosini
- Department of Electronics, Information and BioengineeringNeuroengineering and Medical Robotics Laboratory NearLabMilanItaly
| | - Sokol Kosta
- Department of Electronic SystemsAalborg UniversityCopenhagenDenmark
| | - Enriqueta Felip
- Vall d'Hebron University HospitalBarcelonaSpain
- Vall d'Hebron Institute of OncologyBarcelonaSpain
| | | | | | | | | | | | | | | | - Gabriella Pravettoni
- Applied Research Division for Cognitive and Psychological ScienceIEO, European Institute of Oncology IRCCSMilanItaly
- Department of Oncology and Hemato‐OncologyUniversity of MilanMilanItaly
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Prelaj A, Ganzinelli M, Provenzano L, Mazzeo L, Viscardi G, Metro G, Galli G, Agustoni F, Corte CMD, Spagnoletti A, Giani C, Ferrara R, Proto C, Brambilla M, Dumitrascu AD, Inno A, Signorelli D, Pizzutilo EG, Brighenti M, Biello F, Bennati C, Toschi L, Russano M, Cortellini A, Catania C, Bertolini F, Berardi R, Cantini L, Pecci F, Macerelli M, Emili R, Bareggi C, Verderame F, Lugini A, Pisconti S, Buzzacchino F, Aieta M, Tartarone A, Spinelli G, Vita E, Grisanti S, Trovò F, Auletta P, Lorenzini D, Agnelli L, Sangaletti S, Mazzoni F, Calareso G, Ruggirello M, Greco GF, Vigorito R, Occhipinti M, Manglaviti S, Beninato T, Leporati R, Ambrosini P, Serino R, Silvestri C, Zito E, Pedrocchi ACL, Miskovic V, de Braud F, Pruneri G, Lo Russo G, Genova C, Vingiani A. APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research. Clin Lung Cancer 2024; 25:190-195. [PMID: 38262770 DOI: 10.1016/j.cllc.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024]
Abstract
INTRODUCTION Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). METHODS AND OBJECTIVES APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. CONCLUSION APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.
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Affiliation(s)
- Arsela Prelaj
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy; Electronic, Information e Bio-engeenering, Politecnico di Milano, Milan, Italy
| | - Monica Ganzinelli
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Leonardo Provenzano
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy.
| | - Laura Mazzeo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Giuseppe Viscardi
- Oncology Department, Ospedale Monaldi, Azienda Ospedaliera Dei Colli, Napoli, Italy
| | - Giulio Metro
- Oncology Unit, Azienda Ospedaliera Santa Maria della Misercordia, Perugia, Italy
| | - Giulia Galli
- Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Francesco Agustoni
- Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Carminia Maria Della Corte
- Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Napoli, Italy
| | - Andrea Spagnoletti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Claudia Giani
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Roberto Ferrara
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Claudia Proto
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Marta Brambilla
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Andra Diana Dumitrascu
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Alessandro Inno
- Oncology Department, IRCCS Ospedale Sacro Cuore don Calabria, Verona, Italy
| | - Diego Signorelli
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | | | | | - Federica Biello
- Medical Oncology Unit, Azienda Ospedaliero Universitaria Maggiore della Carità, Novara, Italy
| | - Chiara Bennati
- Oncology Unit, Ospedale Santa Maria delle Croci, Ravenna, Italy
| | - Luca Toschi
- Oncology Department, Istituto Clinico Humanitas IRCCS, Milan, Italy
| | - Marco Russano
- Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy; Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, United Kingdom
| | - Alessio Cortellini
- Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
| | - Chiara Catania
- Oncology Department, Humanitas Gavazzeni, Bergamo, Italy
| | | | - Rossana Berardi
- Clinica Oncologica, Università Politecnica delle Marche, AOU delle Marche, Ancona, Italy
| | - Luca Cantini
- Clinica Oncologica, Università Politecnica delle Marche, AOU delle Marche, Ancona, Italy
| | - Federica Pecci
- Clinica Oncologica, Università Politecnica delle Marche, AOU delle Marche, Ancona, Italy
| | - Marianna Macerelli
- Medical Oncology Unit, Azienda Ospedaliero-Universitaria Santa Maria Della Misericordia, Udine, Italy
| | - Rita Emili
- Oncology Unit, Ospedale Santa Maria della Misericordia, Urbino, Italy
| | - Claudia Bareggi
- Oncology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Antonio Lugini
- Oncology Unit, Azienda Ospedaliera San Giovanni Addolorata, Rome, Italy
| | | | | | - Michele Aieta
- Oncology Unit, IRCCS CROB, Rionero in Vulture, Italy
| | | | | | - Emanuele Vita
- Oncology Department, Policlinico Universitario Fondazione "A.Gemelli" IRCCS, Rome, Italy
| | - Salvatore Grisanti
- Medical Oncology Unit, ASST Spedali Civili di Breascia, University of Brescia, Brescia, Italy
| | - Francesco Trovò
- Electronic, Information e Bio-engeenering, Politecnico di Milano, Milan, Italy
| | - Pietro Auletta
- IPOP onlus - Associazione Insieme per i Pazienti di Oncologia Polmonare, Milan, Italy
| | - Daniele Lorenzini
- Pathology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Luca Agnelli
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Sabina Sangaletti
- Sperimental Oncology and Molecular Medicine Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | | | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Margherita Ruggirello
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | | | - Raffaella Vigorito
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Mario Occhipinti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Sara Manglaviti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Teresa Beninato
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Rita Leporati
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Paolo Ambrosini
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Roberta Serino
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Cecilia Silvestri
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Emanuela Zito
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | | | - Vanja Miskovic
- Electronic, Information e Bio-engeenering, Politecnico di Milano, Milan, Italy
| | - Filippo de Braud
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Giancarlo Pruneri
- Pathology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Giuseppe Lo Russo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Carlo Genova
- Medical Oncology Unit, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Andrea Vingiani
- Pathology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
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Prelaj A, Galli EG, Miskovic V, Pesenti M, Viscardi G, Pedica B, Mazzeo L, Bottiglieri A, Provenzano L, Spagnoletti A, Marinacci R, De Toma A, Proto C, Ferrara R, Brambilla M, Occhipinti M, Manglaviti S, Galli G, Signorelli D, Giani C, Beninato T, Pircher CC, Rametta A, Kosta S, Zanitti M, Di Mauro MR, Rinaldi A, Di Gregorio S, Antonia M, Garassino MC, de Braud FGM, Restelli M, Lo Russo G, Ganzinelli M, Trovò F, Pedrocchi ALG. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients. Front Oncol 2023; 12:1078822. [PMID: 36755856 PMCID: PMC9899835 DOI: 10.3389/fonc.2022.1078822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/14/2022] [Indexed: 01/24/2023] Open
Abstract
Introduction Artificial 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. Methods We 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. Results Of 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. Conclusions In 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.
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Affiliation(s)
- Arsela Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy,*Correspondence: Arsela Prelaj,
| | - Edoardo Gregorio Galli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Vanja Miskovic
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Mattia Pesenti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Viscardi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Medical Oncology Unit, Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Benedetta Pedica
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Laura Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Achille Bottiglieri
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Leonardo Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Andrea Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Roberto Marinacci
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alessandro De Toma
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Claudia Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Roberto Ferrara
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marta Brambilla
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mario Occhipinti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Sara Manglaviti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Giulia Galli
- Medical Oncology Unit, Policlinico San Matteo Fondazione IRCCS, Pavia, Italy
| | - Diego Signorelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Claudia Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Teresa Beninato
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Chiara Carlotta Pircher
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Alessandro Rametta
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Sokol Kosta
- Department of Electronic System, Aalborg University, Copenhagen, Aalborg, Denmark
| | - Michele Zanitti
- Department of Electronic System, Aalborg University, Copenhagen, Aalborg, Denmark
| | - Maria Rosa Di Mauro
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Arturo Rinaldi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Settimio Di Gregorio
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Martinetti Antonia
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marina Chiara Garassino
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Thoracic Oncology Program, Section of Hematology/Oncology, University of Chicago, Chicago, IL, United States
| | - Filippo G. M. de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy,Oncology Department, University of Milan, Milan, Italy
| | - Marcello Restelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Giuseppe Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Monica Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Francesco Trovò
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Prelaj A, Bottiglieri A, Provenzano L, Spagnoletti A, Mazzeo L, Miskovic V, Ganzinelli M, Lo Russo G, Ferrara R, Proto C, De Toma A, Brambilla M, Occhipinti M, Manglaviti S, Beninato T, Rametta A, Garassino M, De Braud F, Trovò F, Pedrocchi A. 1071P Trustworthy artificial intelligence models using real-world and circulating genomics data for the prediction of immunotherapy efficacy in non-small cell lung cancer patients. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Prelaj A, Proto C, Lo Russo G, Signorelli D, Ferrara R, Mensah M, Galli G, De Toma A, Viscardi G, Brambilla M, Lobefaro R, Trevisan B, Trovò F, Torri V, Sozzi G, Garassino MC, Boeri M. Integrating clinical and biological prognostic biomarkers in patients with advanced NSCLC treated with immunotherapy: the DEMo score system. Transl Lung Cancer Res 2020; 9:617-628. [PMID: 32676324 PMCID: PMC7354114 DOI: 10.21037/tlcr-20-231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Several biomarkers have been separately described to select patients for immunotherapy (IO), but few studies integrate these markers. Di Maio, EPSILoN and the plasma microRNA signature classifier (MSC), are three different clinico, biochemical and molecular markers able to independently predict prognosis in non-small cell lung cancer (NSCLC). Methods Complete data such as sex, histology, ECOG-PS, stage, smoking status, presence of liver metastasis, LDH and neutrophils-to-lymphocyte ratio were collected to generate Di Maio and EPSILoN. The MSC risk level was prospectively assessed in plasma samples collected at baseline IO. The 3 markers were integrated into the DEMo score system prospectively tested in a cohort of 200 advanced NSCLC patients treated with IO. Endpoints were overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). Results DEMo separated patients in 7-risk groups whose median OS had a trend ranging from 29.7 to 1.5 months (P<0.0001). When comparing patients with the lowest (n=29) and the highest (n=35) DEMo scores ORR was 45% and 3%, respectively (P<0.0001). Considering the 53 PD-L1 ≥50% patients, DEMo identified a group of 13 (25%) patients who benefit less from IO in terms of both OS (HR: 8.81; 95% CI: 2.87–20.01) and PFS (HR: 6.82; 95% CI: 2.57–18.10). Twelve out of 111 (11%) patients who most benefit from IO according to OS (HR: 0.21; 95% CI: 0.07–0.62) and PFS (HR: 0.28; 95% CI: 0.12–0.65) were identified by DEMo in the PD-L1 <50% group. Conclusions The DEMo prognostic score system stratified NSCLC patients treated with IO better than each single marker. The proper use of DEMo according to PD-L1 could improve selection in IO regimens.
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Affiliation(s)
- Arsela Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Electronics, Information, and Bioengineering, Polytechnic University of Milan, Milano, Italy
| | - Claudia Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Giuseppe Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Diego Signorelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Roberto Ferrara
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mavis Mensah
- Tumor Genomics Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Giulia Galli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Alessandro De Toma
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Giuseppe Viscardi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marta Brambilla
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Riccardo Lobefaro
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Benedetta Trevisan
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Francesco Trovò
- Department of Electronics, Information, and Bioengineering, Polytechnic University of Milan, Milano, Italy
| | - Valter Torri
- Pharmacological Research Institute IRCSS Mario Negri, Milano, Italy
| | - Gabriella Sozzi
- Tumor Genomics Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Mattia Boeri
- Tumor Genomics Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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Abstract
Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.
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Affiliation(s)
- Manuel Roveri
- 1 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza L. da Vinci 32, Milano, 20133, Italy
| | - Francesco Trovò
- 1 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, piazza L. da Vinci 32, Milano, 20133, Italy
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Granato A, Ilbeh SM, Trovò F, Borelli M, Granello G, Semenic M, Monti F, Pizzolato G. Transcranial magnetic stimulation as a new approach in medication overuse headache: a pilot study. J Headache Pain 2013. [PMCID: PMC3620142 DOI: 10.1186/1129-2377-14-s1-p168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Granato A, Ilbeh S, Trovò F, Borelli M, Granello G, Semenic M, Monti F, Pizzolato G. Transcranial magnetic stimulation as a new approach in medication overuse headache: a pilot study. J Headache Pain 2013. [DOI: 10.1186/1129-2377-1-s1-p168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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