Evidence map›Paper›PMID 37312079›Full record

ArticleBMC cancer2023

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 and 23 more

Erratum issued Registry-linked trialAbstract read
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05802771 (Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice), which is not on this map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05802771 unknown statusnot on this map

Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice: the LANTERN Study

TypeobservationalSponsorFondazione Policlinico Universitario Agostino Gemelli IRCCSRan2023 to 2026Enrolled600ConditionsNon Small Cell Lung CancerArmssurgical resection
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Observational
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. Review
  15. Review
  16. Article
  17. Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors.

Filippo Lococo *Catholic University of the Sacred Heart, Rome, Italy. filippo.lococo@policlinicogemelli.it.
Luca Boldrini *Catholic University of the Sacred Heart, Rome, Italy.
Charles-Davies DiepriyeRadiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, Rome, Italy.
Jessica EvangelistaCatholic University of the Sacred Heart, Rome, Italy.
Camilla NeroCatholic University of the Sacred Heart, Rome, Italy.
Sara FlaminiThoracic Surgery Unit, A. Gemelli University Hospital Foundation IRCCS, Rome, Italy.
Angelo MinucciCatholic University of the Sacred Heart, Rome, Italy.
Elisa De PaolisDepartmental Unit of Molecular and Genomic Diagnostics, Genomics Core Facility, Gemelli Science and Technology Park (G-STeP), A. Gemelli University Hospital Foundation IRCCS, Rome, Italy.
Emanuele VitaCatholic University of the Sacred Heart, Rome, Italy.
Alfredo CesarioCatholic University of the Sacred Heart, Rome, Italy.
Salvatore AnnunziataCatholic University of the Sacred Heart, Rome, Italy.
Maria Lucia CalcagniCatholic University of the Sacred Heart, Rome, Italy.
Marco ChiappettaCatholic University of the Sacred Heart, Rome, Italy.
Alessandra CancellieriCatholic University of the Sacred Heart, Rome, Italy.
Anna Rita LariciCatholic University of the Sacred Heart, Rome, Italy.
Giuseppe CicchettiCatholic University of the Sacred Heart, Rome, Italy.
Esther G C TroostDepartment of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Róza ÁdányELKH-DE Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Núria FarréInstitut de Recerca de l'Hospital de la Santa Creu i Sant Pau (IR-HSCSP), Barcelona, Spain.
Ece ÖztürkSchool of Medicine, Turkey and Koç University Research Center for Translational Medicine (KUTTAM), Sariyer, Koç University, Istanbul, Turkey.
Dominique Van DoorneDepartment of Philosophy and Educational Sciences, University of Turin - Academy of the Expert Patient ADPEE - EUPATI, Turin, Italy.
Fausto LeonciniCatholic University of the Sacred Heart, Rome, Italy.
Andrea UrbaniCatholic University of the Sacred Heart, Rome, Italy.
Rocco TrisoliniCatholic University of the Sacred Heart, Rome, Italy.
Emilio BriaCatholic University of the Sacred Heart, Rome, Italy.
Alessandro GiordanoCatholic University of the Sacred Heart, Rome, Italy.
Guido RindiCatholic University of the Sacred Heart, Rome, Italy.
Evis SalaCatholic University of the Sacred Heart, Rome, Italy.
Giampaolo TortoraCatholic University of the Sacred Heart, Rome, Italy.
Vincenzo ValentiniCatholic University of the Sacred Heart, Rome, Italy.
Stefania BocciaCatholic University of the Sacred Heart, Rome, Italy.
Stefano Margaritora *Catholic University of the Sacred Heart, Rome, Italy.
Giovanni Scambia *Catholic University of the Sacred Heart, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsThe 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 registrationclinicaltrial.gov - NCT05802771.

Indexed as

Lung NeoplasmsPrecision MedicineArtificial IntelligenceHumansMultiomicsQuality of LifeArtificial intelligence (AI)Big dataDigital human avatars (DHA)GenomicsLung cancerMachine learningPersonalize medicinePrecision medicineRadiomicsSystem medicine

Identifiers

PMID37312079
PMCPMC10262371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.