Clinical trial · Observational
Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice
Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice: the LANTERN Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this multi-centric observational clinical trial is to to develop accurate predictive models for lung cancer patients, through the creation of Digital Human Avatars using various omics-based variables and integrating well-established clinical factors with "big data" and advanced imaging features The main goals of LANTERN project are: * To develop prevention models for early lung cancer diagnosis; * To set up personalized predictive models for individual-specific treatments; Lung cancer patients will be prospectively enrolled and main omics data (including radiomics and genomics) will be collected, reflecting the main omics domains associated with the lung cancer diagnosis and decision making pathway. An exploratory analysis across all collected datasets will select a pool of potential biomarkers to create a multiple distinct multivariate models, trained though advanced machine learning (ML) and AI techniques sub-divided into specific areas of interest. Finally, the developed predictive models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the Digital Human Avatar.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Non Small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| surgical resection | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- enrolled patients
- description
- Non small cell lung cancer patients underwent surgical resection. We will use part of this cohort to built the predictive models and a second part to validate the creted models.
- interventionNames
- Procedure: surgical resection
Primary outcomes (1)
- measure
- To develop prevention models for early lung cancer diagnosis
- timeFrame
- 36 months
- description
- Development of prognostic model in NSCLC patients using omics data. In particular, will be determinate the association between radiomics characteristics and biomarkers to lung cancer stage and survival outcome. Omics data and prognostic model will be tested in terms of disease free and overall survival comapring the different models.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients with (suspected) NSCLC * Age \>18 yrs * ECOG 0-3 * Written Informed Consent Exclusion Criteria: * ECOG 4 * Psychosocial, or emotional conditions controindicating participation to the study
References
Publications (1)
- DERIVEDLococo F, Boldrini L, Diepriye CD, Evangelista J, Nero C, Flamini S, Minucci A, De Paolis E, Vita E, Cesario A, Annunziata S, Calcagni ML, Chiappetta M, Cancellieri A, Larici AR, Cicchetti G, Troost EGC, Adany R, Farre N, Ozturk E, Van Doorne D, Leoncini F, Urbani A, Trisolini R, Bria E, Giordano A, Rindi G, Sala E, Tortora G, Valentini V, Boccia S, Margaritora S, Scambia G. Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study. BMC Cancer. 2023 Jun 13;23(1):540. doi: 10.1186/s12885-023-10997-x. PMID 37312079