ArticleBMC cancer2023
Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study.
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.
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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.
Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice: the LANTERN Study
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence and Decision-Making in Oncology: A Review of Ethical, Legal, and Informed Consent Challenges.Current oncology reports · 2025Pooled it
- Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training.Breathe (Sheffield, England) · 2026Review
- Observational
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- LANTERN-XGB: An Interpretable Multi-Modal Machine Learning for Improving Clinical Decision-Making in Lung Cancer.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Liquid biopsy biomarkers for cancer detection, treatment monitoring, and clinical outcome prediction.Frontiers in cell and developmental biology · 2026Review
- Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.European radiology experimental · 2025Review
- Multi-omics research on moyamoya disease: current perspectives and future directions.Nutrition & metabolism · 2025Review
- Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.International journal of molecular sciences · 2025Review
- A New Era, New Risks: The Cardio-Oncology Perspective on Immunotherapy in Non-Small Cell Lung Cancer.Cancers · 2025Review
- Low-Malignant-Potential Adenocarcinoma: A Histological Category with a Significantly Better Prognosis than Other Solid Adenocarcinomas at IA Stage.Current oncology (Toronto, Ont.) · 2025Article
- The Role of ctDNA for Diagnosis and Histological Prediction in Early Stage Non-Small-Cell Lung Cancer: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Leveraging Radiotherapy Data for Precision Oncology: Veterans Affairs Granular Radiotherapy Information Database.JCO clinical cancer informatics · 2025Article
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.Cancers · 2024Review
- The future of collaborative precision oncology approaches in sub-Saharan Africa: learnings from around the globe.Frontiers in oncology · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
33 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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