Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice: the LANTERN Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 600
- 主要终点
- To develop prevention models for early lung cancer diagnosis
研究概览
简要总结
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.
详细描述
Patient enrolment and omics data collection The objective of this WP is to gather information from all the clinical and omics based data sources considered as clinically significant for decision support in the lung cancer comprehensive diagnosis and therapy workflow. A structured terminological system will be developed for prospective data collection through specific Case Report Forms (CRFs).
Patients will be enrolled by the dedicated research enrolment centres and data obtained from the five omics-based variables, will be collected and recorded in a secure database.
Omics data archiving and inter-actionability The main aim of this WP is to allow complete data integration into both existing and new archiving systems and to ensure an easy and effective use and sharing of collected omics data.
All collected data representing the different considered omics-domains will be recorded according to a shared common ontology. The shared general ontology will represent a structured terminological system for data archiving and analysis where all the different omics domains will be recorded in a specific eCRF, ensuring coherence for all the collected data variables. Finally, the collected omics-related data will then undergo radiomic analysis and radiomic features will then be extracted.
Omics data modelling, Digital Human avatar (DHA) creation and validation
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with (suspected) NSCLC
- •Age >18 yrs
- •Written Informed Consent
排除标准
- •Psychosocial, or emotional conditions controindicating participation to the study
结局指标
主要结局
To develop prevention models for early lung cancer diagnosis
时间窗: 36 months
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.
次要结局
未报告次要终点