Trial Title:
I3LUNG: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy
NCT ID:
NCT05537922
Condition:
Non Small Cell Lung Cancer
Lung Cancer Metastatic
Lung Cancer, Nonsmall Cell
Lung Adenocarcinoma
Conditions: Official terms:
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Adenocarcinoma of Lung
Conditions: Keywords:
NSCLC
Artificial Intelligence
Immunotherapy
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Other
Summary:
I3LUNG is an international project aiming to develop a medical device to predict
immunotherapy efficacy for NSCLC patients using the integration of multisource data (real
word and multi-omics data). This objective will be reached through a retrospective -
setting up a transnational platform of available data from 2000 patients - and a
prospective - multi-omics prospective data collection in 200 NSCLS patients - study
phase.
The retrospective cohort will be used to perform a preliminary knowledge extraction phase
and to build a retrospective predictive model for IO (R-Model), that will be used in the
prospective study phase to create a first version of the PDSS tool, an AI-based tool to
provide an easy and ready-to-use access to predictive models, increasing care
appropriateness, reducing the negative impacts of prolonged and toxic treatments on
wellbeing and healthcare costs.
The prospective part of the project includes the collection and the analysis of
multi-OMICs data from a multicentric prospective cohort of about 200 patients. This
cohort will be used to validate the results obtained from the retrospective model through
the creation of a new model (P-Model), which will be used to create the final PDSS tool.
Detailed description:
The I3LUNG project aims to achieve the highest performance in personalized medicine
through Artificial Intelligence/Machine Learning (AI/ML) modelled on multimodal patients'
data, together with implementing an AI/ML model in a real-life setting. A set of
patient-centered ML tools designed and validated for the project, which make use of the
novel virtual patient AVATAR entity for predicting progression and outcome. To maximize
its impact, the use of Trustworthy explanaible AI methodology will integrate the AI's
inherent performances with the input of human intuition to construct a responsible AI
application able to fully implement truly individualized treatment decisions in NSCLC
interpretable and trustworthy for clinicians. The final objective is the establishment of
a Worldwide Data Sharing and Elaboration Platform (DSEP). The DSEP will provide guiding
tools for patients, providing information to generate awareness on treatments. Lastly, it
gives access to researchers and the general scientific community to the most up-to-date
data sources on NSCLC.
Within the I3LUNG project, an ad-hoc IPDAS for NSCLC patients will be developed. Patient
decision aids are tools that might be used by patients either before or within a
consultation with physicians. Patient decision aids explicitly represent the decision to
be made and provide patients with user-friendly information about each treatment option
by focusing on harms and benefits. This tool could allow patients to explain and clarify
the high complexity of the information provided by the AI/ML approach. These decisional
support systems have been demonstrated to be effective in empowering patients, improving
their knowledge, promoting their active participation in clinical decision-making about
treatments, and improving overall patient satisfaction with care while decreasing
decisional conflict and decisional regret (26-30).
Finally, within the I3LUNG project it will be assessed whether using the IPDAS during the
clinical consultation would foster the quality of the shared decision-making as well as
the quality of the doctor-patient communication. Alongside the evaluation of the impact
of the IPDAS, it will be also evaluated whether the inclusion of the AI/ML predictive
models in clinical practice will be added value in supporting oncologists' clinical
decision-making and decreasing cognitive fatigue and decisional conflict.
I3LUNG adopts a two-pronged approach to develop a medical device through the creation and
validation of retrospective and prospective AI-based models to predict immunotherapy
efficacy for NSCLC patients using the integration of multisource data (real word and
multi-omics data) through a retrospective - setting up a transnational platform of
available data from 2000 patients - and a prospective - multi-omics prospective data
collection in 200 NSCLS patients - study phase.
The retrospective part of the I3LUNG project includes the analysis of a multicentric
retrospective cohort of more than 2,000 patients. This cohort will be used to perform a
preliminary knowledge extraction phase and to build a retrospective predictive model for
IO (R-Model), that will be used in the prospective study phase to create a first version
of the PDSS tool, an AI-based tool to provide an easy and ready-to-use access to
predictive models, increasing care appropriateness, reducing the negative impacts of
prolonged and toxic treatments on wellbeing and healthcare costs. Also, CT and PET scans
will be collected and a first radiomic signature will be created to feed the R-Model.
The prospective part of the project includes the collection and the analysis of
multi-OMICs data from a multicentric prospective cohort of about 200 patients. This
cohort will be used to validate the results obtained from the retrospective model through
the creation of a new model (P-Model), which will be used to create the final PDSS tool.
Criteria for eligibility:
Study pop:
The retrospective cohort consists of aNSCLC patients treated with IO. Data from an
estimated 2000 patients treated with IO-based therapy will be collected from all the
clinical partners (INT, GHD, VHIO, MH, SZMC and UOC). Informed consent for the study will
be obtained before enrolment. If not feasible, i.e. patients not alive, the approval to
Privacy Guarantee will be obtained.
In the prospective phase, the study cohort consists of aNSCLC patients candidate for
first-line IO-based therapy with available surgical samples (enough to perform OMICs).
Baseline data of an estimated 200 patients from 5 clinical centers (INT, GHD, VHIO, MH
and SZMC) will be collected including complete clinical, multi- OMICs analysis, imaging
of CT and PET scan at baseline IO, behavioral, health economic, QoL measurements with
based-sensor techniques and standard QoL. Informed consent for the study will be obtained
before enrolment.
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
- Age >/= 18 years.
- Eastern Cooperative Oncology Group (ECOG) performance status
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
University of Chicago
Address:
City:
Chicago
Zip:
60637
Country:
United States
Status:
Recruiting
Contact:
Last name:
Marina Garassino
Facility:
Name:
Metropolitan Hospital
Address:
City:
Athens
Country:
Greece
Status:
Recruiting
Contact:
Last name:
Elena Linardou
Facility:
Name:
Shaare Zedek Medical Center
Address:
City:
Gerusalemme
Country:
Israel
Status:
Recruiting
Contact:
Last name:
Nir Peled
Facility:
Name:
Vall D'Hebron Institute of Oncology
Address:
City:
Barcelona
Country:
Spain
Status:
Recruiting
Contact:
Last name:
Enriqueta Felip
Start date:
October 1, 2022
Completion date:
October 1, 2027
Lead sponsor:
Agency:
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Agency class:
Other
Collaborator:
Agency:
Vall d'Hebron Institute of Oncology
Agency class:
Other
Collaborator:
Agency:
Shaare Zedek Medical Center
Agency class:
Other
Collaborator:
Agency:
LungenClinic Grosshansdorf
Agency class:
Other
Collaborator:
Agency:
Metropolitan Hospital, Athens
Agency class:
Other
Collaborator:
Agency:
University of Chicago
Agency class:
Other
Source:
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05537922
https://i3lung.eu/
