Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05537922 |
| Other study ID # |
INT147/22 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 1, 2022 |
| Est. completion date |
October 1, 2027 |
Study information
| Verified date |
January 2023 |
| Source |
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano |
| Contact |
Arsela Prelaj, MD |
| Phone |
+39 022390 3647 |
| Email |
arsela.prelaj[@]istitutotumori.mi.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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.
