i3lung: integrative science, intelligent data platform for individualized lung cancer care with immunotherapy

Conditions: Non Small Cell Lung Cancer; Lung Cancer Metastatic; Lung Cancer, Nonsmall Cell; Lung Adenocarcinoma

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.

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.

Eligibility criteria:
inclusion criteria: * age \>/= 18 years. * eastern cooperative oncology group (ecog) performance status \</= 2. * histologically confirmed diagnosis of stage iiib/c-iv non-small-cell lung cancer * received any line immunotherapy (maintenance therapy with durvalumab is allowed) for retrospective cohort; clinical indication for frontline treatment with immunotherapy as first line treatment for prospective cohort. * patients with cns metastasis are allowed * patients with driver genomic alterations are allowed (only for retrospective cohort) * evidence of a personally signed and dated icf indicating that the patient has been informed of and understands all pertinent aspects of the study before enrolment (only for prospective cohort) * availability of at least one ffpe block for -omics data generation (only for prospective cohort) exclusion criteria: * patients without minimal treatment information data to be included in the retrospective cohort * prior treatment for advanced disease (only for prospective cohort) * unavailability or inability to comply with the requested study procedures, including compilation of qol questionnaires

Keywords: nsclc artificial intelligence immunotherapy