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Recruiting NCT06788366

AI-HOPE Lung Cancer: Building a Predictive Tool for Metastatic Lung Cancer

Observational NSCLC Stage IV

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Immunotherapy, Chemotherapy.
Who it may be relevant to
Registry conditions: NSCLC Stage IV. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Italy
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Overview

The goal of our project is building a predictive response algorithm for patients with metastatic lung cancer, exploiting an artificial intelligence platform. It will collect patient information from all areas (clinical, laboratory, radiological, pathological) and analyse them, understanding connections and correlations, both at baseline and at pre-specified timepoints. It would lead to the development of a reliable and constantly evolving predictive score, able to continuously re-weight the importance of each variable as new data come in. Since the greatest clinical need is identifying non-responders to immunotherapy and chemo-immunotherapy combination (30% of all treated patients), these two populations are defined as the starting cohorts (Cohort A, immunotherapy alone, Cohort B, chemo-immunotherapy combinations). For each cohort, three main questions are to be answered: Q1) Early progressors (defined as progressive disease or death within three months of treatment or at first radiological restaging) Q2) Toxicity (with a special focus on severe toxicities G≥3) Q3) Long survivors (defined as patients reaching an overall survival of at least 1.5x median overall survival in registrative trials) The early identification of non-responders, high-risk patients (or on the other hand, long survivors) would help their healthcare planning, providing individualised follow-up strategies or prompting their inclusion in alternative treatments (eg clinical trials). For all cohorts, first data entry will be retrospective and second data entry will be prospective (as validation set).

Interventions

  • Drug Immunotherapy
    First-line regimen according to clinical practice
  • Drug Chemotherapy
    First-line regimen according to clinical practice

Primary outcome measures

  • Early progressive disease [Time frame: From date of enrolment until the date of first documented disease progression or death, whichever comes first, assessed within 8 to 12 weeks from first-line treatment start]
  • Lung toxicity [Time frame: From date of enrolment until the date of first documented immune-related pneumonitis of G3 or more, assessed up to 96 months]
  • Long survivors [Time frame: At a 3-year cut-off]

Eligibility criteria

Inclusion criteria

  • Patients with histological or cytological diagnosis of NSCLC
  • Stage IV according to investigator's staging procedures (or any locally advanced tumour not feasible for local radical treatment)
  • Treatment with at least 1 cycle of mono-immunotherapy or chemo-immunotherapy (as per clinical practice)
  • Availability of follow-up

Exclusion criteria

  • Patients with other thoracic tumours non-NSCLC (i.e. SCLC)
  • Stage other than IV or feasible for radical treatment upfront
  • Treatment within clinical trials (with combination regimens different from the aforementioned combinations)
  • Lost to follow-up

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

Italy · 1 center
  • Francesca Rita Ogliari — Milan

Identifiers

NCT: NCT06788366 · AI-HOPE

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗