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

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

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06788366
Enrollment
2000
Registered
2025-01-23
Start date
2024-02-12
Completion date
2026-12-31
Last updated
2025-01-23

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

NSCLC Stage IV

Brief summary

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

DRUGImmunotherapy

First-line regimen according to clinical practice

DRUGChemotherapy

First-line regimen according to clinical practice

Sponsors

IRCCS San Raffaele
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Early progressive diseaseFrom 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 startNumber of patients experiencing progressive disease (PD) as best response to first-line treatment
Lung toxicityFrom date of enrolment until the date of first documented immune-related pneumonitis of G3 or more, assessed up to 96 monthsNumber of patients experiencing immune-related pneumonitis of G3 or more
Long survivorsAt a 3-year cut-offNumbero of patients experiencing an overall survival (time from treatment initiation to death) longer than 3 years (1.5x median overall survival from clinical trials)

Countries

Italy

Contacts

Primary ContactFrancesca Rita Ogliari, MD
oncologia.medica@hsr.it0039 02 2643 2643
Backup ContactClinical Trial Center OSR
ctc.trialmanagement@hsr.it

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026