Image Mining and ctDNA to Improve Risk Stratification and Outcome Prediction in NSCLC Applying Artificial Intelligence.

NCT06163846 | Unknown DMC

• 1 other identifier: AIRC_IG_2019_23596
• Study Type: observational
• Target: 415
• Locations: 1 country
• Sites: 1

Brief Summary

Lung cancer is the leading cause of cancer-related death in Europe. Pathological staging is the gold standard, but it can be influenced by neo-adjuvant treatment and number of sampled lymph nodes; it is not feasible in advanced stages and in patients with high-risk comorbidities. Therefore, patients with tumors of the same stage can experience variations in the incidence of recurrence and survival since suboptimal staging leads to inappropriate treatment that result in poorer outcomes. It is still undetermined what are the tumor characteristics that can accurately assess tumor burden and predict patient outcome.Our central hypothesis is that image-derived and genetic characteristics are consistent with disease stage and patient outcome. Combining through artificial intelligence techniques data coming from imaging and circulating cell-free tumor DNA (ctDNA) can provide accurate staging and predict outcome. This hypothesis has been formulated based on preliminary data and on the evidence that image-derived biomarkers by means of image mining (radiomics and deep learning algorithms) are able to provide "phenotype" and prognostic information. On the other hand, the analysis of ctDNA isolated from the plasma of patients has been proposed as an alternative method to assess the disease in the different phases, in particular, at diagnosis and after surgery, for detection of residual disease.

Conditions

Non Small Cell Lung Cancer

Outcome Measures

Primary Outcomes (1)

• Artificial intelligence and circulating cell-free tumor DNA (ctDNA) for the staging and predict outcome in patients with with non-small cell lung cancer.

  Evaluate the prognostic role of advanced image analysis, ctDNA and their combination.

  5 years

Interventions

Assess the role of baseline image mining, ctDNA data and their combination in patient staging and risk stratification DIAGNOSTIC_TEST

Assess the combination of baseline and follow-up image mining, together with ctDNA, in predicting disease relapse and progression.

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This clinical research protocol will be an observational, prospective, bicentric, single-arm study. Patients newly diagnosed with non-small cell lung cancer will be eligible. 415 patients will be enrolled (of which 170 at San Raffaele Hospital). At the time of enrollment all eligible patients will sign the informed consent.

You may not qualify if:

• pregnant or breast- feeding women.

Sponsors & Collaborators

• IRCCS San Raffaele: lead

Study Sites (1)

Irccs San Raffaele

Milan, 20132, Italy

RECRUITING

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Alessandra Maielli

CONTACT

0226433639; maielli.alessandra@hsr.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor in Diagnostic Imaging and Radiotherapy Faculty of Medicine and Surgery, Vita-Salute San Raffaele University Director, Department of Nuclear Medicine, IRCCS Ospedale San Raffaele

Study Record Dates

• First Submitted: November 23, 2023
• First Posted: December 11, 2023
• Study Start: July 10, 2020
• Primary Completion: December 10, 2020
• Study Completion: June 1, 2025
• Last Updated: December 20, 2023

Record last verified: 2023-12

Locations

IT (1)