ALK Digital Pathology Outcome Predition, Multi Institutional, Restrospective Study

NCT06846736 | Enrolling By Invitation

• 1 other identifier: SMC-9969-22
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

The Goal of this observational study is to develop an AI-driven pathologic image analysis-based classifier that can identify patients unlikely to significantly benefit from the currently utilized first-line ALK inhibitors (advanced-generation ALK inhibitors). Our goal is a classifier with final ROC-AUC value of 0.75.

Conditions

Alk-positive Non-Small Cell Lung Cancer; ALK-inhibitor Treated

Outcome Measures

Primary Outcomes (1)

• A classifier predicting outcome for advanced NSCLC ALK+ patients on ALK inhibitor treatment

  A classifier predicting outcome for advanced NSCLC ALK+ patients on ALK inhibitor treatment, based on AI analysis of digital pathology images of the diagnostic biopsy

  36 months

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

ALK positive patients with advanced NSCLC that have received an advanced-generation ALK inhibitor treatment as the first ALK inhibitor

You may qualify if:

• Patient aged ≥ 18 years;
• Patient with an oncologic disease;
• ALK positive patients with advanced NSCLC that have received an advanced-generation ALK inhibitor treatment as the first ALK inhibitor (i.e. alectinib, lorlatinib, brigatinib or ceritinib)

You may not qualify if:

• Absence of information on the last oncologic treatment received;
• Patient without a general or specific consent for this study

Sponsors & Collaborators

• Sheba Medical Center: lead
• Mayo Clinic: collaborator
• Gustave Roussy, Cancer Campus, Grand Paris: collaborator

Study Sites (1)

Sheba Medical Center

Ramat Gan, 5262000, Israel

Location

Study Officials

• Jair Bar, MD-PhD

  Sheba Medical Cernter

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 20, 2025
• First Posted: February 26, 2025
• Study Start: August 13, 2023
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2028
• Last Updated: February 26, 2025

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will share

Locations

IL (1)