ALK Digital Pathology Outcome Predition, Multi Institutional, Restrospective Study (ALKDigPath)

ALK Digital Pathology Outcome Prediction

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Alk-positive Non-Small Cell Lung Cancer; ALK-inhibitor Treated

Detailed Description

This is a retrospective study. All data have been collected at different time points during the patients' routine visits at the hospital.

1. Collection of a retrospective set of 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): collection of the clinical data, pathologic data, response to treatment and scans H&E images
2. Image analysis of the scanned H&E images, development of a classifier of the data to identify responders vs. non-responders.

Image analysis and AI development will be carried out at the Sheba Medical Center, in-house development. The clinical data will be analyzed, tagging study samples as belonging to a responder (R), vs. a non-responder (NR). For the purpose of this study, a NR will be defined as a patient that has progressed or died on an ALK inhibitor treatment within the first year of treatment.

The study cases will be randomly split to three: a training cohort, a validation cohort and a test cohort. The cohorts will be stratified by the response to treatment (i.e. equal proportion of R vs. NR cases in each cohort). Next, scanned images will be processed and analyzed. Slides analysis would be done using python using the pytorch packages. Further statistical analysis will be done with R statistical programming.

At first the whole slide image (WSI) is divided into thousands of tiles. These are examined by a convolutional neural network (CNN) to extract tile level features. We will be using Resnet, a common deep learning model used for computer vision as the CNN. The CNN will be trained with multiple instance learning (MIL) at the tile level and later the predicted scores will be aggregated for the WSI level . The final model will be conducted on the slides, to distinguish between R vs. NR. The classifier will be developed on the training cohort, modified if required following processing of the validation cohort and finally tested for efficacy on the test cohort. Cross-Validations techniques will also be used.

We aim to use this technique in order identify a sub-group of ALK positive patients that might be candidates for more aggressive treatment options.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

Study Locations

• Israel
  • Ramat Gan, Israel, 5262000
    • Sheba Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

Description

Inclusion Criteria:

• 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)

Exclusion Criteria:

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Sheba Medical Center
• Collaborators: Mayo Clinic; Gustave Roussy, Cancer Campus, Grand Paris
• Investigators: Principal Investigator: Jair Bar, MD-PhD, Sheba Medical Cernter

Study record dates

Study Major Dates

• Study Start (Actual): August 13, 2023
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: February 20, 2025
• First Submitted That Met QC Criteria: February 20, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 20, 2025
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Carcinoma, Bronchogenic; Bronchial Neoplasms; Carcinoma, Non-Small-Cell Lung
• Other Study ID Numbers: SMC-9969-22

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Based on inidividual requests

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No