Alk-positive Non-Small Cell Lung CancerALKDigPathNCT06846736
ALK Digital Pathology Outcome Predition, Multi Institutional, Restrospective Study
An observational study of Alk-positive Non-Small Cell Lung Cancer and ALK-inhibitor Treated, run by Sheba Medical Center at 1 site in Israel.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Alk-positive Non-Small Cell Lung Cancer, ALK-inhibitor Treated
- How many people
- 200 participants
- Where
- 1 siteIsrael
- Study dates
- 13 Aug 2023to 1 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: ALK positive patients with advanced NSCLC that have received an advanced-generation ALK inhibitor treatment as the first ALK inhibitor
Who may be able to join
- 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)
Who cannot join
- Absence of information on the last oncologic treatment received;
- Patient without a general or specific consent for this study
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read the detailed description
This is a retrospective study. All data have been collected at different time points during the patients' routine visits at the hospital.
- 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
- 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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Feb 2025
- Study start13 Aug 2023
- Primary completion1 Dec 2027 (estimated)
- Study completion1 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Alk-positive Non-Small Cell Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sheba Medical Center
- Sponsor type
- Other government
- Collaborators
- Mayo Clinic, Gustave Roussy, Cancer Campus, Grand Paris
- Responsible party
- Sponsor
- Investigators
- Jair Bar, MD-PhD, Principal investigator, Sheba Medical Cernter
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06846736
- Sponsor's protocol ID
- SMC-9969-22
- Acronym
- ALKDigPath
- Status last verified
- Aug 2024
- First submitted
- 20 Feb 2025
- Last updated
- 26 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Based on inidividual requests
Study at a glance
From the registry record, last updated by the sponsor on 26 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ALK Digital Pathology Outcome Prediction
- ClinicalTrials.gov ID
- NCT06846736
- Acronym / sponsor ID
- ALKDigPath / SMC-9969-22
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Sheba Medical CenterOther government
- Collaborators
- Mayo Clinic, Gustave Roussy, Cancer Campus, Grand Paris
- Study start
- 13 Aug 2023 (actual)
- Primary completion
- 1 Dec 2027 (estimated)
- Study completion
- 1 Dec 2028 (estimated)
- First posted
- 26 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This is a retrospective study. All data have been collected at different time points during the patients' routine visits at the hospital.
- 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
- 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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- ALK positive patients with advanced NSCLC that have received an advanced-generation ALK inhibitor treatment as the first ALK inhibitor
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (2 listed)
- Absence of information on the last oncologic treatment received;
- Patient without a general or specific consent for this study
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
A classifier predicting outcome for advanced NSCLC ALK+ patients on ALK inhibitor treatment
Time frame: 36 months
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Feb 2025.
- Individual participant dataSharing planned: Yes. Based on inidividual requests
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jair Bar, MD-PhD principal investigator
Sheba Medical Cernter
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Alk-positive Non-Small Cell Lung Cancer and ALK-inhibitor Treated, run by Sheba Medical Center at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 26 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
A classifier predicting outcome for advanced NSCLC ALK+ patients on ALK inhibitor treatment (time frame: 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Feb 2025.