This is the classic website, which will be retired eventually. Please visit the modernized ClinicalTrials.gov instead.
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

Artificial Intelligence for Help Non-Small Cell Lung Cancer: Measure Cancer Biology and Treatment Response Via Imaging (SALMON)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05254132
Recruitment Status : Not yet recruiting
First Posted : February 24, 2022
Last Update Posted : February 24, 2022
Sponsor:
Collaborator:
University Hospital, Antwerp
Information provided by (Responsible Party):
OncoRadiomics

Brief Summary:

SALMON is a prospective, multi-center, multi-country, biomarker validation study that synergizes an extensive non-interventional biomarker discovery study on diagnostic images and tissue biopsies of non-small cell lung cancer NSCLC (rATLAS) with a smaller biomarker minimally interventional study on patients with metastases who undergo liquid biopsy and imaging follow-up for 2 years (aRECIST). A total of 1120 patients will be screened to get 1000 participants enrolled in rATLAS, and a subset of 250 participants will be screened to then recruit 150 participants also for aRECIST. The study will end after one visit for participants in rATLAS while there is a 2-years follow-up period for participants in aRECIST. Participants will not receive any treatment specific for this study, but might receive standard of care therapy or investigational products in the framework of another clinical study following the baseline visit.

The objectives of optimizing AI based tools for the assessment of EGFR status (rATLAS) and automated Response Evaluation Criteria in Solid Tumours 1.1 (RECIST 1.1) (aRECIST) will be achieved using a trial design that combines a biomarker discovery study design (cross-sectional for rATLAS) with a reader study design (follow-up study in aRECIST). Medical treatments in the aRECIST cohort are not dictated by study protocol, rather determined by the clinicians in line with standard clinical practice.


Condition or disease Intervention/treatment
Non Small Cell Lung Cancer Procedure: Liquid biopsy

Show Show detailed description

Layout table for study information
Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Artificial Intelligence to Help Non-Small Cell Lung Cancer Patients: Measure Lung Cancer Biology and Treatment Response Via Imaging
Estimated Study Start Date : July 1, 2022
Estimated Primary Completion Date : June 30, 2025
Estimated Study Completion Date : June 30, 2025

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
aRECIST
Treatment naive patients with metastatic NSCLC (stage four) with life expectancy of more than three months.
Procedure: Liquid biopsy
Participants in the aRECIST group will undergo a blood draw for liquid biopsy analysis at baseline and follow up visits

rATLAS
Treatment naive patients diagnosed with NSCLC.



Primary Outcome Measures :
  1. Equivalence between aRECIST and manual RECIST in the evaluation of target lesions at time of study enrolment [ Time Frame: At time of study enrolment ]
    Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

  2. Equivalence between aRECIST and manual RECIST in the evaluation of target lesions at month 3 [ Time Frame: Month 3 ]
    Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

  3. Equivalence between aRECIST and manual RECIST in the evaluation of target lesions at month 6 [ Time Frame: Month 6 ]
    Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

  4. Equivalence between aRECIST and manual RECIST in the evaluation of target lesions at month 12 [ Time Frame: Month 12 ]
    Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

  5. Equivalence between aRECIST and manual RECIST in the evaluation of target lesions at month 24 [ Time Frame: Month 24 ]
    Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

  6. Identification of imaging biomarkers that discriminate EGFR status in patients with NSCLC with a minimum area under the curve of 0.65 [ Time Frame: At time of study enrolment ]
    A radiomics-based EGFR mutation prediction model will be trained and tested. The EGFR mutation prediction model is considered successful if its AUC of ROC is ≥ 0.65 in the independent test set of 200 patients.


Secondary Outcome Measures :
  1. Reduction in diagnostic time and inter-reader variability compared to manual RECIST (local reading) in determining therapeutic response on target lesions. [ Time Frame: Month 24 ]
    Statistical testing on the performance difference between models predicting survival at 24 months will be performed to compare the prognostic value of aRECIST to that of RECIST. Categorical similarity measures between central panel RECIST and local RECIST will be computed through Cohen's kappa coefficient. Inter-reader agreement within the central panel will be computed trough the kappa coefficient.

  2. Identification of imaging biomarkers that correlate with major oncogenic biomarkers to help guide drug development and therapy choice in NSCLC, with a minimum AUC of 0.65 [ Time Frame: At time of study enrolment ]
    This is assessed by comparing biomarker results from solid biopsies with CT-scan imaging features collected at the baseline visit. A customized radiomics approach to identify statistically significant differences in radiomics features between numerous genomic/biological statuses will be used to map the rATLAS.


Biospecimen Retention:   Samples With DNA
Blood sample for liquid biopsy analysis (DNA and RNA analysis, immunohistochemistry) Tumor biopsy sample (DNA and RNA analysis, immunohistochemistry)


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
The multi-racial population of rATLAS will be prospectively recruited from 35 different EU sites. No gender predilection will be applied, so that the final population could represent a real spectrum of the population affected by NSCLC. The aRECIST cohort will include stage IV NSCLC patients recruited from 10 different sites.
Criteria

Inclusion Criteria:

  • Participant must be aged at least 18 years
  • Willing and able to comply with clinic visits and study-related procedures.
  • Willing and able to provide signed informed consent.
  • Participant must be at first diagnosis of NSCLC and have the largest diameter of the primary tumor equal or greater than 2 cm.
  • Participant must be treatment naïve (includes radiotherapy).
  • Participant must have received a CT scan for the diagnosis of NSCLC according to "Imaging Protocol" document (Appendix 1).
  • Participant with confirmed availability of representative tumor specimens in formalin-fixed, paraffin-embedded (FFPE) blocks or ≥25 unstained slides (at least 10 unstained slides). Participant without adequate archival tumor specimens cannot be included

Additional inclusion criteria specific to aRECIST cohort:

  • Participant must be diagnosed with NSCLC Stage IV.
  • Participant must have a life expectancy ≥ 3 months.
  • Participant must have at least one lesion that is suitable for accurate repeated assessment (according to RECIST criteria).
  • Participant must be able to comply with standard of care visits for imaging purposes to follow-up on treatment response.
  • Participant must need to agree to undergo a liquid biopsy at baseline and at follow-up visits.
  • Participant must undergo either chemotherapy or immunotherapy after baseline visit, according to SoC.

Exclusion Criteria:

  • Pregnant or breast-feeding participants (to avoid radiation exposure)
  • Participant is either an employee of Radiomics or the investigational center or an immediate relative of an employee of Radiomics or the investigational center.
  • Participant with total body CT scan already performed at a different site with acquisition parameters different from those reported in the Imaging Protocol

Additional inclusion criteria specific to aRECIST cohort:

• Participant who previously underwent or are planned for curable cancer surgery (lobectomy, wedge resection, pneumonectomy) or ablative radiotherapy on metastases.


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05254132


Contacts
Layout table for location contacts
Contact: Lise Barbeaux +3242292280 lise.barbeaux@radiomics.bio
Contact: Mariaelena Occhipinti, MD PhD +3242292280 mariaelena.occhipinti@radiomics.bio

Sponsors and Collaborators
OncoRadiomics
University Hospital, Antwerp
Investigators
Layout table for investigator information
Principal Investigator: Jan P Van Meerbeeck, MD University Hospital, Antwerp
Publications:

Layout table for additonal information
Responsible Party: OncoRadiomics
ClinicalTrials.gov Identifier: NCT05254132    
Other Study ID Numbers: 0060RDX
First Posted: February 24, 2022    Key Record Dates
Last Update Posted: February 24, 2022
Last Verified: February 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by OncoRadiomics:
AI
Radiomics
Oncology
RECIST
Lung cancer
Medical imaging
Imaging biomarkers
Immunotherapy
iRECIST
Additional relevant MeSH terms:
Layout table for MeSH terms
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases
Carcinoma, Bronchogenic
Bronchial Neoplasms