- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06163846
Image Mining and ctDNA to Improve Risk Stratification and Outcome Prediction in NSCLC Applying Artificial Intelligence.
December 13, 2023 updated by: Chiti Arturo, IRCCS San Raffaele
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.
Study Overview
Status
Recruiting
Conditions
Detailed Description
Our central hypothesis is that peculiar image-derived and genetic characteristics are consistent with disease stage and patient outcome.
Therefore, they are biomarkers of disease burden and relapse, which can be non- invasively assessed.
The combination through artificial intelligence methods of data coming from medical imaging and circulating cell-free tumor DNA (ctDNA) can provide accurate staging and outcome prediction.
This hypothesis has been formulated based on the evidence that medical images are able to provide meanable data reflecting tumor characteristics, capturing intrinsic tumor heterogeneity, non-invasively, using a whole- body and whole-lesion assessment.
In fact, in recent years, advanced analysis of medical imaging using radiomics, machine learning or in combination - image mining, has been explored.
Image- derived biomarkers, by means of texture feature extraction and convolutional neural network application, have been tested to provide "phenotype" information (malignant vs benign, and histotype identification, and T or N staging.
Moreover, correlations between image-derived quantitative features with tissue gene-expression patterns have been shown, linking the imaging phenotypes to the genotype as also demonstrated in our preliminary data.
Secondly, image mining approach has been proposed to provide prognostic information at baseline evaluation, as also shown in our previous work.
Still, few prospective studies with robust methodological approach have been published.
On the other hand, the analysis of circulating cell-free tumor DNA (ctDNA) isolated from the plasma of lung cancer patients has been proposed as an alternative method to assess the disease in the different phases.
In particular, at diagnosis, the post-surgical detection of residual disease, the identification of mutations in the metastatic setting for treatment guidance and monitoring treatment response.
Even if, ctDNA has been detected in patients with all stages of NSCLC with levels increasing with stage and tumor burden ctDNA information has not been explored yet for the purpose of staging.
The possibility to detect a tumor in the early phase of its development or the recurrence has to face the issue of the low amount of cfDNA in patients with minimal disease burden.
Moreover, the presence of a para-physiological ctDNA background particularly in aged people affects the specificity.
In this respect, the investigators expect that the combination of different biomarkers will allow to solve this problem.Artificial Intelligence analytics are increasingly described in healthcare applications.
In recent years, supervised, semi-supervised, and unsupervised machine learning methods have been applied to analyze genomic, proteomic, clinical data and radiographical characteristics.
Deep learning methods offer opportunities for comprehensive analysis of multi-dimensional data for improved prognosis prediction.
The rationale for the proposed project is that, once it is known which imaging features and ctDNA-derived information is linked to the tumor stage and post-operative risk of relapse, the developed algorithm will be an effective and innovative approach for both staging and follow-up of patients affected by lung cancer, with implications on decision-making in clinical practice.
Study Type
Observational
Enrollment (Estimated)
415
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Alessandra Maielli
- Phone Number: 0226433639
- Email: maielli.alessandra@hsr.it
Study Locations
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-
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Milano, Italy, 20132
- Recruiting
- IRCCS San Raffaele
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Contact:
- Arturo Chiti
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
N/A
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.
Description
Inclusion Criteria:
- Patients with new pathological diagnosis of lung cancer, available baseline imaging (CT and FDG-PET/CT), age > 18 years, and eligibility for surgery will be considered for inclusion.
Exclusion Criteria:
- pregnant or breast- feeding women.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Artificial intelligence and circulating cell-free tumor DNA (ctDNA) for the staging and predict outcome in patients with with non-small cell lung cancer.
Time Frame: 5 years
|
Evaluate the prognostic role of advanced image analysis, ctDNA and their combination.
|
5 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
July 10, 2020
Primary Completion (Actual)
December 10, 2020
Study Completion (Estimated)
June 1, 2025
Study Registration Dates
First Submitted
November 23, 2023
First Submitted That Met QC Criteria
December 1, 2023
First Posted (Actual)
December 11, 2023
Study Record Updates
Last Update Posted (Estimated)
December 20, 2023
Last Update Submitted That Met QC Criteria
December 13, 2023
Last Verified
December 1, 2023
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AIRC_IG_2019_23596
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
UNDECIDED
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Non Small Cell Lung Cancer
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WindMIL TherapeuticsBristol-Myers SquibbTerminatedNSCLC | Lung Cancer | Lung Cancer Metastatic | Lung Cancer, Non-small Cell | Non Small Cell Lung Cancer | Non-small Cell Lung Cancer | Non-small Cell Lung Cancer Metastatic | Non Small Cell Lung Cancer MetastaticUnited States
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University of California, San FranciscoAstraZenecaActive, not recruitingStage IIIA Non-Small Cell Lung Cancer | Stage I Non-Small Cell Lung Cancer | Stage IA Non-Small Cell Lung Cancer | Stage IB Non-Small Cell Lung Cancer | Stage II Non-Small Cell Lung Cancer | Stage IIA Non-Small Cell Lung Cancer | Stage IIB Non-Small Cell Lung CancerUnited States
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AIO-Studien-gGmbHBristol-Myers Squibb; Eli Lilly and Company; Merck Sharp & Dohme LLC; Pfizer; Gilead... and other collaboratorsRecruitingSmall-cell Lung Cancer | Non-small Cell Lung Cancer Metastatic | Non-small Cell Lung Cancer Stage I | Metastatic Non-small Cell Lung Cancer (NSCLC) | Non Small Cell Lung Cancer Stage III | Non-small Cell Lung Cancer Stage IIGermany
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University of Wisconsin, MadisonNational Cancer Institute (NCI)CompletedStage IIIA Non-small Cell Lung Cancer | Stage IIIB Non-small Cell Lung Cancer | Extensive Stage Small Cell Lung Cancer | Recurrent Small Cell Lung Cancer | Recurrent Non-small Cell Lung Cancer | Stage IV Non-small Cell Lung Cancer | Healthy, no Evidence of Disease | Limited Stage Small Cell Lung... and other conditionsUnited States
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National Cancer Institute (NCI)TerminatedStage IIIA Non-small Cell Lung Cancer | Stage IA Non-small Cell Lung Cancer | Stage IB Non-small Cell Lung Cancer | Stage IIA Non-small Cell Lung Cancer | Stage IIB Non-small Cell Lung CancerUnited States
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Alexander ChiNot yet recruitingNon-small Cell Lung Cancer Stage III | Non-small Cell Lung Cancer | Non-small Cell Lung Cancer Stage I | Non-small Cell Carcinoma | Non-small Cell Lung Cancer Stage IIChina
-
National Cancer Institute (NCI)Not yet recruitingStage IIIA Non-small Cell Lung Cancer | Stage IA Non-small Cell Lung Cancer | Stage IB Non-small Cell Lung Cancer | Stage IIA Non-small Cell Lung Cancer | Stage IIB Non-small Cell Lung CancerCanada
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Karen KellyBristol-Myers Squibb; National Cancer Institute (NCI); TransgeneCompletedStage IIIA Non-Small Cell Lung Cancer | Stage IIIB Non-Small Cell Lung Cancer | Recurrent Non-Small Cell Lung Carcinoma | Stage IV Non-Small Cell Lung Cancer | Stage I Non-Small Cell Lung Cancer | Stage II Non-Small Cell Lung CancerUnited States
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Stanford UniversityAstraZenecaRecruitingNon-small Cell Lung Cancer Stage III | Non-small Cell Lung Cancer | Non-small Cell Lung Cancer Stage I | Non-small Cell Lung Cancer Stage IIUnited States
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Ohio State University Comprehensive Cancer CenterActive, not recruitingStage IIIA Non-small Cell Lung Cancer | Stage IIIB Non-small Cell Lung Cancer | Recurrent Non-small Cell Lung Cancer | Stage IIA Non-small Cell Lung Cancer | Stage IIB Non-small Cell Lung CancerUnited States