CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
NCT ID: NCT03940846
Last Updated: 2020-04-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
650 participants
OBSERVATIONAL
2019-03-01
2021-01-31
Brief Summary
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Medical imaging and radiomics feature extraction represent a candidate alternative to conventional tissue biopsy, a theory that is investigated in this study.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Maastro (Lung1)
Open source dataset available at TCIA.org. The cohort includes CT scans of 422 patients diagnosed with NSCLC.
Virtual biopsy
Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
UCSF
A cohort of patients diagnosed with NSCLC at UCSF medical center. It includes CT scans of 165 patients.
Virtual biopsy
Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
Radboud
A cohort of patients diagnosed with NSCLC at Radboud medical center. It includes CT scans of 255 patients.
Virtual biopsy
Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
Stanford
Open source dataset available at TCIA.org. The cohort includes CT scans of 211 patients diagnosed with NSCLC.
Virtual biopsy
Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
Interventions
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Virtual biopsy
Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* Availability of histologic tumor analysis results
Exclusion Criteria
ALL
No
Sponsors
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University of California, San Francisco
OTHER
Radboud University Medical Center
OTHER
Maastricht University
OTHER
Responsible Party
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Locations
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Maastricht University
Maastricht, Limburg, Netherlands
Countries
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Other Identifiers
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LHist
Identifier Type: -
Identifier Source: org_study_id
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