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At Bolder Science, we want your clinical trial search experience to be the best it can be. Complete the following prompts to easily find the trials you are interested in and see trials recruiting near you. You can adjust these selections in your dashboard after creating your account.

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CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

  • Clinicaltrials.gov identifier

    NCT03940846

  • Recruitment Status

    Active, not recruiting

  • First Posted

    May 7, 2019

  • Last update posted

    April 6, 2020

Study Description

Brief summary:

Lung cancer remains the leading cause of cancer related mortality worldwide, with more than 1.5 million related deaths annually. Lung cancer is divided into two main groups: Small Cell Lung Carcinoma (SCLC) and Non-Small Cell Lung Carcinoma (NSCLC), with prevalence of ~20% and 80% respectively. NSCLC is further subdivided into adenocarcinoma (the most common), squamous cell carcinoma (SCC), and large cell carcinoma. Furthermore, each subtype is likely to have specific mutations, which could be targeted for treatment. Medical imaging and radiomics feature extraction represent a candidate alternative to conventional tissue biopsy, a theory that is investigated in this study.

  • Condition or Disease:Nonsmall Cell Lung Cancer
  • Intervention/Treatment: Diagnostic Test: Virtual biopsy
  • Phase: N/A

Detailed Description

N/A

Study Design

  • Study Type: Observational
  • Estimated Enrollment: 650 participants
  • Observational Model: Cohort
  • Time Perspective: Retrospective
  • Official Title: CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
  • Actual Study Start Date: March 2019
  • Estimated Primary Completion Date: October 2020
  • Estimated Study Completion Date: January 2021

Groups and Cohorts

Groups/Cohorts Intervention/treatment
: Maastro (Lung1)
Open source dataset available at TCIA.org. The cohort includes CT scans of 422 patients diagnosed with NSCLC.
Diagnostic Test: 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.
Diagnostic Test: 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.
Diagnostic Test: 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.
Diagnostic Test: 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.

Outcome Measures

  • Primary Outcome Measures: 1. Lung histology [ Time Frame: December 2019 ]
    Is the tumor under investigation an adenocarcinoma of the lung?

Eligibility Criteria

  • Ages Eligible for Study: (Child, Adult, Older Adult)
  • Sexes Eligible for Study: All
  • Accepts Healthy Volunteers: No
  • Sampling Method: Non-Probability Sample
  • Study Population: Patients diagnosed with NSCLC, who further underwent tissue biopsy to determine tumor histology.

Criteria

Inclusion Criteria:

- Availability of diagnostic non-contrast enhanced CT scan.

- Availability of histologic tumor analysis results

Exclusion Criteria:

-

Contacts and Locations

Contacts

Locations

Netherlands, Limburg
Maastricht University
Maastricht

Sponsors and Collaborators

Maastricht University

University of California, San Francisco

Radboud University

More Information

  • Responsible Party: Maastricht University
  • ClinicalTrials.gov Identifier: NCT03940846 History of Changes
  • Other Study ID Numbers: LHist
  • First Posted: May 7, 2019 Key Record Dates
  • Last Update Posted: April 6, 2020
  • Last Verified: April 2019
  • Individual Participant
    Data (IPD) Sharing
    Statement:

  • Plan to Share IPD: Undecided
  • Studies a U.S. FDA-regulated Drug Product: No
  • Studies a U.S. FDA-regulated Device Product: No
  • Additional relevant MeSH terms: Adenocarcinoma
    Carcinoma, Non-Small-Cell Lung
    Adenocarcinoma of Lung