4006-776-356 出国就医服务电话

免费获得国外相关药品，最快 1 个工作日回馈药物信息

出境医 / 临床实验 / CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

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 Intervention/treatment
Nonsmall Cell Lung Cancer Diagnostic Test: Virtual biopsy

Study Design
Layout table for study information
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 1, 2019
Estimated Primary Completion Date : October 31, 2020
Estimated Study Completion Date : January 31, 2021
Arms and Interventions
Group/Cohort 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.
Other Name: Radiomics-based histology prediction
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.
Other Name: Radiomics-based histology prediction
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.
Other Name: Radiomics-based histology prediction
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.
Other Name: Radiomics-based histology prediction
Outcome Measures
Primary Outcome Measures :
  1. Lung histology [ Time Frame: December 2019 ]
    Is the tumor under investigation an adenocarcinoma of the lung?


Eligibility Criteria
Layout table for eligibility information
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

Locations
Layout table for location information
Netherlands
Maastricht University
Maastricht, Limburg, Netherlands, 6229ER
Sponsors and Collaborators
Maastricht University
University of California, San Francisco
Radboud University
Tracking Information
First Submitted Date May 6, 2019
First Posted Date May 7, 2019
Last Update Posted Date April 6, 2020
Actual Study Start Date March 1, 2019
Estimated Primary Completion Date October 31, 2020   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: May 6, 2019) 		Lung histology [ Time Frame: December 2019 ]
Is the tumor under investigation an adenocarcinoma of the lung?
Original Primary Outcome Measures Same as current
Change History
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures Not Provided
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
Official Title CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
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.
Detailed Description Not Provided
Study Type Observational
Study Design Observational Model: Cohort
Time Perspective: Retrospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
Sampling Method Non-Probability Sample
Study Population Patients diagnosed with NSCLC, who further underwent tissue biopsy to determine tumor histology.
Condition Nonsmall Cell Lung Cancer
Intervention 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.
Other Name: Radiomics-based histology prediction
Study Groups/Cohorts
  • Maastro (Lung1)
    Open source dataset available at TCIA.org. The cohort includes CT scans of 422 patients diagnosed with NSCLC.
    Intervention: Diagnostic Test: Virtual biopsy
  • UCSF
    A cohort of patients diagnosed with NSCLC at UCSF medical center. It includes CT scans of 165 patients.
    Intervention: Diagnostic Test: Virtual biopsy
  • Radboud
    A cohort of patients diagnosed with NSCLC at Radboud medical center. It includes CT scans of 255 patients.
    Intervention: Diagnostic Test: Virtual biopsy
  • Stanford
    Open source dataset available at TCIA.org. The cohort includes CT scans of 211 patients diagnosed with NSCLC.
    Intervention: Diagnostic Test: Virtual biopsy
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Unknown status
Estimated Enrollment
 (submitted: May 6, 2019) 		650
Original Estimated Enrollment Same as current
Estimated Study Completion Date January 31, 2021
Estimated Primary Completion Date October 31, 2020   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

  • Availability of diagnostic non-contrast enhanced CT scan.
  • Availability of histologic tumor analysis results

Exclusion Criteria:

-
Sex/Gender
Sexes Eligible for Study: All
Ages Child, Adult, Older Adult
Accepts Healthy Volunteers No
Contacts Contact information is only displayed when the study is recruiting subjects
Listed Location Countries Netherlands
Removed Location Countries  
 
Administrative Information
NCT Number NCT03940846
Other Study ID Numbers LHist
Has Data Monitoring Committee Yes
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: Undecided
Responsible Party Maastricht University
Study Sponsor Maastricht University
Collaborators
  • University of California, San Francisco
  • Radboud University
Investigators Not Provided
PRS Account Maastricht University
Verification Date April 2019

治疗医院

新药特效药

治疗案例

前沿医讯