CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

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.

Study Overview

• Status: Unknown
• Conditions: Nonsmall Cell Lung Cancer
• Intervention / Treatment: Diagnostic test: Virtual biopsy

• Study Type: Observational
• Enrollment (Anticipated): 650

Contacts and Locations

Study Locations

• Netherlands
  • Limburg
    • Maastricht, Limburg, Netherlands, 6229ER
      • Maastricht University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients diagnosed with NSCLC, who further underwent tissue biopsy to determine tumor histology.

Description

Inclusion Criteria:

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

Exclusion Criteria:

-

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts 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 Names: 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 Names: 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 Names: 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 Names: Radiomics-based histology prediction

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Lung histology	Is the tumor under investigation an adenocarcinoma of the lung?	December 2019

Collaborators and Investigators

• Sponsor: Maastricht University
• Collaborators: Radboud University Medical Center; University of California, San Francisco

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2019
• Primary Completion (Anticipated): October 31, 2020
• Study Completion (Anticipated): January 31, 2021

Study Registration Dates

• First Submitted: May 6, 2019
• First Submitted That Met QC Criteria: May 6, 2019
• First Posted (Actual): May 7, 2019

Study Record Updates

• Last Update Posted (Actual): April 6, 2020
• Last Update Submitted That Met QC Criteria: April 3, 2020
• Last Verified: April 1, 2019

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms by Histologic Type; Neoplasms; Lung Diseases; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Respiratory Tract Neoplasms; Thoracic Neoplasms; Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Carcinoma, Non-Small-Cell Lung; Adenocarcinoma; Adenocarcinoma of Lung
• Other Study ID Numbers: LHist

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