CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

Study Overview
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Brief Summary
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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.

Official Title
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CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology

Conditions
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Nonsmall Cell Lung Cancer

Intervention / Treatment
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* Diagnostic Test: Virtual biopsy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Availability of diagnostic non-contrast enhanced CT scan. Availability of histologic tumor analysis results Exclusion Criteria: -
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Maastro (Lung1)<br>Open source dataset available at TCIA.org. The cohort includes CT scans of 422 patients diagnosed with NSCLC. | Diagnostic Test: Virtual biopsy<br>* 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.<br>* Other names: Radiomics-based histology prediction;|
| UCSF<br>A cohort of patients diagnosed with NSCLC at UCSF medical center. It includes CT scans of 165 patients. | Diagnostic Test: Virtual biopsy<br>* 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.<br>* Other names: Radiomics-based histology prediction;|
| Radboud<br>A cohort of patients diagnosed with NSCLC at Radboud medical center. It includes CT scans of 255 patients. | Diagnostic Test: Virtual biopsy<br>* 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.<br>* Other names: Radiomics-based histology prediction;|
| Stanford<br>Open source dataset available at TCIA.org. The cohort includes CT scans of 211 patients diagnosed with NSCLC. | Diagnostic Test: Virtual biopsy<br>* 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.<br>* Other names: Radiomics-based histology prediction;|

What is the study measuring?
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Primary Outcome Measures

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