Radiomics Multifactorial Biomarker for Pulmonary Nodules

Study Overview
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Brief Summary
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The investigators aim to investigate the utility of radiomics to differentiate malignant nodules from benign nodules and invasive adenocarcinoma from non-invasive adenocarcinoma.

Detailed Description
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With the development of computed tomography (CT) equipment and the increasing use of lung cancer screening programs with low-dose CT, a growing number of early-stage lung cancers were detected so that a large number of patients have undergone surgery. Although a number of radiological studies have been used morphological signs so-called semantic features to make a differential diagnosis, it is still hard to apply by clinician because pulmonary nodules especially ground-glass nodules and small size nodules have atypical radiology signs and have strong subjectivity from different observers. Recently, CT-based radiomics, extracting the quantitative high-throughput features from medical images and facilitating clinical decision-making system, showed a good performance to predict diagnosis and prognosis of diverse cancer. Therefore, the proposed project aims to develop and validate radiomics models based on CT images to identify malignant nodules and then to discriminate the different types of lung adenocarcinoma in patients with pulmonary nodules.

Official Title
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Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study

Conditions
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Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Lung Diseases, Neoplasms, Pathology

Intervention / Treatment
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* Diagnostic Test: radiomics


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: intraoperative frozen section diagnosis and final pathology diagnosis are available preoperative standard non-enhanced CT is available Pathologically confirmed Exclusion Criteria: with a previous history of radiation therapy, chemotherapy or biopsy the time interval between the CT examination and surgery was more than two weeks

Ages Eligible for Study
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Minimum Age: 18 Years

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 |
| --- | --- |
| Training dataset<br>No interventions | Diagnostic Test: radiomics<br>* The high-throughput extraction of large amounts of quantitative image features from medical images<br>|
| External validation1<br>No interventions | Diagnostic Test: radiomics<br>* The high-throughput extraction of large amounts of quantitative image features from medical images<br>|
| External validation2<br>No interventions | Diagnostic Test: radiomics<br>* The high-throughput extraction of large amounts of quantitative image features from medical images<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Malignant nodules classifier | Model based on Radiomic that can differentiate malignant nodules from benign nodules. | 30 days | 
| Invasive adenocarcinoma classifier | Model based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma. | 30 days | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Lung cancer, CT, Frozen sections, Radiomics, Lung adenocarcinoma