Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)

Study Overview
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Brief Summary
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evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules.

Detailed Description
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Background: Lung cancer is the leading cause of cancer deaths. Patients with pulmonary nodules often undergo multiple computed tomography (CT) examinations for diagnostic and follow-up purposes. Purpose: The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary nodules. Abstract: Despite recent advances, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer death worldwide because it is often diagnosed at advanced stages that are not surgically curable. Nevertheless, early detection of lung cancer allows surgical resection, offers curative treatment and the best chance of survival. There is currently no screening program in France, but individual screening can be carried out depending on risk factors. Many pulmonary nodules are discovered each year, most of which are benign. The challenge is to distinguish malignant lesions from the multitude of benign lesions. One of the most effective criteria is the doubling time of the nodules which leads to multiple follow-up examinations requiring ionizing radiation to assess the size and growth of the nodules. Great efforts are currently being made by CT manufacturers in order to reduce the radiation with equivalent diagnostic performance. Patients who were referred to our department for an unenhanced low-dose chest CT (LD CT) for pulmonary nodules check-up or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULDCT, <0,25 mSv, similar to standard two-view chest X-Ray) with deep learning-based reconstruction (DLIR). The main objective of this study is to evaluate the diagnostic performance between ULD and LD CT protocols for the detection of pulmonary nodules. The impact of dose reduction will be assessed in this context. The data from each examination will be blindly interpreted from the results of the other one. No follow-up will be required for the study.

Official Title
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Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)

Conditions
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Pulmonary Nodules, Multiple

Intervention / Treatment
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* Radiation: ULD CT


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age ≥ 18 years old, Patient referred for non-enhanced chest CT for lung nodule check-up or follow-up, Affiliation to a social security program, Ability of the subject to understand and express opposition Exclusion Criteria: Age <18 years old, Person under guardianship or curatorship, Pregnant woman, Any contraindications to CT

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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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional Model Description: prospective transversal monocentric study
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Other: ultra-low dose CT<br>All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination. | Radiation: ULD CT<br>* All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic accuracy | The study aimed to investigate the diagnostic accuracy (Sensibility and Specificity) of ultra-low dose CT using DLIR reconstruction for the detection of pulmonary nodules in comparison with the low dose CT reference protocol. | Day 0 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Image quality | The signal-to-noise ratio or SNR is calculated on areas of interest placed manually on the image (pulmonary parenchyma, axillary fat and surrounding air). This ratio is calculated by the average signal strength in these areas, divided by the standard deviation of the signal in outdoor areas such as the surrounding air. The quality of the image is estimated by a score ranging from 0 (poor quality) to 3 (excellent quality) determined subjectively by the operator. | Day 0 | 
| Pulmonary nodules volume | Difference of pulmonary nodules volume between images acquired at low dose CT and ultra-low dose CT. | Day 0 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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lung nodules, pulmonary nodules, lung cancer, radiation dose, dose reduction, low dose CT, ultra-low dose CT, DLIR, deep learning reconstruction