Clinical trial NCT04482114: Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)
Official title: Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Pulmonary Nodules, Multiple
Keywords: lung nodules
pulmonary nodules
lung cancer
radiation dose
dose reduction
low dose CT
ultra-low dose CT
DLIR
deep learning reconstruction
Lead sponsor: Centre Hospitalier Universitaire, Amiens
Sponsor class: OTHER
First posted: 2020-07-22
Enrollment: 70
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: prospective transversal monocentric study
Primary purpose: DIAGNOSTIC

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

Detailed description: * 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.

Eligibility criteria: 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

Primary outcomes:
measure: Diagnostic accuracy
timeFrame: Day 0
Secondary outcomes:
measure: Image quality
timeFrame: Day 0
measure: Pulmonary nodules volume
timeFrame: Day 0
Other outcomes:
Locations:
city: Amiens
country: France
facility: CHU Amiens-Picardie
Study officials:
References: