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

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

Study Overview

• Status: Active, not recruiting
• Conditions: Pulmonary Nodules, Multiple
• Intervention / Treatment: Radiation: ULD CT

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.

• Study Type: Interventional
• Enrollment (Actual): 70
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Cédric Renard, Dr; Phone Number: 0322087500; Email: renard.cedric@chu-amiens.fr
• Study Contact Backup: Name: Salah Mattoug; Phone Number: 0322088385; Email: mattoug.salah@chu-amiens.fr

Study Locations

• France
  • Amiens, France, 80000
    • CHU Amiens-Picardie

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: ultra-low dose CT; 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; 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.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire, Amiens

Study record dates

Study Major Dates

• Study Start (Actual): July 22, 2020
• Primary Completion (Actual): July 1, 2022
• Study Completion (Anticipated): July 1, 2023

Study Registration Dates

• First Submitted: July 15, 2020
• First Submitted That Met QC Criteria: July 20, 2020
• First Posted (Actual): July 22, 2020

Study Record Updates

• Last Update Posted (Actual): March 22, 2023
• Last Update Submitted That Met QC Criteria: March 21, 2023
• Last Verified: March 1, 2023

More Information

Terms related to this study

• Keywords: lung cancer; dose reduction; lung nodules; radiation dose; pulmonary nodules; low dose CT; ultra-low dose CT; DLIR; deep learning reconstruction
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Multiple Pulmonary Nodules
• Other Study ID Numbers: PI2020_843_0052

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No