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Lung cancer (LC) screening using low-dose chest CT (LDCT) has already proven its efficacy.
The mortality reduction associated with LC screening is around 20%, much higher than the reduction in mortality associated with screening for breast, colon or prostate cancers.
Implementing lung cancer screening on a large scale faces two main obstacles:
The gold standard for determining on the benign or malignant nature of a nodule is definitive histology. Otherwise, the evolution of the nodule on serial thoracic imaging is a good alternative. The period of indeterminacy of a nodule can be as long as 24 months in many cases, which can be a source of prolonged and sometimes unjustified anxiety for screening candidates.
The purpose of this randomized controlled study that focuses on LC screening in patients aged 50 to 80 years, who smoked more than 20 packs/ year or stopped smoking less than 15 years ago. Its objective is to determine whether assisting multidisciplinary team (MDT) meetings with an AI-based analysis of screening LDCT accelerates the definitive classification of nodules into malignant or benign.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| IA Group | Experimental | Patients with at least one nodule (> 6mm) for whom the multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography |
|
| Group not IA analysis | Other | Patients with at least one nodule (> 6mm) for whom the multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| IA | Other | The multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography |
|
| Measure | Description | Time Frame |
|---|---|---|
| Diagnosis of lung disease | Elapsed time between lung nodule discovery and MDT decision making. | At 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| Operating characteristics of Ai-based strategy | At 3 years |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Marquette Charles-Hugo, PhD | Contact | +33492037777 | marquette.c@chu-nice.fr | |
| Boutros Jacques | Contact | +33492037777 | boutros.j@chu-nice.fr |
| Name | Affiliation | Role |
|---|---|---|
| Marquette Charles-Hugo | CHU de Nice, Service de Pneumologie | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHU de Nice - Hôpital de Pasteur | Recruiting | Nice | Alpes-maritimes | 06001 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38355174 | Derived | Benzaquen J, Hofman P, Lopez S, Leroy S, Rouis N, Padovani B, Fontas E, Marquette CH, Boutros J; Da Capo Study Group. Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol. BMJ Open. 2024 Feb 13;14(2):e074680. doi: 10.1136/bmjopen-2023-074680. |
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Data are available upon reasonable request
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| Not IA | Other | The multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography |
|
| D008171 |
| Lung Diseases |
| D012140 | Respiratory Tract Diseases |