Status Approved
First Submitted Date
2020/05/21
Registered Date
2020/05/22
Last Updated Date
2020/11/24
CRIS Required
WHO ICTRP (International Clinical Trial Registry Platform) Required
1. Background
CRIS Registration Number |
KCT0005051 |
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Unique Protocol ID | D-1908-160-1059 |
Public/Brief Title | Diagnosis of lung nodule and lung cancer on screening chest radiographs: Comparative clinical trial for evaluation of artificial intelligence-integrated PACS versus conventional PACS |
Scientific Title | Diagnosis of lung nodule and lung cancer on screening chest radiographs: Comparative clinical trial for evaluation of artificial intelligence-integrated PACS versus conventional PACS |
Acronym | |
MFDS Regulated Study | No |
IND/IDE Protocol | No |
Registered at Other Registry | No |
Healthcare Benefit Approval Status | Not applicable |
2. Institutional Review Board / Ethics Committee
Board Approval Status | Submitted approval |
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Board Approval Number | D-1908-160-1059 |
Approval Date | 2020-05-13 |
Institutional Review Board Name | Seoul National University Hospital Institutional Review Board |
Institutional Review Board Address | 103, Daehak-ro, Jongno-gu, Seoul |
Institutional Review Board Telephone | 02-2072-0694 |
Data Monitoring Committee | No |
3. Contact Details
Contact Person for Principal Investigator / Scientific Queries | |
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Name | Jin Mo Goo |
Title | Professor |
Telephone | +82-2-2072-2624 |
Affiliation | Seoul National University Hospital |
Address | 101 Daehak-ro, Jongno-gu, Seoul |
Contact Person for Public Queries | |
Name | Ju Gang Nam |
Title | Clinical Professor |
Telephone | +82-2-2072-2254 |
Affiliation | Seoul National University Hospital |
Address | 101 Daehak-ro, Jongno-gu, Seoul |
Contact Person for Updating Information | |
Name | Ju Gang Nam |
Title | Clinical Professor |
Telephone | +82-2-2072-2254 |
Affiliation | Seoul National University Hospital |
Address | 101 Daehak-ro, Jongno-gu, Seoul |
4. Status
Study Site | Single | |
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Overall Recruitment Status | Recruiting | |
Date of First Enrollment | 2020-06-15 Actual | |
Target Number of Participant | 84000 | |
Primary Completion Date | 2021-09-30 , Anticipated | |
Study Completion Date | 2021-12-31 , Anticipated | |
Recruitment Status by Participating Study Site 1 | ||
Name of Study | Seoul National University Hospital | |
Recruitment Status | Recruiting | |
Date of First Enrollment | 2020-06-15 , |
5. Source of Monetary / Material Support
1. Source of Monetary/Material Support | |
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Organization Name | Ministry of Health & Welfare |
Organization Type | Government |
Project ID | HI19C1129 |
6. Sponsor Organization
1. Sponsor Organization | |
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Organization Name | Seoul National University Hospital |
Organization Type | Medical Institute |
7. Study Summary
Lay Summary | Background: Lung cancer is the highest mortality-cancer in the world, and it is well known that lung cancer screening is important as early detection yields improved survival. While large randomized controlled trials have demonstrated the efficacy of screening with low-dose CT in high-risk groups, chest radiograph has failed to show effectiveness as a screening modality, and it has been reported that about 30% of lung cancers were missed in chest radiography on formal reading. However, chest radiograph is still the most commonly performed imaging modality, and has many advantages in terms of accessibility, cost, and radiation exposure. There are many causes of miss-interpretation of lung cancer in chest radiograph, but the location of the lesion and the fatigue of the radiologists are well known causes. Recently, many retrospective studies have reported that artificial intelligence-algorithms using deep learning may compensate these factors. However, prospective clinical trials have not yet been conducted to show the improvement of the interpretation-quality when using deep learning algorithms compared to the radiologists reading alone in actual clinical situations. In order to apply the algorithm in actual workflow, first there must be a quality-approved algorithm, and second the PACS system should be integrated with the algorithm. In this study, we aim to evaluate if the artificial intelligence-integrated PACS (Infinitt M6) system integrated with the lung lesion-detection algorithm (Lunit INSIGHT CXR MCA), which has been verified through various studies and approved by the Ministry of Food and Drug Safety, may improve lung nodule detection rate from chest radiographs in an asymptomatic health checkup population. Hypothesis: Artificial intelligence-integrated PACS would increase the lung nodule detection rate from chest radiographs on an asymptomatic health checkup population. Objective: To investigate the effectiveness of the Artificial intelligence-integrated PACS in lung nodule detection from chest radiographs on an asymptomatic health checkup population, and help set up clinical workflow. Plan: All chest radiographs from asymptomatic patients who visit Seoul National University healthcare screening center are randomly distributed to conventional PACS system and Artificial intelligence-integrated PACS system for interpretation. All other reading conditions (reading environment, reading doctor, interpretation guideline) should be equal. The lung nodule detection rate, i.e. rate of positive cases who are confirmed to have meaningful nodules on the follow-up chest CT (taken within 3 months) divided by total population, is evaluated and compared between two groups. Additionally, false positive rate, true positive rate, false negative rate, lung cancer detection rate, and other lung disease detection rate are evaluated and compared between two groups for available cases. |
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8. Study Design
Study Type | Interventional Study |
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Study Purpose | Diagnosis |
Phase | Not applicable |
Intervention Model | Parallel |
Blinding/Masking | Open |
Allocation | RCT |
Intervention Type | Medical Device |
Intervention Description | In case group, chest radiograph interpretation will be performed using a deep learning-based computer-aided detection software (Software name: Lunit INSIGHT CXR MCA, Manufacturer: Lunit Inc.). The software was approved as a medical device by the Ministry of Food and Drug Safety, Republic of Korea. The software was designed to analyze a chest radiograph image, and identify relevant abnormalities (nodule, consolidation, and pneumothorax) on the image, and to assist the interpretation by doctors. The software provides a probability value for the presence of abnormality on the chest radiograph, and localization of abnormality overlaid on the original image. The duty board-certified radiologist will interpret chest radiographs after evaluation of both original chest radiograph image and the result from the computer-aided detection system. In control group, the duty board-certified radiologist will interpret chest radiographs after evaluation of original chest radiograph images only, as conventional clinical practice. |
Number of Arms | 2 |
Arm 1 |
Arm Label Artificial intelligence computer-aided detection system-assisted interpretation group |
Target Number of Participant 42000 |
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Arm Type Experimental |
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Arm Description Chest radiograph interpretation will be performed using a deep learning-based computer-aided detection software (Software name: Lunit INSIGHT CXR MCA, Manufacturer: Lunit Inc.). The software was approved as a medical device by the Ministry of Food and Drug Safety, Republic of Korea. The software was designed to analyze a chest radiograph image, and identify relevant abnormalities (nodule, consolidation, and pneumothorax) on the image, and to assist the interpretation by doctors. The software provides a probability value for the presence of abnormality on the chest radiograph, and localization of abnormality overlaid on the original image. The board-certified radiologist will interpret chest radiographs after evaluation of both original chest radiograph image and the result from the computer-aided detection system. |
|
Arm 2 |
Arm Label Conventional interpretation group |
Target Number of Participant 42000 |
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Arm Type Active comparator |
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Arm Description The duty board-certified radiologist will interpret chest radiographs after evaluation of original chest radiograph images only, as conventional clinical practice. |
9. Subject Eligibility
Condition(s)/Problem(s) |
* (J00-J99)Diseases of the respiratory system (J98.40)Solitary pulmonary nodule Lung nodule (non-calcified solid nodules larger than or equal to 8 mm or part solid-nodules with solid portion larger than or equal to 6 mm) |
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Rare Disease | No |
Inclusion Criteria |
Gender Both |
Age 19Year~No Limit |
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Description All adult individuals who performed chest radiograph in Seoul National University Healthcare Screening Center |
|
Exclusion Criteria |
Individuals who had respiratory symptoms when taking chest radiograph |
Healthy Volunteers | Yes |
10. Outcome Measure(s)
Type of Primary Outcome | Efficacy | |
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Primary Outcome(s) 1 | ||
Outcome | Lung nodule detection rate |
|
Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 1 | ||
Outcome | Positive rate |
|
Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 2 | ||
Outcome | Sensitivity |
|
Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 3 | ||
Outcome | Specificity |
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Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 4 | ||
Outcome | Positive predictive value |
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Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 5 | ||
Outcome | Negative predictive value |
|
Timepoint | Three months after chest radiograph |
|
Secondary Outcome(s) 6 | ||
Outcome | Lung cancer detection rate |
|
Timepoint | End of the trial |
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Secondary Outcome(s) 7 | ||
Outcome | Detection rate of other lung disease |
|
Timepoint | End of the trial |
|
Secondary Outcome(s) 8 | ||
Outcome | False referral rate |
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Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 9 | ||
Outcome | Revisit rate within three months |
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Timepoint | Three months after chest radiograph |
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Secondary Outcome(s) 10 | ||
Outcome | CT referral rate |
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Timepoint | Three months after chest radiograph |
11. Study Results and Publication
Result Registered | No |
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12. Sharing of Study Data(Deidentified Individual-Patient Data, IPD)
Sharing Statement | No |
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