Lung CancerNCT04036903
D-Lung: An Analytics Platform for Lung Cancer Based on Deep Learning Technology
An observational study of Lung Cancer, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial summary
Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Lung Cancer
- What is being tested?
- computed tomography
- Type of treatment
- Surgery or procedure
- How many people
- 130 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Jul 2018to 30 Jun 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
Study population: This is a single institutional retrospective cohort study of patients within hospitals in Hong Kong, who had undergone thoracic CT for suspicious lung nodules.
Who may be able to join
- Subjects with suspicious lung nodules.
- Thin-layer thoracic CT and pathology examination have been performed for suspicious lung nodules.
Who cannot join
- Subjects with accompanied lesions on CT images that may interfere to lung nodules analysis
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin
About this study
Official title: D-Lung: An Analytics Platform for Primary Lung Cancer Screening, Diagnosis and Management Based on Deep Learning Technology
Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. The National Lung Screen Trial has demonstrated that the mortality rates could decline greatly, by the utility of low-dose helical computed tomography for screen of pulmonary nodules. Thus, automatic detection, diagnosis and management of pulmonary nodules, play the vital roles in computer-aided lung cancer screening and early intervention.
- Conditions
- Lung Cancer
- Being tested
- computed tomography
- Keywords
- Lung nodules, Deep learning
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
computed tomographyRadiation
thoracic CT examinations for diagnosis, and/or follow-up.
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy
proportion of true results(both true positives and true negatives) among whole instances
Measured: 2 years
sensitivity
true positive rate in percentage(%) derived by ROC analysis
Measured: 2 years
specificity
true negative rate in percentage (%) derived by ROC analysis
Measured: 2 years
area under curve (AUC)
area under ROC curve in percentage (%)
Measured: 2 years
Secondary outcomes (2)
average number of false positives per scan (FPs/scan)
FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis
Measured: 2 years
competition performance metric (CPM)
Competitive performance metric (CPM) is a criterion used for CAD system evaluation. Based on FROC paradigm, CPM score is computed as an average sensitivity at seven predefined average false positive rates. CPM score ranges from 0 to 1, with higher CPM score indicating better CAD performance.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2019
- Study start1 Jul 2018
- Primary completion30 Jun 2020
- Study completion30 Jun 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 130 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Department of Computer Science & Engineering, CUHK
- Responsible party
- Principal investigator, Professor Winnie W.C. Chu, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04036903
- Sponsor's protocol ID
- 2019.306
- Status last verified
- Feb 2023
- First submitted
- 26 Jul 2019
- Last updated
- 8 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- D-Lung: An Analytics Platform for Primary Lung Cancer Screening, Diagnosis and Management Based on Deep Learning Technology
- ClinicalTrials.gov ID
- NCT04036903
- Acronym / sponsor ID
- 2019.306
- Conditions
- Lung Cancer
- Keywords
- Lung nodules, Deep learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 130 participants (actual)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Collaborators
- Department of Computer Science & Engineering, CUHK
- Study start
- 1 Jul 2018 (actual)
- Primary completion
- 30 Jun 2020 (actual)
- Study completion
- 30 Jun 2020 (actual)
- First posted
- 30 Jul 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. The National Lung Screen Trial has demonstrated that the mortality rates could decline greatly, by the utility of low-dose helical computed tomography for screen of pulmonary nodules. Thus, automatic detection, diagnosis and management of pulmonary nodules, play the vital roles in computer-aided lung cancer screening and early intervention.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- This is a single institutional retrospective cohort study of patients within hospitals in Hong Kong, who had undergone thoracic CT for suspicious lung nodules.
Key inclusion criteria (2 listed)
- Subjects with suspicious lung nodules.
- Thin-layer thoracic CT and pathology examination have been performed for suspicious lung nodules.
Key exclusion criteria (1 listed)
- Subjects with accompanied lesions on CT images that may interfere to lung nodules analysis
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| computed tomographythoracic CT examinations for diagnosis, and/or follow-up. | Radiation | – |
Primary outcome
accuracy
Time frame: 2 years
proportion of true results(both true positives and true negatives) among whole instances
sensitivity
Time frame: 2 years
true positive rate in percentage(%) derived by ROC analysis
specificity
Time frame: 2 years
true negative rate in percentage (%) derived by ROC analysis
area under curve (AUC)
Time frame: 2 years
area under ROC curve in percentage (%)
2 secondary outcomes
average number of false positives per scan (FPs/scan)
Time frame: 2 years
competition performance metric (CPM)
Time frame: 2 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2023.
- Individual participant dataSharing planned: No
Related trials for Lung Cancer
RecruitingPhase 1/2
A Study to Investigate ALE.P02 as a Monotherapy or in Combination With Pembrolizumab in Adult Patients With Selected CLDN1+ Solid Tumors
Squamous Non-small-cell Lung Cancer, Head and Neck Squamous Cell Carcinoma
RecruitingPhase 3
A Clinical Study to Assess the Efficacy of Adjuvant Immunotherapy With Cemiplimab in Patients With Surgically Removed Non-small Cell Lung Cancer Who Have Not Received Prior Chemotherapy
Non-Small Cell Lung Cancer
RecruitingPhase 2
Testing Osimertinib as a Treatment for Lung Cancers With an EGFR Exon 20 Change
Advanced Lung Non-Small Cell Carcinoma, Recurrent Lung Non-Small Cell Carcinoma
Not yet recruitingPhase 2/3
Master Protocol for the Study of Rilogrotug for the Treatment of Weight Loss and Anorexia in Patients With Cancer Cachexia and mCRC or mNSCLC
Cachexia; Cancer, Cachexia
RecruitingPhase 3
Testing the Impact of an Anti-Cancer Drug, Atezolizumab, After Surgery to Prevent Early Stage Non-small Cell Lung Cancer From Returning, AASI-NSCLC Trial
Lung Non-Small Cell Carcinoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 8 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
accuracy (time frame: 2 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Feb 2023.