Lung CancerNCT04000620
Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging
An observational study of Lung Cancer, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
Trial summary
Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Lung Cancer
- What is being tested?
- surgery and punture
- Type of treatment
- Surgery or procedure
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2018to May 2024
- 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 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Lung cancer patients with PET/CT or CT examination before any cancer-specific treatment
Who may be able to join
- Pathological diagnosis of lung cancer
- PET/CT or CT examination before any cancer-specific treatment
Who cannot join
- A history of other malignancies
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Wuhan Union Hospital
Wuhan, Hubei, 430000
Zhilei Lv, MD, 86-15107177084, 794646434@qq.com
About this study
Official title: The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging
Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.
Read the detailed description
Radiologist
- Conditions
- Lung Cancer
- Being tested
- surgery, punture
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
surgeryProcedure or surgery
treatment intent surgery
puntureProcedure or surgery
diagnostic punture
| Study group | Role | What they receive |
|---|---|---|
| cancer cell involvement predicted by deep learning | the participants with lesions of lung, lymph node or other sites predicted as positive for cancer cell involvement by imaging based deep learning. Receives: Procedure: surgery; Procedure: punture | |
| no cancer cell involvement predicted by deep learning | the participants with lesions of lung, lymph node or other sites predicted as negative for cancer cell involvement by imaging based deep learning. Receives: Procedure: surgery; Procedure: punture |
What is being measured
The primary outcome is the main question the study is designed to answer.
pathologic result revealed cancer cell involvement in lesion
Measured: 1 month after the pathologic test
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2019
- Study start1 May 2018
- Primary completion30 Dec 2021 (estimated)
- Study completionMay 2024 (estimated)
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.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yang Jin, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04000620
- Sponsor's protocol ID
- AFDSFA
- Status last verified
- Nov 2021
- First submitted
- 24 Jun 2019
- Last updated
- 16 Nov 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 16 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging
- ClinicalTrials.gov ID
- NCT04000620
- Acronym / sponsor ID
- AFDSFA
- Conditions
- Lung Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 May 2018 (actual)
- Primary completion
- 30 Dec 2021 (estimated)
- Study completion
- May 2024 (estimated)
- First posted
- 27 Jun 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 diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.
Detailed description
Radiologist
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| cancer cell involvement predicted by deep learning | – | Procedure: surgery, Procedure: punturethe participants with lesions of lung, lymph node or other sites predicted as positive for cancer cell involvement by imaging based deep learning. |
| no cancer cell involvement predicted by deep learning | – | Procedure: surgery, Procedure: punturethe participants with lesions of lung, lymph node or other sites predicted as negative for cancer cell involvement by imaging based deep learning. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Lung cancer patients with PET/CT or CT examination before any cancer-specific treatment
Key inclusion criteria (2 listed)
- Pathological diagnosis of lung cancer
- PET/CT or CT examination before any cancer-specific treatment
Key exclusion criteria (1 listed)
- A history of other malignancies
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 |
|---|---|---|
| surgerytreatment intent surgery | Procedure or surgery | cancer cell involvement predicted by deep learning, no cancer cell involvement predicted by deep learning |
| punturediagnostic punture | Procedure or surgery | cancer cell involvement predicted by deep learning, no cancer cell involvement predicted by deep learning |
Primary outcome
pathologic result revealed cancer cell involvement in lesion
Time frame: 1 month after the pathologic test
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Nov 2021.
- Individual participant dataSharing planned: Undecided
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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.
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 Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
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: status not confirmed, as of the sponsor's last update on 16 Nov 2021. 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhilei Lv, MD (86-15107177084, 794646434@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
pathologic result revealed cancer cell involvement in lesion (time frame: 1 month after the pathologic test). 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 16 Nov 2021.