Lung CancerNCT05542992
Deep Learning Model for Pure Solid Nodules Classification
An observational study of Lung Cancer, run by Chang Chen at 5 sites in China.
Trial summary
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort. 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?
- CT-based deep learning model
- How many people
- 260 participants
- Where
- 5 sitesChina
- Study dates
- 1 Jan 2022to 31 Dec 2023
- 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: Patients with pulmonary radiological pure-solid nodules with size less than 3cm
Who may be able to join
- Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
- The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
- Age ranging from 18-75 years;
- definied pathological examination report available;
- Obtained written informed consent.
Who cannot join
- Multiple lung lesions;
- Poor quality of CT images;
- Participants with incomplete clinical information;
- Participants who have received neoadjuvant therapy before initial CT evaluation.
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Lanzhou
China, Gansu
Minjie Ma, Dr, 021-65115006, 721405952@qq.com
- Recruiting
Zunyi
China, Guizhou
Yongxiang Song, Dr, 021-65115006, zhaosurgery@163.com
- Recruiting
Nanchang
China, Jiangxi
Bentong Yu, Dr, 021-65115006, 1151697503@qq.com
- Recruiting
Ningbo
China, Zhejiang
Minglei Yang, Dr, 021-65115006, almondjj@163.com
- Recruiting
Shanghai Pulmonary Hospital
Yangpu, Shanghai Municipality
Chang Chen, MD, PhD, +86-021-65115006 ext. 2074, chenthoracic@163.com
About this study
Official title: Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.
- Conditions
- Lung Cancer
- Being tested
- CT-based deep learning model
- Keywords
- deep learning model, pure-solid nodules, PET-CT
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT-based deep learning modelDiagnostic test
CT-based deep learning model for pure-solid nodules classifications
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC
Area under the curve of the receiver operating characteristic
Measured: 2022.01-2023.12
Secondary outcomes (5)
Accuracy
Ratio of the number of correctly classified samples to the total number of samples
Measured: 2022.01-2023.12
sensitivity
The probability of detecting a positive test in the population with the gold standard for disease (positive)
Measured: 2022.01-2023.12
Specificity
Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard
Measured: 2022.01-2023.12
PPV
Positive predictive value
Measured: 2022.01-2023.12
NPV
Negative predictive value
Measured: 2022.01-2023.12
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2022
- Study start1 Jan 2022
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (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
- 260 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chang Chen
- Sponsor type
- University, hospital or other
- Collaborators
- Ningbo No.2 Hospital, Zunyi Medical College, The First Affiliated Hospital of Nanchang University, LanZhou University
- Responsible party
- Sponsor-investigator, Chang Chen, Shanghai Pulmonary Hospital, Shanghai, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05542992
- Sponsor's protocol ID
- L21-022
- Status last verified
- Sep 2022
- First submitted
- 13 Sep 2022
- Last updated
- 16 Sep 2022
- Results posted
- 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 16 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study
- ClinicalTrials.gov ID
- NCT05542992
- Acronym / sponsor ID
- L21-022
- Conditions
- Lung Cancer
- Keywords
- deep learning model, pure-solid nodules, PET-CT
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 260 participants (estimated)
- Lead sponsor
- Chang ChenUniversity, hospital or other
- Collaborators
- Ningbo No.2 Hospital, Zunyi Medical College, The First Affiliated Hospital of Nanchang University, LanZhou University
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 16 Sep 2022 (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.
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with pulmonary radiological pure-solid nodules with size less than 3cm
Key inclusion criteria (5 listed)
- Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
- The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
- Age ranging from 18-75 years;
- definied pathological examination report available;
- Obtained written informed consent.
Key exclusion criteria (4 listed)
- Multiple lung lesions;
- Poor quality of CT images;
- Participants with incomplete clinical information;
- Participants who have received neoadjuvant therapy before initial CT evaluation.
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 |
|---|---|---|
| CT-based deep learning modelCT-based deep learning model for pure-solid nodules classifications | Diagnostic test | – |
Primary outcome
AUC
Time frame: 2022.01-2023.12
Area under the curve of the receiver operating characteristic
5 secondary outcomes
Accuracy
Time frame: 2022.01-2023.12
sensitivity
Time frame: 2022.01-2023.12
Specificity
Time frame: 2022.01-2023.12
PPV
Time frame: 2022.01-2023.12
NPV
Time frame: 2022.01-2023.12
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Sep 2022.
- Individual participant dataSharing planned: No
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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.
5 sites 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 Chang Chen at 5 sites 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 5 inclusion and 4 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 Sep 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites 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?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC (time frame: 2022.01-2023.12). 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 Sep 2022.