Lung AdenocarcinomaNCT05736991
Deep Learning Signature for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
An observational study of Lung Adenocarcinoma and Radiomics, run by Shanghai Pulmonary Hospital, Shanghai, China at 4 sites in China.
Trial summary
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma based on a multicenter prospective cohort. Read more about this study
- Who is it for?
- Ages 20 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Adenocarcinoma, Radiomics, Grading System
- What is being tested?
- PET/CT-based Radiomics Signature
- How many people
- 600 participants
- Where
- 4 sitesChina
- Study dates
- 1 Nov 2022to 30 Apr 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 20 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Clinical Stage I Lung Adenocarcinoma
Who may be able to join
- Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum diameter of lesion less than 4 cm on CT scans; (3) The maximum short axis diameter of lymph nodes less than 1 cm on CT scan; (4) The SUVmax of hilar and mediastinal lymph nodes less than 2.5; (5) Pathological confirmation of primary lung adenocarcinoma; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.
Who cannot join
- Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Mucinous adenocarcinomas; (5) Participants who have received neoadjuvant therapy.
Trial locations and contacts
4 sites in 1 country.
China4 sites, 4 recruiting
- Recruiting
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi
Bentong Yu, Dr, 15505177258, bentongyunanchang@163.com
- Recruiting
Ningbo HwaMei Hospital
Ningbo, Zhejiang
Minglei Yang, Dr, 1555177258, ymlsph@163.com
- Recruiting
Shanghai Pulmonary Hospital
Yangpu, Shanghai Municipality
Chang Chen, MD, PhD, +86-021-65115006 ext. 2074, chenthoracic@163.com
- Recruiting
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou
Yongxiang Song, Dr, 15505177258, zhong961008@163.com
About this study
Official title: Deep Learning Signature Based on PET-CT Images for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma based on a multicenter prospective cohort.
- Conditions
- Lung Adenocarcinoma, Radiomics, Grading System
- Being tested
- PET/CT-based Radiomics Signature
- Medical subject headings
- Adenocarcinoma of Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PET/CT-based Radiomics SignatureDiagnostic test
Radiomics Signature Based on PET-CT for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve
Area under the receiver operating characteristic curve
Measured: 2022.11-2023.4
Secondary outcomes (5)
Sensitivity
Sensitivity
Measured: 2022.11-2023.4
Specificity
Specificity
Measured: 2022.11-2023.4
Positive predictive value
Positive predictive value
Measured: 2022.11-2023.4
Negative predictive value
Negative predictive value
Measured: 2022.11-2023.4
Accuracy
Accuracy
Measured: 2022.11-2023.4
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2023
- Study start1 Nov 2022
- Primary completion30 Apr 2023 (estimated)
- Study completion30 Apr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung AdenocarcinomaStudies 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
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
- Sponsor type
- University, hospital or other
- Collaborators
- Ningbo No.2 Hospital, Zunyi Medical College, The First Affiliated Hospital of Nanchang University
- Responsible party
- Principal investigator, Chang Chen, Shanghai Pulmonary Hospital, Shanghai, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05736991
- Sponsor's protocol ID
- DLGS
- Status last verified
- Feb 2023
- First submitted
- 7 Feb 2023
- Last updated
- 21 Feb 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Signature Based on PET-CT Images for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
- ClinicalTrials.gov ID
- NCT05736991
- Acronym / sponsor ID
- DLGS
- Conditions
- Lung Adenocarcinoma, Radiomics, Grading System
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Shanghai Pulmonary Hospital, Shanghai, ChinaUniversity, hospital or other
- Collaborators
- Ningbo No.2 Hospital, Zunyi Medical College, The First Affiliated Hospital of Nanchang University
- Study start
- 1 Nov 2022 (actual)
- Primary completion
- 30 Apr 2023 (estimated)
- Study completion
- 30 Apr 2023 (estimated)
- First posted
- 21 Feb 2023 (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 evaluate the performance of a PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma based on a multicenter prospective cohort.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 20 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Clinical Stage I Lung Adenocarcinoma
Key inclusion criteria (1 listed)
- Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum diameter of lesion less than 4 cm on CT scans; (3) The maximum short axis diameter of lymph nodes less than 1 cm on CT scan; (4) The SUVmax of hilar and mediastinal lymph nodes less than 2.5; (5) Pathological confirmation of primary lung adenocarcinoma; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.
Key exclusion criteria (1 listed)
- Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Mucinous adenocarcinomas; (5) Participants who have received neoadjuvant therapy.
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 |
|---|---|---|
| PET/CT-based Radiomics SignatureRadiomics Signature Based on PET-CT for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma | Diagnostic test | – |
Primary outcome
Area under the receiver operating characteristic curve
Time frame: 2022.11-2023.4
Area under the receiver operating characteristic curve
5 secondary outcomes
Sensitivity
Time frame: 2022.11-2023.4
Specificity
Time frame: 2022.11-2023.4
Positive predictive value
Time frame: 2022.11-2023.4
Negative predictive value
Time frame: 2022.11-2023.4
Accuracy
Time frame: 2022.11-2023.4
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Feb 2023.
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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.
The First Affiliated Hospital of Nanchang University
Nanchang, China
4 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 Adenocarcinoma and Radiomics, run by Shanghai Pulmonary Hospital, Shanghai, China at 4 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 1 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 21 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 4 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?
Area under the receiver operating characteristic curve (time frame: 2022.11-2023.4). 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 21 Feb 2023.