Non-small Cell Lung CancerNCT05925738
Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
An observational study of Non-small Cell Lung Cancer and Spread Through Air Space, run by Shanghai Pulmonary Hospital, Shanghai, China at 3 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 aggressive histological pattern in resected non-small cell lung cancer 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
- Non-small Cell Lung Cancer, Spread Through Air Space, Visceral Pleural Invasion
- What is being tested?
- PET/CT-based Deep Learning Signature
- How many people
- 1,500 participants
- Where
- 3 sitesChina
- Study dates
- 1 May 2023to 31 Oct 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: Resected Stage I-III Non-small Cell Lung Cancer
Who may be able to join
- Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) Obtained written informed consent.
Who cannot join
- Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants who have received neoadjuvant therapy.
Trial locations and contacts
3 sites in 1 country.
China3 sites, 3 recruiting
- Recruiting
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi
Bentong Yu, Dr, 021-65115006, 1151697503@qq.com
- Recruiting
Ningbo HwaMei Hospital
Ningbo, Zhejiang
Minglei Yang, Dr, 021-65115006, almondjj@163.com
- Recruiting
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou
Yongxiang Song, Dr, 15505177258, zhong961008@163.com
About this study
Official title: Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting aggressive histological pattern in resected non-small cell lung cancer based on a multicenter prospective cohort.
- Conditions
- Non-small Cell Lung Cancer, Spread Through Air Space, Visceral Pleural Invasion, Lymphovascular Invasion
- Being tested
- PET/CT-based Deep Learning Signature
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PET/CT-based Deep Learning SignatureDiagnostic test
Deep Learning Signature Based on PET-CT for Predicting the Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve
The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
Measured: 2023.5.1-2023.10.31
Secondary outcomes (1)
Sensitivity
The sensitivity of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
Measured: 2023.5.1-2023.10.31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2023
- Study start1 May 2023
- Primary completion31 Oct 2023 (estimated)
- Study completion31 Oct 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-small Cell 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
- 1,500 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
- NCT05925738
- Sponsor's protocol ID
- DLAHP
- Status last verified
- Jun 2023
- First submitted
- 12 May 2023
- Last updated
- 29 Jun 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 29 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
- ClinicalTrials.gov ID
- NCT05925738
- Acronym / sponsor ID
- DLAHP
- Conditions
- Non-small Cell Lung Cancer, Spread Through Air Space, Visceral Pleural Invasion, Lymphovascular Invasion
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 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 May 2023 (actual)
- Primary completion
- 31 Oct 2023 (estimated)
- Study completion
- 31 Oct 2023 (estimated)
- First posted
- 29 Jun 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 aggressive histological pattern in resected non-small cell lung cancer 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
- Resected Stage I-III Non-small Cell Lung Cancer
Key inclusion criteria (1 listed)
- Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) 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) 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 Deep Learning SignatureDeep Learning Signature Based on PET-CT for Predicting the Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer | Diagnostic test | – |
Primary outcome
Area under the receiver operating characteristic curve
Time frame: 2023.5.1-2023.10.31
The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
1 secondary outcome
Sensitivity
Time frame: 2023.5.1-2023.10.31
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2023.
- Individual participant dataSharing planned: No
Related trials for Non-small Cell Lung Cancer
RecruitingPhase 3
Pembrolizumab With or Without Maintenance Sacituzumab Tirumotecan (Sac-TMT; MK-2870) in Metastatic Squamous Non-small Cell Lung Cancer (NSCLC) [MK-2870-023]
Non-small Cell Lung Cancer, NSCLC
RecruitingPhase 3
Testing the Addition of a Type of Drug Called Immunotherapy to the Usual Chemotherapy Treatment for Non-small Cell Lung Cancer, an ALCHEMIST Treatment Trial (Chemo-IO [ACCIO])
Lung Non-Small Cell Carcinoma, Lung Non-Small Cell Squamous Carcinoma
RecruitingPhase 3
A Study of Adagrasib Plus Pembrolizumab Plus Chemotherapy vs. Placebo Plus Pembrolizumab Plus Chemotherapy in Participants With Previously Untreated Non-squamous Non-small Cell Lung Cancer With KRAS G12C Mutation (KRYSTAL-4)
Carcinoma, Non-Small-Cell Lung
RecruitingPhase 1
Impact of Using a GLP1 on Treatment Related Side Effects of Lung Cancer Patients Receiving Lorlatinib
Lung Cancer
RecruitingPhase 3
Sacituzumab Tirumotecan (MK-2870) in Combination With Pembrolizumab Versus Pembrolizumab Alone in Metastatic Non-small Cell Lung Cancer (NSCLC) With Programmed Cell Death Ligand 1 (PD-L1) Tumor Proportion Score (TPS) ≥ 50% (MK-2870-007)
Non-small Cell Lung Cancer (NSCLC)
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.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-small Cell Lung Cancer and Spread Through Air Space, run by Shanghai Pulmonary Hospital, Shanghai, China at 3 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 29 Jun 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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: 2023.5.1-2023.10.31). 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 29 Jun 2023.