Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar ResectionDL-Rec-ILANCT06659601
Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
An observational study of Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection, run by First Affiliated Hospital of Chongqing Medical University at 1 site in China.
Trial summary
This study aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist thoracic surgeons in making optimal treatment decisions.The study will use existing CT data to… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
- How many people
- 9 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to 24 Oct 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population consists of patients diagnosed with stage IA invasive lung adenocarcinoma (ILADC) who underwent preoperative chest CT scans and sub-lobar resection. The participants are not limited in age, include both males and females, and were treated at our institution. The patients of external testing cohort are from other institution.
Inclusion Criteria:(i) pathological confirmation of LADC; (ii) undergoing sub-lobar resection (wedge resection or segmentectomy); (iii) CT scanning prior to surgery; (iv) pathological staging of IA; and (v) complete clinical and follow-up data.
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Exclusion Criteria:(i) multiple primary LADC; and (ii) other pulmonary lesions that might interfere with the morphological assessment of tumors.
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Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital of Chongqing Medical University
Yuzhong District, Chongqing Municipality, 400016
About this study
This study aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist thoracic surgeons in making optimal treatment decisions.The study will use existing CT data to train and validate the model, without requiring any additional intervention for the participants.
Read the detailed description
This study is designed to develop a deep learning model to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection using noncontrast CT images. The best indications for sub-lobar resection in patients with early-stage LADC are still debated, making surgical method selection somewhat difficult. The deep learning model can noninvasively and objectively predict the recurrence risk of patients with stage IA ILADC following sub-lobectomy and are helpful in predicting prognosis of patients with stage IA ILADC after sub-lobectomy and can facilitate the choosing of the optimal surgery mode of these patients.
The study will utilize retrospective data from patients with stage IA invasive lung adenocarcinoma after sub-lobar resection . Noncontrast CT images will be collected at admission and used as inputs for the deep learning model. The model will be trained using convolutional neural networks (CNN) to identify patterns associated with recurrence.
In addition to model development, the study will also evaluate the model's performance on a separate validation cohort to assess generalizability. Statistical analyses will include performance metrics such as area under the receiver operating characteristic (ROC) curve (AUC) and precision-recall curve.
This study aims to provide a valuable tool for clinicians to make timely decisions in choosing the optimal therapeutic approach.
- Keywords
- Lung cancer, Sub-lobar resection, Computed tomography, 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.
| Study group | Role | What they receive |
|---|---|---|
| Training Cohort | Patients in this cohort diagnosed with stage IA invasive lung adenocarcinoma who underwent sub-lobar resection. This cohort is used to train the 3D deep learning model to predict recurrence risk. | |
| Validation Cohort | Patients in this cohort with stage IA ILADC. It is used to validate the model performance internally and assess its generalization within the same institution。 | |
| Testing Cohort | Patients in this cohort from other institution. It is used to test the generalizability of the model in predicting recurrence risk in an independent dataset. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Recurrence Prediction Accuracy
The primary outcome measure is the accuracy of the 3D deep learning model in predicting the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection. Accuracy will be evaluated by comparing the model's predictions with actual patient outcomes using metrics such as sensitivity, specificity, and area under the ROC curve (AUC).
Measured: October 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2024
- Study start1 Jun 2023
- Primary completion1 Jan 2024
- Study completion24 Oct 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar ResectionStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Chongqing Medical UniversityThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 9 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xin Fan, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06659601
- Sponsor's protocol ID
- 2022MSXM147
- Other IDs
- 2022MSXM147
- Acronym
- DL-Rec-ILA
- Status last verified
- Oct 2024
- First submitted
- 24 Oct 2024
- Last updated
- 26 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to privacy concerns and institutional regulations, individual participant data (IPD) will not be shared with external researchers.
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
- ClinicalTrials.gov ID
- NCT06659601
- Acronym / sponsor ID
- DL-Rec-ILA / 2022MSXM147
- Keywords
- Lung cancer, Sub-lobar resection, Computed tomography, Deep learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9 participants (actual)
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical UniversityUniversity, hospital or other
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- 1 Jan 2024 (actual)
- Study completion
- 24 Oct 2024 (actual)
- First posted
- 26 Oct 2024 (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.
This study aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist thoracic surgeons in making optimal treatment decisions.The study will use existing CT data to train and validate the model, without requiring any additional intervention for the participants.
Detailed description
This study is designed to develop a deep learning model to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection using noncontrast CT images. The best indications for sub-lobar resection in patients with early-stage LADC are still debated, making surgical method selection somewhat difficult. The deep learning model can noninvasively and objectively predict the recurrence risk of patients with stage IA ILADC following sub-lobectomy and are helpful in predicting prognosis of patients with stage IA ILADC after sub-lobectomy and can facilitate the choosing of the optimal surgery mode of these patients.
The study will utilize retrospective data from patients with stage IA invasive lung adenocarcinoma after sub-lobar resection . Noncontrast CT images will be collected at admission and used as inputs for the deep learning model. The model will be trained using convolutional neural networks (CNN) to identify patterns associated with recurrence.
In addition to model development, the study will also evaluate the model's performance on a separate validation cohort to assess generalizability. Statistical analyses will include performance metrics such as area under the receiver operating characteristic (ROC) curve (AUC) and precision-recall curve.
This study aims to provide a valuable tool for clinicians to make timely decisions in choosing the optimal therapeutic approach.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training Cohort | – | –Patients in this cohort diagnosed with stage IA invasive lung adenocarcinoma who underwent sub-lobar resection. This cohort is used to train the 3D deep learning model to predict recurrence risk. |
| Validation Cohort | – | –Patients in this cohort with stage IA ILADC. It is used to validate the model performance internally and assess its generalization within the same institution。 |
| Testing Cohort | – | –Patients in this cohort from other institution. It is used to test the generalizability of the model in predicting recurrence risk in an independent dataset. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of patients diagnosed with stage IA invasive lung adenocarcinoma (ILADC) who underwent preoperative chest CT scans and sub-lobar resection. The participants are not limited in age, include both males and females, and were treated at our institution. The patients of external testing cohort are from other institution.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Recurrence Prediction Accuracy
Time frame: October 2024
The primary outcome measure is the accuracy of the 3D deep learning model in predicting the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection. Accuracy will be evaluated by comparing the model's predictions with actual patient outcomes using metrics such as sensitivity, specificity, and area under the ROC curve (AUC).
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 26 Oct 2024.
- Individual participant dataSharing planned: No. Due to privacy concerns and institutional regulations, individual participant data (IPD) will not be shared with external researchers.
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 Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection, run by First Affiliated Hospital of Chongqing Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Oct 2024. 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?
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?
Recurrence Prediction Accuracy (time frame: October 2024). 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 26 Oct 2024.