Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection (DL-Rec-ILA)
Study Overview
Status
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 Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Chongqing
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Yuzhong District, Chongqing, China, 400016
- The First Affiliated Hospital of Chongqing Medical University
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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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Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
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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.
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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。
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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.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
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Recurrence Prediction Accuracy
Time Frame: October 2024
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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).
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October 2024
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Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 2022MSXM147 (Other Grant/Funding Number: Joint project of Chongqing Health Commission and Science and Technology Bureau)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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