Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection (DL-Rec-ILA)

Chongqing Medical University

Status: Completed

Conditions: Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection

Study type: Observational

Funder types: Other

Identifiers

NCT06659601

2022MSXM147 (Other Grant/Funding Number)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 9 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Exclusion Criteria:(i) multiple primary LADC; and (ii) other pulmonary lesions that might interfere with the morphological assessment of tumors.

Trial design

9 participants in 3 patient groups

Training Cohort

Description:

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

Description:

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

Description:

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.