Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

9 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-06-01

Study Completion Date

2024-10-24

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

NCT06285058

Deep Learning Model Pathological Complete Response Non-small Cell Lung Cancer +1 more

NOT_YET_RECRUITING

Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

NCT05925738

Non-small Cell Lung Cancer Spread Through Air Space Visceral Pleural Invasion +1 more

UNKNOWN

A Radiomic Model for Risk of Local Recurrence and DFS for T3 and T4 Non-small Cell Lung Cancer

NCT06405815

Lung Cancer, Nonsmall Cell

COMPLETED

Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer

NCT05925751

Non-small Cell Lung Cancer Neoadjuvant Chemoimmunotherapy Complete Pathological Response

UNKNOWN

AI Models for Predicting Occult Pleural Dissemination in NSCLC

NCT07065422

Non-Small Cell Lung Cancer

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

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

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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.

No interventions assigned to this group

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。

No interventions assigned to this group

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.

No interventions assigned to this group