RecruitingNCT05830812

Improving the Intraoperative Diagnosis Accuracy of Invasiveness for Small-sized Lung Adenocarcinoma

Improving the Intraoperative Diagnosis Accuracy for Pre-invasive and Invasive Small-sized Lung Adenocarcinoma Node by Combining Multi-modal Information


Sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Enrollment

3,000 participants

Start Date

Jan 1, 2023

Study Type

OBSERVATIONAL

Conditions

Summary

The goal of this observational study is to improve the intraoperative diagnosis accuracy of invasiveness for small-sized lung adenocarcinoma by combining multi-modal information. The main question it aims to answer is whether multi-modal information have great value of prediction on the invasiveness for small-sized lung adenocarcinoma. Since a promising limited resection is largely based on intraoperative frozen section diagnosis, there is a growing demand on the high-accuracy of timely pathology diagnosis. The multi-modal information of participants will be collected retrospectively.


Eligibility

Min Age: 20 YearsMax Age: 80 Years

Plain Language Summary

Simplified for easier understanding

This study is developing a better way to determine during lung surgery — while the patient is still on the operating table — whether a small lung tumor is an early invasive cancer or a less aggressive non-invasive type, so that the surgeon can choose the most appropriate extent of tissue removal. **You may be eligible if...** - You are 20–80 years old - You have a small lung tumor (less than 3 cm) that appears to be clinical stage I lung cancer - You have had a CT scan of your chest within 3 months before surgery - The tumor is confirmed to be lung adenocarcinoma after surgical removal - You have not had any prior lung treatment **You may NOT be eligible if...** - Your CT images have significant artefacts that make assessment difficult - You received treatment for the lung tumor before surgery - Your clinical information or imaging files are incomplete - You have a history of other cancers - Your lung cancer is associated with cystic airspaces on CT Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTInvasiveness diagnosis

To predict the invasiveness of patients with small-sized lung adenocarcinoma intraoperatively based on multi-modal information.


Locations(1)

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

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NCT05830812


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