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RECRUITING
NCT06496360

Prediction of Mediastinal Station IV Lymph Node Metastasis in Non-small Cell Lung Cancer

Sponsor: Qilu Hospital of Shandong University

View on ClinicalTrials.gov

Summary

Mediastinal lymph node metastasis is a common metastasis pathway of non-small cell lung cancer (NSCLC), and its occurrence is closely related to the lymphatic drainage pattern, which is different in different pulmonary lobe NSCLC, which poses a challenge for the formulation of individualized treatment strategies. Accurate staging is the prerequisite for accurate treatment of NSCLC. Computed Tomograph (CT) examination is an important tool for evaluating mediastinal lymph node metastasis, which is crucial for making treatment plan and evaluating patient prognosis. However, it is difficult to diagnose metastatic lymph nodes with insignificant imaging features. Especially metastatic lymph nodes in areas 4 and 7. Both zone 4 and zone 7 are hot spots for mediastinal lymph node metastasis. However, clinical guidelines do not make clear provisions on lymph node dissection in zone 4, which makes preoperative clinical staging and prognosis evaluation of patients with NSCLC particularly important. By integrating and analyzing a large amount of data in CT images, the newly emerging CT radiomics technology captures subtle features that may be overlooked in conventional CT scans, showing great application prospects in the accuracy of non-invasive diagnosis of lymph node metastasis. This study aims to explore the mediastinal drainage pattern and the role of CT in evaluating mediastinal lymph node metastasis, in order to provide valuable imaging evidence for accurately judging mediastinal lymph node metastasis of NSCLC, formulating appropriate lymph node dissection scope, optimizing treatment strategy, and improving patient prognosis.

Official title: Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics

Key Details

Gender

All

Age Range

Any - Any

Study Type

OBSERVATIONAL

Enrollment

150

Start Date

2024-08-01

Completion Date

2025-06

Last Updated

2024-08-07

Healthy Volunteers

No

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence

The model employs machine learning algorithms to analyze CT imaging data of patients with non-small cell lung cancer. It focuses on the identification and assessment of features of the mediastinal fourth group lymph nodes, including size, shape, margins, and density. By extracting features related to lymph node metastasis, the model assists doctors in making more accurate diagnoses.

Locations (1)

Qilu Hospital of Shandong University

Jinan, Shandong, China