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

NCT ID: NCT06496360

Last Updated: 2024-08-07

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 150 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-08-01
• Study Completion Date: 2025-06-30

Brief 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.

Detailed Description

Background: Lung cancer is one of the malignant tumors with the highest morbidity and mortality in the world. non-small cell lung carcinoma (NSCLC) accounts for about 85% of lung cancer, and its 5-year survival rate is about 19%. Mediastinal lymph node metastasis is a common metastasis pathway in non-small cell lung carcinoma (NSCLC), and its occurrence is closely related to lymphatic drainage pattern. The lymphatic drainage pattern of different lung lobe tumors is also different. Many studies have shown that the fourth and seventh stations of mediastinal lymph nodes are the areas with high incidence of lymph node metastasis. In particular, lymph node metastasis at station 4 was associated with poorer patient outcomes. Although systemic lymph node dissection usually includes at least three sets of mediastinal lymph nodes, including station 7, there is no uniform protocol for station 4 dissection. This situation has a negative impact on the stage and prognosis assessment of lung cancer patients. CT examination is an important tool to evaluate mediastinal lymph node status, but the accuracy is not high. The emerging CT radiomics has shown great application prospect in the accuracy of diagnosis of lymph node metastasis. The use of radiomics to evaluate the station lymph nodes is helpful to improve the accuracy of the diagnosis of lymph node metastasis, and it is also expected to provide a more scientific basis for determining the scope of lymph node dissection.

Objective: To predict the lymph node metastasis of the fourth mediastinal group by CT imaging, and to help determine the scope and stage of lymph node dissection by comparing with the pathological gold standard.

Study design: The clinical and pathological data of newly diagnosed non-small cell lung cancer patients admitted to the Department of Cardiothoracic Surgery of Qilu Hospital from 2017 to March 2024 were retrospectively collected. Clinicopathologic data and imaging data of 150 patients are expected to be collected. Inclusion criteria included patients who had undergone pathological examination of the fourth group of lymph nodes at initial visit and enhanced CT scan within two weeks prior to surgery. Lymph node Region of Interest (ROI) was sketched for all enrolled patients, and all lymph nodes were divided into metastatic and non-metastatic groups. The purpose of the study was to analyze the imaging data of these patients and integrate the corresponding clinical and pathological information, such as clinical factors: age, lung lobe, gender, image signs, etc, and pathological factors: pathological type, histological type, etc. In addition, the short diameter of each lymph node was measured to determine the metastasis rate under different short diameter criteria. Using machine learning technology to construct prediction model. The purpose of the model was to identify and extract the features of the fourth group of lymph nodes with and without metastasis. By careful comparison with the final pathological results, the investigators will evaluate the accuracy and effectiveness of the model in predicting the status of lymph node metastasis. The model can quantify the risk of lymph node metastasis and help doctors develop more personalized treatment plans.

Conditions

Non-small Cell Lung Cancer

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

Case

Artificial Intelligence

Intervention Type: DIAGNOSTIC_TEST

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.

Control

Artificial Intelligence

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. Surgical resection and systematic lymph node dissection were performed in the department of thoracic surgery, and the postoperative pathological findings were confirmed as NSCLC and complete pathological diagnostic data were retained.

2. Chest CT enhancement scan was completed within 2 weeks prior to surgery

3. Image quality meets analysis standards and clinical data is complete.

4. Lymph nodes that were pathologically confirmed to be metastatic or non-metastatic at station 4 were selected

Exclusion Criteria

1. Preoperative chemoradiotherapy or other treatment

2. Distant metastasis or other malignant tumors are present

3. Incomplete clinical data or image artifacts

4. No metastatic or non-metastatic lymph nodes were found at station 4

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Qilu Hospital of Shandong University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Qilu Hospital of Shandong University

Jinan, Shandong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Yanru Kang, postgraduate

Role: CONTACT

2083884573@qq.com

18334864091

Facility Contacts

Kang yanru, graduate student

Role: primary

2083884573@qq.com

18334864091

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Other Identifiers

KLYY-202404-041

Identifier Type: -

Identifier Source: org_study_id