Ignite Creation Date:
2024-07-17 @ 11:47 AM
Last Modification Date:
2024-10-26 @ 3:34 PM
Study NCT ID:
NCT06496360
Status:
RECRUITING
Last Update Posted:
2024-07-11
First Post:
2024-06-12
Brief Title:
Prediction of Mediastinal Station IV Lymph Node Metastasis in Non-small Cell Lung Cancer
Sponsor:
Qilu Hospital of Shandong University
Organization:
Qilu Hospital of Shandong University
Study Overview
Official Title:
Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics
Status:
RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None