Non-small Cell Lung CancerNCT06496360
Prediction of Mediastinal Station IV Lymph Node Metastasis in Non-small Cell Lung Cancer
An observational study of Non-small Cell Lung Cancer, run by Qilu Hospital of Shandong University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Non-small Cell Lung Cancer
- What is being tested?
- Artificial Intelligence
- How many people
- 150 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2024to Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The clinical and pathological data of newly diagnosed patients with non-small cell lung cancer admitted to cardiothoracic Surgery Department of Qilu Hospital were retrospectively 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
Who may be able to join
- 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.
- Chest CT enhancement scan was completed within 2 weeks prior to surgery
- Image quality meets analysis standards and clinical data is complete.
- Lymph nodes that were pathologically confirmed to be metastatic or non-metastatic at station 4 were selected
Who cannot join
- Preoperative chemoradiotherapy or other treatment
- Distant metastasis or other malignant tumors are present
- Incomplete clinical data or image artifacts
- No metastatic or non-metastatic lymph nodes were found at station 4
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital of Shandong University
Jinan, Shandong, 250063
Kang yanru, graduate student, 18334864091, 2083884573@qq.com
About this study
Official title: Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics
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.
Read the 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
- Being tested
- Artificial Intelligence
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Case | Not described Receives: Diagnostic Test: Artificial Intelligence | |
| Control | Not described Receives: Diagnostic Test: Artificial Intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the Curve(AUC)
To evaluate the ability and clinical practicability of the model to predict lymph node metastasis
Measured: June 2025
Receiver operator characteristic curve(ROC)
Sensitivity and specificity of different models under different thresholds
Measured: June 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jul 2024
- Study start1 Aug 2024
- Primary completionNov 2024 (estimated)
- Study completionJun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Non-small Cell Lung CancerStudies of the same condition, open and under way.
- More trials run by Qilu Hospital of Shandong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06496360
- Sponsor's protocol ID
- KLYY-202404-041
- Status last verified
- Apr 2024
- First submitted
- 12 Jun 2024
- Last updated
- 7 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48. doi: 10.3322/caac.21763.
PubMed 36633525Takano N, Ariyasu R, Koyama J, Sonoda T, Saiki M, Kawashima Y, Oguri T, Hisakane K, Uchibori K, Nishikawa S, Kitazono S, Yanagitani N, Ohyanagi F, Horiike A, Gemma A, Nishio M. Improvement in the survival of patients with stage IV non-small-cell lung cancer: Experience in a single institutional 1995-2017. Lung Cancer. 2019 May;131:69-77. doi: 10.1016/j.lungcan.2019.03.008. Epub 2019 Mar 21.
PubMed 31027701Zhou D, Yue D, Zhang Z, Tian P, Feng Y, Liu Z, Zhang B, Wang M, Zhao X, Wang C. Prognostic significance of 4R lymph node dissection in patients with right primary non-small cell lung cancer. World J Surg Oncol. 2022 Jul 1;20(1):222. doi: 10.1186/s12957-022-02689-w.
PubMed 35778770Shamji FM, Beauchamp G, Sekhon HJS. The Lymphatic Spread of Lung Cancer: An Investigation of the Anatomy of the Lymphatic Drainage of the Lungs and Preoperative Mediastinal Staging. Thorac Surg Clin. 2021 Nov;31(4):429-440. doi: 10.1016/j.thorsurg.2021.07.005.
PubMed 34696855Hanaoka J, Yoden M, Okamoto K, Kaku R, Ohshio Y. Mediastinal lymph node evaluation, especially at station 4L, in left upper lobe lung cancer. J Thorac Dis. 2022 Sep;14(9):3321-3334. doi: 10.21037/jtd-22-537.
PubMed 36245624Mascalchi M, Zompatori M. Mediastinal Lymphadenopathy in Lung Cancer Screening: A Red Flag. Radiology. 2022 Mar;302(3):695-696. doi: 10.1148/radiol.212501. Epub 2021 Nov 23. No abstract available.
PubMed 34812678Yoshida Y, Saeki N, Yotsukura M, Nakagawa K, Watanabe H, Yatabe Y, Watanabe SI. Visualization of patterns of lymph node metastases in non-small cell lung cancer using network analysis. JTCVS Open. 2022 Oct 13;12:410-425. doi: 10.1016/j.xjon.2022.10.003. eCollection 2022 Dec.
PubMed 36590713
Study at a glance
From the registry record, last updated by the sponsor on 7 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics
- ClinicalTrials.gov ID
- NCT06496360
- Acronym / sponsor ID
- KLYY-202404-041
- Conditions
- Non-small Cell Lung Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Study start
- 1 Aug 2024 (actual)
- Primary completion
- Nov 2024 (estimated)
- Study completion
- Jun 2025 (estimated)
- First posted
- 11 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Case | – | Diagnostic Test: Artificial Intelligence |
| Control | – | Diagnostic Test: Artificial Intelligence |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The clinical and pathological data of newly diagnosed patients with non-small cell lung cancer admitted to cardiothoracic Surgery Department of Qilu Hospital were retrospectively 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
Key inclusion criteria (4 listed)
- 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.
- Chest CT enhancement scan was completed within 2 weeks prior to surgery
- Image quality meets analysis standards and clinical data is complete.
- Lymph nodes that were pathologically confirmed to be metastatic or non-metastatic at station 4 were selected
Key exclusion criteria (4 listed)
- Preoperative chemoradiotherapy or other treatment
- Distant metastasis or other malignant tumors are present
- Incomplete clinical data or image artifacts
- No metastatic or non-metastatic lymph nodes were found at station 4
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial IntelligenceThe 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. | Diagnostic test | Case, Control |
Primary outcome
Area under the Curve(AUC)
Time frame: June 2025
To evaluate the ability and clinical practicability of the model to predict lymph node metastasis
Receiver operator characteristic curve(ROC)
Time frame: June 2025
Sensitivity and specificity of different models under different thresholds
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Aug 2024.
- Background: PubMed 36633525Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48. doi: 10.3322/caac.21763.
- Background: PubMed 31027701Takano N, Ariyasu R, Koyama J, Sonoda T, Saiki M, Kawashima Y, Oguri T, Hisakane K, Uchibori K, Nishikawa S, Kitazono S, Yanagitani N, Ohyanagi F, Horiike A, Gemma A, Nishio M. Improvement in the survival of patients with stage IV non-small-cell lung cancer: Experience in a single institutional 1995-2017. Lung Cancer. 2019 May;131:69-77. doi: 10.1016/j.lungcan.2019.03.008. Epub 2019 Mar 21.
- Background: PubMed 35778770Zhou D, Yue D, Zhang Z, Tian P, Feng Y, Liu Z, Zhang B, Wang M, Zhao X, Wang C. Prognostic significance of 4R lymph node dissection in patients with right primary non-small cell lung cancer. World J Surg Oncol. 2022 Jul 1;20(1):222. doi: 10.1186/s12957-022-02689-w.
- Background: PubMed 34696855Shamji FM, Beauchamp G, Sekhon HJS. The Lymphatic Spread of Lung Cancer: An Investigation of the Anatomy of the Lymphatic Drainage of the Lungs and Preoperative Mediastinal Staging. Thorac Surg Clin. 2021 Nov;31(4):429-440. doi: 10.1016/j.thorsurg.2021.07.005.
- Background: PubMed 36245624Hanaoka J, Yoden M, Okamoto K, Kaku R, Ohshio Y. Mediastinal lymph node evaluation, especially at station 4L, in left upper lobe lung cancer. J Thorac Dis. 2022 Sep;14(9):3321-3334. doi: 10.21037/jtd-22-537.
- Background: PubMed 34812678Mascalchi M, Zompatori M. Mediastinal Lymphadenopathy in Lung Cancer Screening: A Red Flag. Radiology. 2022 Mar;302(3):695-696. doi: 10.1148/radiol.212501. Epub 2021 Nov 23. No abstract available.
- Background: PubMed 36590713Yoshida Y, Saeki N, Yotsukura M, Nakagawa K, Watanabe H, Yatabe Y, Watanabe SI. Visualization of patterns of lymph node metastases in non-small cell lung cancer using network analysis. JTCVS Open. 2022 Oct 13;12:410-425. doi: 10.1016/j.xjon.2022.10.003. eCollection 2022 Dec.
- Individual participant dataSharing planned: No
Related trials for Non-small Cell Lung Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
A Prospective ,Multicenter ,Non-interventional ,Real-world Study Observing Changes in Molecular Markers Osimertinib Targeted Therapy in EGFRm NSCLC
Non Small Cell Lung Cancer
RecruitingPhase 3
A Study Evaluating Sotorasib Platinum Doublet Combination Versus Pembrolizumab Platinum Doublet Combination as a Front-Line Therapy in Participants With Stage IV or Advanced Stage IIIB/C Nonsquamous Non-Small Cell Lung Cancers (CodeBreaK 202)
Non-Small Cell Lung Cancer (NSCLC)
Active, not recruitingPhase 2
Datopotamab Deruxtecan (Dato-DXd, DS-1062a) in Advanced and/or Unresectable Non-Small Cell Lung Cancer
Metastatic Lung Cancer, Advanced Non Small Cell Lung Cancer
Not yet recruitingPhase 1/2
STAR0602 With Nivolumab in Non Small Cell Lung Cancer and in Metastatic Castrate-Resistant Prostate Cancer, and STAR0602 Alone in Thymic Carcinoma
NSCLC, Non-Small Cell Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-small Cell Lung Cancer, run by Qilu Hospital of Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yanru Kang, postgraduate (18334864091, 2083884573@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the Curve(AUC) (time frame: June 2025). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06496360, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Aug 2024.