Clinical trial · Observational
Prediction of Mediastinal Station IV Lymph Node Metastasis in Non-small Cell Lung Cancer
Prediction Model of Mediastinal Group IV Lymph Node Metastasis in Non-small Cell Lung Cancer Based on CT Radiomics
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Non-small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial Intelligence | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Case
- interventionNames
- Diagnostic Test: Artificial Intelligence
- label
- Control
- interventionNames
- Diagnostic Test: Artificial Intelligence
Primary outcomes (2)
- measure
- Area under the Curve(AUC)
- timeFrame
- June 2025
- description
- To evaluate the ability and clinical practicability of the model to predict lymph node metastasis
- measure
- Receiver operator characteristic curve(ROC)
- timeFrame
- June 2025
- description
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (7)
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- BACKGROUNDTakano 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. PMID 31027701
- BACKGROUNDZhou 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. PMID 35778770
- BACKGROUNDShamji 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. PMID 34696855
- BACKGROUNDHanaoka 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. PMID 36245624
- BACKGROUNDMascalchi 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. PMID 34812678
- BACKGROUNDYoshida 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.