Journals / Yüksek İhtisas Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Cilt: 6 Sayı: 2

Can Computer-Aided CT Analysis Redefine Mediastinal Lymph Node Diagnosis?

Şeref Barbaros Arık(YÜKSEK İHTİSAS ÜNİVERSİTESİ)0000-0001-6030-977X
Kadir Canoğlu(SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, İSTANBUL SULTAN ABDÜLHAMİD HAN SAĞLIK UYGULAMA VE ARAŞTIRMA MERKEZİ)0000-0003-1579-3392
Pages
41–47
Publication Date
2025-09-07
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, advanced imaging and AI-driven three-dimensional analyses may enhance diagnostic accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Abstract Background: Accurate mediastinal lymph node evaluation is essential in the diagnosis and staging of malignant tumors.
📊
Key Findings & Data:Methods: A retrospective analysis of 103 patients with 172 lymph nodes.
💡
Conclusion & Impact:However, advanced imaging and AI-driven three-dimensional analyses may enhance diagnostic accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Mediastinal Lymph Nodes · Computed Tomography (CT) · Lung Cancer

Abstract

Abstract Background: Accurate mediastinal lymph node evaluation is essential in the diagnosis and staging of malignant tumors. Traditional size-based criteria pose challenges, particularly for smaller nodes, necessitating alternative approaches. Objective: To investigate the diagnostic value of volume, calculated diameter, and HU values obtained using computer-aided software in distinguishing between benign and malignant mediastinal lymph nodes on contrast-enhanced and non-contrast thoracic CT. Methods: A retrospective analysis of 103 patients with 172 lymph nodes. Quantitative metrics were derived using the "Vitrea Lung Nodule Analysis" software. Diagnostic performance was assessed using ROC analysis, and statistical associations were examined through univariate and multivariate logistic regression models. Results: Age and male gender were significant predictors of malignancy (p<0.001). While HU values and calculated parameters showed limited diagnostic performance, multivariate analysis revealed significant effects of lymph node diameter and multiple nodes on malignancy classification (p<0.05). The model achieved an AUC of 0.804, with sensitivity and specificity of 0.815 and 0.684, respectively. Conclusion: Despite incorporating entire lymph nodes in quantitative assessments, HU values and volumes demonstrated limited clinical utility. However, advanced imaging and AI-driven three-dimensional analyses may enhance diagnostic accuracy.

Özet

Background: Accurate mediastinal lymph node evaluation is essential in the diagnosis and staging of malignant tumors. Traditional size-based criteria pose challenges, particularly for smaller nodes, necessitating alternative approaches. Objective: To investigate the diagnostic value of volume, calculated diameter, and HU values obtained using computer-aided software in distinguishing between benign and malignant mediastinal lymph nodes on contrast-enhanced and non-contrast thoracic CT. Methods: A retrospective analysis of 103 patients with 172 lymph nodes. Quantitative metrics were derived using the "Vitrea Lung Nodule Analysis" software. Diagnostic performance was assessed using ROC analysis, and statistical associations were examined through univariate and multivariate logistic regression models. Results: Age and male gender were significant predictors of malignancy (p<0.001). While HU values and calculated parameters showed limited diagnostic performance, multivariate analysis revealed significant effects of lymph node diameter and multiple nodes on malignancy classification (p<0.05). The model achieved an AUC of 0.804, with sensitivity and specificity of 0.815 and 0.684, respectively. Conclusion: Despite incorporating entire lymph nodes in quantitative assessments, HU values and volumes demonstrated limited clinical utility. However, advanced imaging and AI-driven three-dimensional analyses may enhance diagnostic accuracy.

🔖

Cite This Article

Arık, Ş. B., Canoğlu, K. (2025). Can Computer-Aided CT Analysis Redefine Mediastinal Lymph Node Diagnosis?. Yüksek İhtisas Üniversitesi Sağlık Bilimleri Dergisi, Cilt: 6 Sayı: 2, 41–47. https://ajindex.com/articles/1647732

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (21)

  1. Panunzio A, Sartori P. Lung cancer and radiological imaging. Curr Radiopharm. 2020;13(3):238–242. https://doi.org/10.2174/1874471013666200523161849
  2. Barta JA, Powell CA, Wisnivesky JP. Global epidemiology of lung cancer. Ann Glob Health. 2019;85(1). https://doi.org/10.5334/aogh.2419
  3. Ning J, Ge T, Jiang M, Jia K, Wang L, Li W, et al. Early diagnosis of lung cancer: which is the optimal choice? Aging (Albany NY). 2021;13(4):6214 6227. https://doi.org/10.18632/aging.202504
  4. Polat G, Polat M, Meletlioglu E. Effect of contrast medium on early detection and analysis of mediastinal lymph nodes in computed tomography (1992). Rev Assoc Med Bras. 2023;69(3):392–397. https://doi.org/10.1590/1806 9282.20220869
  5. Purandare NC, Rangarajan V. Imaging of lung cancer: implications on staging and management. Indian J Radiol Imaging. 2015;25(2):109–120. https://doi. org/10.4103/0971-3026.155831
  6. Xia Y, Zhang B, Zhang H, Li W, Wang KP, Shen H. Evaluation of lymph node metastasis in lung cancer: who is the chief justice? J Thorac Dis. 2015;7(Suppl 4):S231-237.
  7. Ganeshalingam S, Koh DM. Nodal staging. Cancer Imaging. 2009;9(1):104 111. https://doi.org/10.1102/1470-7330.2009.0017
  8. Teran MD, Brock MV. Staging lymph node metastases from lung cancer in the mediastinum. J Thorac Dis. 2014;6(3):230–236. https://doi.org/10.3978/j. issn.2072-1439.2013.12.18
  9. Hegde P, Molina JC, Thivierge-Southidara M, Jain RV, Gowda A, Ferraro P, et al. Combined endosonographic mediastinal lymph node staging in positron emission tomography and computed tomography node-negative non small-cell lung cancer in high-risk patients. Semin Thorac Cardiovasc Surg. 2020;32(1):162–168. https://doi.org/10.1053/j.semtcvs.2019.07.007
  10. Sato T, Shimada Y, Mimae T, Tsutani Y, Miyata Y, Ito H, et al. The impact of pathological lymph node metastasis with lymphatic invasion on the survival of patients with clinically node-negative non-small cell lung cancer: a multicenter study. Lung Cancer. 2021;158:9–14. https://doi.org/10.1016/j. lungcan.2021.05.029
  11. Huang S, Meng H, Cen R, Ni Z, Li X, Suwal S, et al. Use quantitative parameters in spectral computed tomography for the differential diagnosis of metastatic mediastinal lymph nodes in lung cancer patients. J Thorac Dis. 2021;13(8):4703–4713. https://doi.org/10.21037/jtd-21-385
  12. Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora J, Abraira V, Roque IFM. PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev. 2014;2014(11):CD009519. https://doi.org/10.1002/14651858.CD009519.pub2
  13. Iyer H, Anand A, Sryma PB, Gupta K, Naranje P, Damle N, et al. Mediastinal lymphadenopathy: a practical approach. Expert Rev Respir Med. 2021;15(10):1317–1334. https://doi.org/10.1080/17476348.2021.1920404
  14. Chalian H, McAdams HP, Lee Y, Duan F, Wu Y, Khoshpouri P, et al. Mediastinal lymphadenopathy in the national lung screening trial (NLST) is associated with interval lung cancer. Radiology. 2022;302(3):684–692. https://doi.org/10.1148/ radiol.210522
  15. Gao L, Lu X, Wen Q, Hou Y. Added value of spectral parameters for the assessment of lymph node metastasis of lung cancer with dual-layer spectral detector computed tomography. Quant Imaging Med Surg. 2021;11(6):2622 2633. https://doi.org/10.21037/qims-20-1045
  16. Zhaoming G, Zhenfa Z. Lymph node evaluation and surgical procedure selection for non-small cell lung cancer. Holistic Integrative Oncology 2024;3(1). https://doi.org/10.1007/s44178-024-00070-3
  17. Dollin Y, Munoz Pineda JA, Sung L, Hasteh F, Fortich M, Lopez A, et al. Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review. Mediastinum. 2024;8:51. https:// doi.org/10.21037/med-24-32
  18. Yang Y, Zheng B, Li Y, Li Y, Ma X. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT. Med Phys. 2023;50(1):152–162. https:// doi.org/10.1002/mp.15901
  19. Ding N, Mao Y, Gao S, Xue Q, Wang D, Zhao J, et al. Predictors of lymph node metastasis and possible selective lymph node dissection in clinical stage IA non-small cell lung cancer. J Thorac Dis. 2018;10(7):4061–4068. https://doi. org/10.21037/jtd.2018.06.129
  20. Ji H, Hu C, Yang X, Liu Y, Ji G, Ge S, et al. Lymph node metastasis in cancer progression: molecular mechanisms, clinical significance and therapeutic interventions. Signal Transduct Target Ther. 2023;8(1):367. https://doi. org/10.1038/s41392-023-01576-4
  21. Fiterman N, Berkman N, Kuint R. Predictors of malignant lymph node involvement in patients with mediastinal lymphadenopathy and previous cancer: a cohort study. Thorac Cancer. 2022;13(4):631–636. https://doi. org/10.1111/1759-7714.14311