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A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time

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Abstract

Purpose

Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis.

Methods

We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA).

Results

Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%.

Conclusions

The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.

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References

  1. Collaboration GB of DC, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017. Jama Oncol. 2019;5:1749–68.

  2. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WEE, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (Eighth) Edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11:39–51.

    Article  PubMed  Google Scholar 

  3. National Comprehensive Cancer Network clinical practice guidelines in oncology non-small cell lung cancer. Version 3.2022.

  4. Birim Ö, Kappetein AP, Stijnen T, Bogers AJJC. Meta-analysis of positron emission tomographic and computed tomographic imaging in detecting mediastinal lymph node metastases in non-small cell lung cancer. Ann Thorac Surg. 2005;79:375–82.

    Article  PubMed  Google Scholar 

  5. Shen G, Lan Y, Zhang K, Ren P, Jia Z. Comparison of 18F-FDG PET/CT and DWI for detection of mediastinal nodal metastasis in non-small cell lung cancer: a meta-analysis. PLoS ONE. 2017;12: e0173104.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Houdu B, Lasnon C, Licaj I, Thomas G, Do P, Guizard A-V, et al. Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. Eur J Nucl Med Mol. 2019;I(46):421–8.

    Article  Google Scholar 

  7. Dhopeshwarkar MR, Roberts HC, Paul NS, Dong Z, Tsao M, Menezes RJ. Screen-detected Lung cancer a retrospective analysis of CT appearance. Acad Radiol. 2011;18:1270–6.

    Article  PubMed  Google Scholar 

  8. Muraoka Y, Yoshida Y, Nakagawa K, Ito K, Watanabe H, Narita T, et al. Maximum standardized uptake value of the primary tumor does not improve candidate selection for sublobar resection. J Thorac Cardiovasc Surg. 2022;163:1656-1665.e3.

    Article  PubMed  Google Scholar 

  9. Dong X, Qiu X, Liu Q, Jia J. Endobronchial ultrasound-guided transbronchial needle aspiration in the mediastinal staging of non-small cell lung cancer: a meta-analysis. Ann Thorac Surg. 2013;96:1502–7.

    Article  PubMed  Google Scholar 

  10. Xu DM, Gietema H, de Koning H, Vernhout R, Nackaerts K, Prokop M, et al. Nodule management protocol of the NELSON randomized lung cancer screening trial. Lung Cancer. 2006;54:177–84.

    Article  PubMed  Google Scholar 

  11. Xu DM, van der Zaag-Loonen HJ, Oudkerk M, Wang Y, Vliegenthart R, Scholten ET, et al. Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON Study: cancer risk during 1 year of follow-up. Radiology. 2009;250:264–72.

    Article  PubMed  Google Scholar 

  12. Setojima Y, Shimada Y, Tanaka T, Shigefuku S, Makino Y, Maehara S, et al. Prognostic impact of solid-part tumor volume doubling time in patients with radiological part-solid or solid lung cancer. Eur J Cardio-thorac. 2019. https://doi.org/10.1093/ejcts/ezz305.

    Article  Google Scholar 

  13. Miura K, Hamanaka K, Koizumi T, Kawakami S, Kobayashi N, Ito K. Solid component tumor doubling time is a prognostic factor in non-small cell lung cancer patients. J Cardiothorac Surg. 2019;14:57.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Park S, Lee SM, Kim S, Lee J-G, Choi S, Do K-H, et al. Volume doubling times of lung adenocarcinomas: correlation with predominant histologic subtypes and prognosis. Radiology. 2020;295: 191835.

    Article  Google Scholar 

  15. Nakahashi K, Shiono S, Nakatsuka M, Endo M. Prediction of lymph node metastasis of clinical stage IA non-small cell lung cancer based on the tumor volume doubling time. Surg Today. 2022;52:1063–71.

    Article  PubMed  Google Scholar 

  16. Usuda K, Saito Y, Sagawa M, Sato M, Kanma K, Takahashi S, et al. Tumor doubling time and prognostic assessment of patients with primary lung cancer. Cancer. 1994;74:2239–44.

    Article  PubMed  CAS  Google Scholar 

  17. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565–74.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Moreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the international association for the study of lung cancer pathology committee. J Thorac Oncol. 2020;15:1599–610.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang Y, Sun Y, Xiang J, Zhang Y, Hu H, Chen H. A prediction model for N2 disease in T1 non–small cell lung cancer. J Thorac Cardiovasc Surg. 2012;144:1360–4.

    Article  PubMed  Google Scholar 

  20. Wang Z, Wu Y, Wang L, Gong L, Han C, Liang N, et al. Predicting occult lymph node metastasis by nomogram in patients with lung adenocarcinoma ≤2 cm. Future Oncol. 2021;17:2005–13.

    Article  PubMed  CAS  Google Scholar 

  21. Kent M, Landreneau R, Mandrekar S, Hillman S, Nichols F, Jones D, et al. Segmentectomy versus wedge resection for non-small cell lung cancer in high-risk operable patients. Ann Thorac Surg. 2013;96:1747–55.

    Article  PubMed  Google Scholar 

  22. Mokhles S, Macbeth F, Treasure T, Younes RN, Rintoul RC, Fiorentino F, et al. Systematic lymphadenectomy versus sampling of ipsilateral mediastinal lymph-nodes during lobectomy for non-small cell lung cancer: a systematic review of randomized trials and a meta-analysis. Eur J Cardio-thorac. 2017;51:1149–56.

    Article  Google Scholar 

  23. Shimizu K, Yoshida J, Nishimura M, Takamochi K, Nakahara R, Nagai K. Treatment strategy for chylothorax after pulmonary resection and lymph node dissection for lung cancer. J Thorac Cardiovasc Surg. 2002;124:499–502.

    Article  PubMed  Google Scholar 

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Funding

We declare that no funds, grants, or other support was received for this manuscript.

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Authors and Affiliations

  1. Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan

    Ryo Karita, Hidemi Suzuki, Yuki Onozato, Taisuke Kaiho, Terunaga Inage, Takamasa Ito, Kazuhisa Tanaka, Yuichi Sakairi & Ichiro Yoshino

  2. Department of Thoracic Surgery, International University of Health and Welfare School of Medicine, Chiba, Japan

    Ryo Karita, Yuki Onozato & Ichiro Yoshino

Authors
  1. Ryo Karita
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  2. Hidemi Suzuki
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  3. Yuki Onozato
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  4. Taisuke Kaiho
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  5. Terunaga Inage
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  6. Takamasa Ito
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  9. Ichiro Yoshino
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Correspondence to Hidemi Suzuki.

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Supplementary Information

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595_2023_2695_MOESM1_ESM.pdf

Supplementary file1 (PDF 145 KB) Supplementary Fig. 1. Nomogram to predict pN2 for patients with cN0M0 lung cancer. A nomogram predicting mediastinal lymph node metastasis in the 560 patients was constructed based on tumor markers and imaging findings. SCD solid component diameter, CTR consolidation tumor ratio, SVDT solid component volume doubling time, CEA carcinoembryonic antigen

595_2023_2695_MOESM2_ESM.pdf

Supplementary file2 (PDF 479 KB) Supplementary Fig. 2. Predictive performance of the nomogram for pN2. a ROC curve for the nomogram for the selected variables and each variable. The area under the ROC curve was 0.795 (95% CI 0.741-0.848). b Calibration curve of the nomogram. The solid line is the bias-corrected curve after 1000 bootstrap sampling iterations. c DCA of the nomogram. DCA showed clinical usefulness with a range of 0% to 26% and 31% to 50%. AUC area under the curve, ROC receiver operating characteristic, DCA decision curve analysis

Supplementary file3 (DOCX 23 KB)

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Karita, R., Suzuki, H., Onozato, Y. et al. A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time. Surg Today 54, 31–40 (2024). https://doi.org/10.1007/s00595-023-02695-9

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  • DOI: https://doi.org/10.1007/s00595-023-02695-9

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