Lymph nodes and vessels are important spread way of lung adenocarcinoma metastasis. In clinical, systemic lymph node dissection is still dominant in lung adenocarcinoma surgery to clarify the tumor stage and predict the prognosis7. However, the scope of lymph node dissection is controversial, especially for early stage lung cancer8–10. Although imageology have a great improvement in these days, lymph nodes status could be evaluated by PET/CT, there still some cases would be found LNI unexpectedly after surgery. What’s more, to perform the systematic lymph node dissection in all early stage cases would increase complications and damage health tissue. Recently, many lymph node dissection methods have been recommended, like selective lymph node dissection, lpbespecific lymph node dissection, lymph node sampling. For the early stage NSCLC patients, the methods of lymph node dissection seemed not imfluence the disease free survival and overall survival11,12. Considering this, to develop a prediction tool to improve the accurate of LNI evaluation is meaningful.
In 2016, Yu et al identified 2268 operable lung adenocarcinoma patients with tumor size less than 3 cm to clarify the risk factors for lymph node involvement and they found that the subtype of lung adenocarcinoma played the most important roles13. In 2017, Haruki et al have retrospectively reviewed 225 lung adenocarcinoma patients to demonstrate the clinicopathological characteristics for lung adenocarcinoma with unexpected lymph node metastasis14. They found younger age, left side tumor, larger tumor size and micropapillary component were significant associated with LNI by multivariate analysis. However, some of these factors reported in these articles could not be obtained preoperative which restricted the application the model. Lye et al reported the result of FDG PET/CT in predicting lymph node metastasis for T1a lung adenocarcinoma. The the sensitivity and specificity for predicting occult lymph node metastasis were 90.0% and 61.7%, with the C-index was 0.76115.. Zhao et al constructed a 3D deep learning model for accurate lymph node metastasis prediction in T1 lung adenocarcinoma16. They enrolled 501 patients and trained with 401 cases among them. The model achieved an C-index of 0.926, which was impressive high and seemed to have a bright future. But the methods required doctors have the knowledge of computer radiomics which could not be put into use in short time.
In this study, we developed a novel nomogram that could predict the risk of having the lymph node involvement based on 326 patients’ clinicopathological parameter. Finally four routine preoperative factors were included: tumor diameter, the presence of lymph node swelling, PLR and CEA. Multivariate logistic regression analysis help us selected the strongest factors and simplify the model as much as we can. The finally discriminative ability of this nomogram was examined and the 0.875 as C-index was high than former simple model. Tumor diameter could influence the LNI probability was accordance with our knowledge before. Wu et al revealed that tumor size parameters based on CT scan were significantly correlated with tumor invasiveness, and the evaluation of m-CT value was most useful musurement in predicting more invasive lung cancer17. Role of preoperative CT scan still is the most common method to stage the NSCLC, even the radiomics have acquired more and more importance. Platelets has been recognized as an increasingly functional factors in the pathogenesis of different cancer18. PLR was a parameter depends on platelet and lymphocyte counts, is a representative index of systemic inflammation. Its prognostic and prediction value in many types of malignance have been demonstrated19–21. The potential prognostic role of PLR in lung adenocarcinoma have also been revealed22,23. CEA is a classic oncofetal protein attached to epithelial-cell apical membrane via its c-terminal glycosylphosphatidylinositolanchor24. High CEA level was also reported have association with NSCLC25,26. High CEA always indict a bad prognostic. In a retrospective study conducted by Wang et al, they found that CEA and PLR were independent risk factors for brain metastasis of lung adenocarcinoma and could be used as predictors for brain metastasis predictability27.However, in a prospective study conducted by Reinmuth et al, CEA showed no prognostic impart for NSCLC28. In our study,
The limitations of this study included its retrospective and single center design. Although we have collected 326 cases, the sample size was still too small. Lack of the information of tumor subtype is another weakness as biopsy preoperative is quite rare and we could not accurate diagnose this without pathology. So as pleural and duct invasion and other pathological feathers.
As far as we know, this is first nomogram for predicting the lymph node metastasis in lung adenocarcinoma with preoperative information. We hope this model could be used and validated by other medical centers around the world.