| Background:Cervical cancer(CC)is one of the four most common cancers in the world.With the improvement of people’s screening awareness and the worldwide application of Human papilloma virus(HPV)vaccine,the overall incidence and mortality of CC are showing a downward trend.In contrast,the incidence of early cervical cancer has increased.According to statistics,15%-20% of patients with early cervical cancer have Lymph node metastasis(LNM),and LNM is one of the main risk factors affecting the prognosis of patients with early cervical cancer,and the survival rate of patients with lymph node metastasis is significantly decreased.And according to the latest guidelines,the presence or absence of lymph node metastasis determines the stage of cervical cancer patients,and the different stages directly affect the choice of treatment.Among them,surgery is the first choice for patients with stage ⅠA2-ⅠB2 and some stage ⅠB3-ⅡA1 cervical cancer.If lymph node metastasis is found before operation,radiotherapy and chemotherapy are the first choice.Therefore,preoperative evaluation of lymph node metastasis is very important.Patients with negative lymph node metastasis undergoing surgical treatment can avoid extensive lymph node dissection,reduce the occurrence of postoperative complications,and improve the prognosis of patients.At present,there are many methods to evaluate lymph node metastasis,but they all have certain limitations.Therefore,it is necessary to continue to search for simple and easy to obtain indicators to evaluate the lymph node metastasis of cervical cancer.In recent years,a large number of literatures at home and abroad have studied the related factors affecting lymph node metastasis of cervical cancer.Among them,it is found that NLR and SCC-Ag have a certain correlation with lymph node metastasis,and they are easy to obtain,but there is no unified conclusion on the best cut-off value.Moreover,there are relatively few studies on risk prediction models for cervical cancer lymph node metastasis.Objective:This study analyzed the relevant factors affecting lymph node metastasis in stage ⅠA2-ⅡA1 cervical cancer,this study constructs a nomogram prediction model for lymph node metastasis in patients with stageⅠA2-ⅡA1 cervical cancer,which helps clinicians to predict the risk of lymph node metastasis more simply,accurately and intuitively,and provide reference for formulating individualized treatment plans and judging the prognosis of patients.Methods:The clinical data of patients with stage ⅠA2-ⅡA1 cervical cancer admitted to the Obstetrics and Gynecology Department of the First Affiliated Hospital of Henan University from January 2018 to October2022 were collected,and the clinical data of 169 patients who met the inclusion and exclusion criteria were retrospectively analyzed.Clinical data included age,weight,height,menopausal status,HPV infection status,pathological type,degree of tissue differentiation,tumor diameter,International Federation of Gynecology and Obstetrics(FIGO)staging,depth of stromal invasion,lymph node metastasis,the absolute value of neutrophil cells,the absolute value of monocyte cells,platelet counts,the absolute value of lymphocytes,fibrinogen,the absolute value of eosinophilic granulocytes,and squamous cell carcinoma antigen(SCC-Ag),among them,the hematological indicators were the results of the last examination before surgery.Body mass index(BMI),Platelet-to-lymphocyte ratio(PLR)and Neutrophil-to-lymphocyte ratio(NLR)were calculated based on the above data.According to postoperative pathological lymph node metastasis,the patients were divided into lymph node non-metastasis group(n=128)and lymph node metastasis group(n=41).SPSS 26.0 software was used to analyze the data.The risk factors related to lymph node metastasis obtained by univariate analysis were included in multivariate analysis to screen out the independent risk factors for lymph node metastasis of cervical cancer.Using R language 4.2.2 software,the independent risk factors were included to draw the nomogram prediction model.Receiver operating characteristic(ROC)Curve,Bootstrap self-sampling method and internal validation calibration curve were used to evaluate the discrimination and calibration of the model.The clinical application value of the model was evaluated by Decision Curve Analysis(DCA).Results:1.Results of univariate analysis: There were significant differences in HPV infection status,degree of differentiation,depth of stromal invasion,NLR and SCC-Ag between the two groups(P < 0.05).There were no significant differences in age,BMI,menopausal status,tumor diameter,pathological type,FIGO staging,the absolute value of eosinophilic granulocytes,the absolute value of monocyte cells,fibrinogen and PLR between the two groups(P > 0.05).2.ROC Curve analysis showed that: The Area Under the Curve(AUC)of NLR and SCC-Ag were 0.716 and 0.763,respectively.The maximum Youden index of NLR and SCC-Ag were 0.473 and 0.516,respectively.The optimal cut-off values corresponding to the maximum Youden index of NLR and SCC-Ag were 2.795 and 2.240ng/ml,respectively.The sensitivity of NLR and SCC-Ag were 70.7% and 80.5%,and the specificity were 76.6% and 71.1%,respectively.According to the optimal cut-off value,NLR and SCCAg greater than or equal to the optimal cut-off value were divided into the high level group,and less than the optimal cut-off value was divided into the low level group.Univariate analysis was performed on the categorical variables NLR and SCC-Ag.The results showed that NLR(X~2 = 30.567,P <0.001),SCC-Ag(X~2= 34.051,P <0.001)were correlated with lymph node metastasis(P < 0.05).3.Multi-factor analysis shows: NLR≥2.795(OR=5.608,95%CI: 2.221-14.157,P < 0.001),SCCAg≥2.240ng/ml(OR=6.621,95%CI: 2.495-17.571,P < 0.001),depth of stromal invasion ≥ 1/2(OR=4.217,95%CI: 1.558-11.417,P=0.005)and the tissue low differentiation(OR=2.969,95%CI: 1.157-7.620,P=0.024)were independent risk factors for LNM in stage ⅠA2-ⅡA1 cervical cancer(P < 0.05).4.Establishment of nomogram prediction model: The independent risk factors screened by multivariate analysis were used to construct a nomogram prediction model using R language.When NLR≥2.795 was assigned a value of 1,the corresponding individual score was 91.25.SCC-Ag≥2.240ng/ml was assigned a value of 1,and the corresponding individual score was 100 points.When the depth of stromal invasion was≥1/2,the value was 1,and the corresponding individual score was 76.25.Poorly differentiated tissue is assigned a value of 1,which corresponds to a single item score of 57.5.5.Evaluation of the nomogram prediction model: The ROC curve of the model showed that the AUC was 0.877(95%CI: 0.816-0.938),indicating that the model had good discrimination.The Bootstrap selfsampling method was repeated for 1000 times,and the consistency index(C-index)was 0.860,indicating that the model had good accuracy.The mean absolute error of the internal validation calibration curve was0.022,indicating that the model was well calibrated.Decision curve analysis showed that when the threshold probability was in the range of 1.3% to 87.2%,the net benefit was better,suggesting that the model had good clinical application value.Conclusion:1.NLR≥2.795,SCC-Ag≥2.240ng/ml,and depth of stromal invasion≥1/2,and the tissue low differentiation were independent risk factors for lymph node metastasis in stage ⅠA2-ⅡA1 cervical cancer(P< 0.05).2.The nomogram prediction model for the risk of lymph node metastasis in stage ⅠA2-ⅡA1 cervical cancer constructed in this study has good discrimination,calibration and clinical application value,which can provide help for clinicians to formulate accurate and individualized treatment plans and evaluate the prognosis of patients. |
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