Diagnostic Value Of IOTA LR2 And ADNEX Models In Ovarian Benign And Malignant Tumors: A Clinical Study

Objective:Using a clinical research to explore on the diagnostic value of IOTA LR2 model and ADNEX model for ovarian benign and malignant tumors.Method:Collecting a total of 347 patients who were hospitalized and operated due to ovarian tumors from September 2018 to June 2020 in the First Affiliated Hospital of Shantou University Medical College and Shantou University Medical College Tumor Hospital.All patients received ultrasound examination and serological examination before operation.The ultrasound examination focused on evaluating the nature of ovarian masses and collecting the indicators required by the LR2 model and the ADNEX model.This study obtained the malignant risk value by LR2 and ADNEX model software,and traced their pathological results after surgery.The pathological results are used as the gold standard to calculate the area under the ROC curve,sensitivity,specificity,positive predictive value,negative predictive value and diagnostic coincidence rate respectively in the LR2 model and ADNEX model.Results:This study uses 347 cases in total,consisting of 196 benign tumors and 151 malignant tumors.1.The area under the ROC curve of the LR2 model was 0.818(95%CI 0.772-0.864),the sensitivity was 92.7%(95%CI: 87.0%-96.1%);the specificity was 70.9%(95%CI:63.9%-77.1%);the positive predictive value was 71.0%(95% CI: 64.1%-77.2%);the negative predictive value was 92.7%(95%CI:86.9%-96.1%);the diagnostic coincidence rate was 80.4%.2.The area under the ROC curve of the ADNEX model was 0.812(95%CI0.767-0.852);the sensitivity of this model was 78.8%(95%CI:79.2%-90.9%);the specificity was 83.7%(95%CI:72.6%-84.4%);the positive predictive value was 78.8%(95% CI: 71.2%-84.8%);the negative predictive value was 83.7%(95%CI:77.5%-84.9%);the diagnostic coincidence rate was 82.1%.3.In predicting different stages of malignant tumors(borderline,stage I,stage II-IV,secondary metastatic tumors),the ADNEX model has better diagnostic efficiency for stage II-IV malignant tumors and secondary metastatic tumors.In distinguishing five different pathological types of ovary(benign,borderline,stage I,stage II-IV and secondary metastatic tumors),the ADNEX model shows its best diagnostic efficiency in distinguishing benign tumors vs stage II-IV tumors,and the area under the ROC curve was 0.95.It has the worst diagnostic efficiency for distinguishing borderline tumors vs stage I tumors,with an area under the ROC curve of 0.65.4.Without the CA125 value,the AUC value of the ADNEX model was 0.797(95%CI:0.751-0.83);the sensitivity was 80.7%(95%CI: 73.4%-86.6%);the specificity was 78.5%(95%CI: 72.0%-83.9%);the positive predictive value was 74.3%(95%CI: 66.9%-80.7%);the negative predictive value was 84.2(95%CI: 77.8%-88.9%).In comparison,the diagnostic efficiency is better when the ADNEX model includes CA125 value,but its difference is not statistically significant as P>0.05.5.When setting different thresholds,the ADNEX model performs different diagnostic efficiency,among which the best threshold in this study was >40.9% with a sensitivity of 86.1%and a specificity of 79.1%.6.Compared with the ADNEX model,the LR2 model has relatively better diagnostic efficiency,but their difference is not statistically significant.The sensitivity of LR2 is higher,while the specificity of ADNEX model is higher.Conclusions1.The IOTA LR2 model and ADNEX model are of certain value in the diagnosis of ovarian benign and malignant tumors.The diagnostic efficiency of the LR2 model is slightly better than that of the ADNEX model.2.The ADNEX model has a certain value in distinguishing the stage of ovarian malignant tumors,especially benign and stage II-IV malignant tumors,but the diagnostic efficiency in distinguishing borderline and stage I tumors is relatively poor,and further research is needed to improve the diagnostic efficiency.3.The level of serum CA125 will slightly reduce the diagnostic efficacy of the ADNEX model,However,CA125 is not the best tumor marker in ovarian cancer diagnosis.4.The best cut-off value of the ADNEX model in this research data is> 40.9%.The optimal cutoff value of the ADNEX model is needs further study.