Developing a Nomogram for Preoperative Prediction of Cervical Cancer Lymph Node Metastasis by Multiplex Immunofluorescence

Abstract

Abstract Background Most traditional procedures can destroy tissue natural structure, and the information on spatial distribution and temporal distribution of immune milieu in situ would be lost. We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC. Methods Patients (180 IB1-IIA2 CC patients of 2009 FIGO (International Federation of Gynecology and Obstetrics)) were divided into two groups based on pLN status. Tissue microarray (TMA) was prepared and tumor-infiltrating immune markers were assessed by mIF. Multivariable logistic regression analysis and nomogram were used to develop the predicting model. Results Multivariable logistic regression analysis constructs a predictive model and the area under the curve (AUC) can reach 0.843. By internal validation with the remaining 40 percent of cases, a new ROC curve has emerged and the AUC reached 0.888. Conclusions This study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC.