Jiao Hu1, Huihuang Li1, Tongchen He1, Hao Deng1, Guanghui Gong2, Yu Cui1, Peihua Liu1, Wenbiao Ren1, Chao Li1, Jinbo Chen3, Xiongbing Zu4. 1. Department of Urology, Xiangya Hospital, Central South University, Changsha, China. 2. Department of Pathology, Xiangya Hospital, Central South University, Changsha, China. 3. Department of Urology, Xiangya Hospital, Central South University, Changsha, China. Electronic address: chenjinbo1989@yahoo.com. 4. Department of Urology, Xiangya Hospital, Central South University, Changsha, China. Electronic address: zuxbxyyy@126.com.
Abstract
BACKGROUND: Accurate preoperative prediction of inguinal lymph node metastasis (LNM) aids in clinical decision making, especially for patients with penile cancer with clinically negative lymph nodes. We aim to develop a nomogram to predict the preoperative risk of LNM by incorporating clinicopathologic features and tumor biomarkers. METHODS: Eighty-four patients with penile cancer with clinically negative lymph nodes were enrolled. The programmed death ligand 1 (PD-L1) expression profile was detected by immunohistochemistry. The neutrophil-to-lymphocyte ratio (NLR) was calculated based on parameters of a routine blood examination. Multivariate logistic regression analysis was utilized to construct predictive nomograms for LNM based on data of 64 patients. The nomogram performance was assessed for calibration, discrimination, and clinical use. RESULTS: Tumor grade, lymphovascular invasion, PD-L1, and NLR were independent predictors of LNM. Then, 4 prediction models were constructed. Clinical model included tumor grade and lymphovascular invasion. NLR model was built by adding the NLR to clinical model. PD-L1 model was built by adding the PD-L1 to clinical model. Finally, a combined model was built by adding both PD-L1 and NLR to clinical model. Combined model showed the best performance compared with other models. It showed good discrimination with a C-index of 0.89, and good calibration. In addition, decision curve analysis suggested that model 4 was clinically useful. CONCLUSIONS: We developed a nomogram that incorporated tumor grade, lymphovascular invasion, PD-L1, and NLR that could be conveniently used to predict the preoperative individualized risk of inguinal LNM in patients with penile cancer.
BACKGROUND: Accurate preoperative prediction of inguinal lymph node metastasis (LNM) aids in clinical decision making, especially for patients with penile cancer with clinically negative lymph nodes. We aim to develop a nomogram to predict the preoperative risk of LNM by incorporating clinicopathologic features and tumor biomarkers. METHODS: Eighty-four patients with penile cancer with clinically negative lymph nodes were enrolled. The programmed death ligand 1 (PD-L1) expression profile was detected by immunohistochemistry. The neutrophil-to-lymphocyte ratio (NLR) was calculated based on parameters of a routine blood examination. Multivariate logistic regression analysis was utilized to construct predictive nomograms for LNM based on data of 64 patients. The nomogram performance was assessed for calibration, discrimination, and clinical use. RESULTS:Tumor grade, lymphovascular invasion, PD-L1, and NLR were independent predictors of LNM. Then, 4 prediction models were constructed. Clinical model included tumor grade and lymphovascular invasion. NLR model was built by adding the NLR to clinical model. PD-L1 model was built by adding the PD-L1 to clinical model. Finally, a combined model was built by adding both PD-L1 and NLR to clinical model. Combined model showed the best performance compared with other models. It showed good discrimination with a C-index of 0.89, and good calibration. In addition, decision curve analysis suggested that model 4 was clinically useful. CONCLUSIONS: We developed a nomogram that incorporated tumor grade, lymphovascular invasion, PD-L1, and NLR that could be conveniently used to predict the preoperative individualized risk of inguinal LNM in patients with penile cancer.
Authors: Haviv Muris Saputra; Furqan Hidayatullah; Yudhistira Pradnyan Kloping; Johan Renaldo; Eric Chung; Lukman Hakim Journal: Ann Med Surg (Lond) Date: 2022-08-05