AUTHOR=Guo Xingren , Song Xiangyang , Long Xiaoyin , Liu Yahui , Xie Yixin , Xie Cheng , Ji Bai 

TITLE=New nomogram for predicting lymph node positivity in pancreatic head cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1053375

DOI=10.3389/fonc.2023.1053375

ISSN=2234-943X

ABSTRACT=Background: Pancreatic cancer is one of the most malignant cancers worldwide, most of which occurs in the head of the pancreas. The existing LPD surgical technique has gone through the learning curve, a wide variety of approaches have been proposed, and the operation has matured. At present, pancreatic head cancer has been gradually changing from "surgeons' evaluation of anatomical resection" to "biologically inappropriate resection". In this study, common preoperative clinical indicators were adopted to predict the risk of lymph node metastasis in pancreatic head cancer.
Methods: The preoperative clinical data of 191 patients with pancreatic head cancer who received LPD in the First Affiliated Hospital of Jilin University from May 2016 to December 2021 were obtained. A univariate regression analysis study was conducted, and the indicators with a significance level of P<0.05 were included in the univariate logistic regression analysis into multivariate. Lastly, a nomogram was built in accordance with age, tumor size, WBC, ALB, and LMR. The model with the highest resolution was selected by obtaining the area under a curve. The clinical net benefit of the prediction model was examined using decision curve analyses.
Results: Multivariate logistic regression analysis found age, tumor size, WBC, ALB, LMR as five independent factors. Based on the above indicators, a nomogram model was constructed. The model is calibrated by validating the calibration curve within 1000 bootstrap resamples.  The ROC curve had an AUC of 0.745(confidence interval of 95%: 0.673-0.816), thus indicating that the model had excellent discriminative skills. DCA indicated that the predictive model achieved a high net benefit throughout almost the entire threshold probability range.
Conclusions: This study has been the first to investigate a nomogram for preoperative prediction of lymphatic metastasis in pancreatic head cancer. The result indicated that age, ALB, tumor size, WBC, and LMR were independent risk factors for lymph node metastasis in pancreatic head cancer. This study may provide a novel perspective for the selection of appropriate continuous treatment regimens, the increase of the survival rate of patients with pancreatic head cancer, and the selection of appropriate neoadjuvant therapy patients.