New nomogram for predicting lymph node positivity in pancreatic head cancer

Abstract

Background: Pancreatic cancer is one of the most malignant cancers worldwide, and it mostly occurs in the head of the pancreas. Existing laparoscopic pancreaticoduodenectomy (LPD) surgical techniques have has undergone a learning curve, a wide variety of approaches for the treatment of pancreatic cancer 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, the risk of lymph node metastasis in pancreatic head cancer was predicted using common preoperative clinical indicators.

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 based on age, tumor size, leucocyte,albumin(ALB), and lymphocytes/monocytes(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.Risk stratification was performed by combining preoperative CT scan with existing models.

Results: Multivariate logistic regression analysis found age, tumor size, WBC, ALB, and LMR as five independent factors. A nomogram model was constructed based on the above indicators. The model was calibrated by validating the calibration curve within 1000 bootstrap resamples. The ROC curve achieved an AUC of 0.745(confidence interval of 95%: 0.673-0.816), thus indicating that the model had excellent discriminative skills. DCA suggested that the predictive model achieved a high net benefit in the nearly 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 suggests that age, ALB, tumor size, WBC, and LMR are 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.

Keywords: LNM; age; clinical indicators; nomogram; pancreatic head cancer.

Figure 1

lasso analysis.

Figure 2

Prediction of LNM in patients with pancreatic head cncer using a nomogram. To get the position of each factor on the corresponding axis, draw lines on the point axis to represent the number of points. Add all the scores and find the place of the total score to determine the probability of LNM for that line in the nomogram.

Figure 3

Model’s forest plot.

Figure 4

The ROC curve of the model.

Figure 5

Calibration curve for predicting LNM. The nomogram predicted LNM is plotted on the x-axis, and the actual incidence of LNM is plotted on the y-axis. A plot along the 45° line will indicate a perfectly calibrated model where the predicted LNM is the same as the actual LNM. The expected probability distribution for the occurrence of LNM is shown at the top of the figure.

Figure 6

A decision curve analysis (DCA) was performed on the nomogram of the model. The solid black line assumes that all patients are LNM positive or negative, respectively. The dashed lines represent the net payoff of the nomogram at different threshold probabilities.