Background
Oesophageal squamous carcinoma(ESCC) is one of the most common cancers worldwide, whose prognosis is closely associated with lymph node metastasis(LNM). This study to investigate the correlation between laboratory indicators and LMN, and to establish a visual prediction model for LMN in ESCC.
Methods
We retrospectively reviewed 183 patients operated on for ESCC. These patients were divided into two groups based on the presence or absence of LMN – The two groups were as follows: group N+(with lymph node metastasis, 60 cases) and group N0(without lymph node metastasis, 123 cases). We performed a logistic regression analysis to determine the risk factors of LNM, draw the receiver operating characteristic curve, calculate the area under the curve (AUC), establish a column line graph visualisation prediction model and perform internal validation, and to perform calibration curve and decision curve.
Results
Multifactorial analysis revealed alcohol, red blood cell distribution width(RDW), and deeper infiltration depth as independent risk factors for LNM. The prediction model included the above three factors with an AUC of = 0.700 (95% confidence interval = 0.619–0.782, P < 0.001). The decision curves were higher than both extreme lines indicating that when the threshold probability was 15–48%, the patients included in the prediction model could benefit from the corresponding intervention.
Conclusion
Alcohol, deeper infiltration depth, and RDW were independent risk factors for LNM of ESCC. A prediction model based on the above three indicators could predict the LNM of ESCC, These indicators are readily available, thereby helping clinicians decision-making.