Prediction of regional lymph node metastasis in rectal cancer: a novel model based on transrectal contrast-enhanced
Qin Huang1,2, Hua Zhuang3, Yuan Luo4
1Department of Medical Ultrasound, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.
Background:
Regional lymph node metastasis (RLNM) is a crucial prognostic factor for rectal cancer (RC). Accurate preoperative assessment of lymph node status assists clinicians in identifying high-risk patients and formulating treatment plans. Transrectal contrast-enhanced ultrasound (CEUS) can be used to evaluate the degree of microcirculation perfusion in RC, and provide information about tumor heterogeneity. The aim of this study was to develop a nomogram based on CEUS for the accurate assessment of RLNM in patients with RC.
Methods:
CEUS data, routine ultrasound parameters, and clinical and pathological data of patients with RC who underwent surgery between April 2020 and December 2023 were collected. Univariate analysis was performed to identify relevant RLNM factors, which were included in binary logistic regression to determine independent risk factors and generate a nomogram. To evaluate the model, receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were plotted.
Results:
A total of 195 patients were included in this study, with 138 and 57 patients in the training and validation groups, respectively. The independent risk factors for RLNM in RC identified through the analysis were Contrast-enhanced ultrasound inhomogeneity grade (CEUS-IG), endoscopic tumor morphology, and carbohydrate antigen 199 (CA199) level. The nomogram constructed based on these variables demonstrated good discrimination, with area under the curve (AUC) values of 0.832 in the training group and 0.792 in the validation group. The calibration curve indicated good consistency between the predicted values and the actual observations, and DCA demonstrated good clinical utility of the model.
Conclusions:
This study combined CEUS-IG with endoscopic tumor morphology and CA199 levels to develop a predictive model for RLNM in rectal cancer, providing valuable predictive information for preoperative evaluation. This model can enhance diagnostic accuracy.
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