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Preoperative prediction and localization of synchronous metastases in colon cancer: the role of CECT combined with clinical characteristics

Yinfei Fan,Ruidan Yang,Yue Shu,Wenjun Xie,Xiaopeng He,Jiong Liu,Jian Shu

Published 2025 in BMC Cancer

ABSTRACT

This study aimed to assess the utility of preoperative contrast-enhanced computed tomography (CECT) and clinical characteristics in predicting the presence and location of synchronous metastases in colon cancer (CC) patients. A retrospective analysis was conducted on pathologically confirmed CC patients who underwent CECT within two weeks prior to radical resection and lymphadenectomy. Synchronous metastases were confirmed via biopsy or characteristic imaging findings. Two independent radiologists reviewed CECT features, including enhancement patterns, tumor characteristics, and lymph node status. Both univariate and multivariate logistic regression analyses were employed to evaluate the distribution of CECT features. The final cohort included 212 patients (95 males and 117 females, with a mean age of 64 years). Distant metastases were categorized as synchronous liver metastasis (65 patients), synchronous peritoneal metastasis (19 patients), and metastases to other sites (22 patients). Elevated carcinoembryonic antigen (CEA) and carbohydrate antigen 19–9 (CA19-9) levels, increased tumor length, higher tumor length-to-thickness ratio, presence of tumor necrosis, lymph node necrosis, and arterial-venous phase CT value differences emerged as independent predictors of synchronous metastases in CC. A predictive model, incorporating these parameters, exhibited strong predictive capability (the area under the curve (AUC) = 0.852, sensitivity: 0.802; specificity: 0.736, accuracy: 0.769). Additionally, tumor necrosis and ascites were significantly associated with synchronous peritoneal metastases, with the model demonstrating high predictive efficacy (AUC = 0.869, sensitivity: 0.895; specificity: 0.782, accuracy:0.802). Integrating clinical characteristics with CECT imaging features provides an effective means of predicting synchronous distant metastases and their locations in CC patients, aiding in the development of personalized treatment plans that may enhance patient survival and quality of life.

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