Construction and validation of nomogram model for endoscopic submucosal dissection in patients with early colorectal cancer based on intestinal microflora and clinical pathological parameters.

Objective: This study aimed to investigate the prognostic factors of endoscopic submucosal dissection (ESD) in early colorectal cancer patients and to develop a predictive model for assessing prognostic risk.

Methods: We retrospectively analyzed the data of 126 patients with early colorectal cancer who underwent ESD treatment at our hospital from February 2022 to February 2024. All cases were randomly divided into the training set (88 cases) and the verification set (38 cases) in a ratio of 7:3. According to the prognosis of patients, they were divided into good prognosis group and bad prognosis group. Within the training set, multivariate Logistic regression analysis was used to identify the independent risk factors affecting the prognosis of ESD treatment and a nomogram prediction model was constructed. The validity of the model prediction was assessed by plotting the receiver operating characteristic (ROC) curve and the calibration curve, and the results were verified in the validation set. The clinical application value of Decision Curve Analysis (DCA) was explored.

Results: Among the 126 patients, 33 cases (26.19%) had poor prognosis after ESD treatment. Logistic analysis showed that tumor size, lymph node metastasis, preoperative serum CEA level, Bacteroides abundance and Enterococcus abundance were the independent risk factors for poor prognosis of ESD treatment (p < 0.05). The nomogram model achieved C-index values of 0.898 (training set) and 0.926 (validation set), with mean absolute errors of 0.101 and 0.066, respectively. In the Hosmer-Lemeshow test, the χ values for the training and validation sets were 8.143(p = 0.419) and 10.591(p = 0.226), respectively. The ROC curves show AUC values of 0.897(95% CI 0.795-0.998) and 0.917(95% CI 0.752-1.000) for the training and validation sets, respectively, and a combination of sensitivity and specificity of 0.870 and 0.938, respectively, and 0.895 and 0.857, respectively.

Conclusion: The nomogram prediction model based on the intestinal flora and clinical pathological parameters of patients with early colorectal cancer has high accuracy and calibration degree, which can provide a key reference for formulating individualized treatment plan in clinical and evaluating the prognosis of patients.