Frontiers in oncology · 2025
A predictive nomogram for assessing the likelihood of retrieving 12 lymph nodes after rectal cancer surgery: a single-center study.
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Ma Jian, Hao Runyang, Jiao Shuai, Chen Qingmin, Yang Baohong, Guan Xu, Li Jiale, Zhao Xinxuan, Huo Yu, Xu Qingxia, Liu Haiyi, Su Wen, Wang Xishan
Аннотация
OBJECTIVE: The retrieval of 12 lymph nodes (LNs) remains a crucial criterion for accurate staging and prognosis evaluation in rectal cancer (RC). However, some patients fail to meet this threshold after surgery. This study developed a nomogram model based on clinical variables to predict the probability of retrieving 12 LNs postoperatively.
METHODS: Patients who underwent radical RC surgery at Shanxi Cancer Hospital between 2015 and 2020 were retrospectively analyzed. Continuous variables were converted into categorical variables. Chi-square tests were used to identify key factors influencing the retrieval of 12 LNs. Significant variables were incorporated into a nomogram model. The model's discrimination ability was evaluated based on the receiver operating characteristic (ROC) curve, while model calibration was assessed using calibration plots. The clinical utility of the model was determined using decision curve analysis (DCA).
RESULTS: A total of 2,724 RC patients were included; 1,906 cases were assigned to the training dataset, while 818 were assigned to the internal validation dataset. Chi-square analysis identified age, T stage, N stage, tumor size, Carcinoembryonic Antigen, CA19-9, hemoglobin, and platelet count as significant factors associated with 12 LN retrieval. The nomogram indicated that T stage, N stage, and tumor size contributed most significantly. The areas under the ROC curves of the model were 0.669 for the training dataset and 0.689 for the internal validation dataset. The calibration plots showed good agreement between the predicted probabilities and actual outcomes. The DCA curves demonstrated a favorable net benefit across a wide range of threshold probabilities.
CONCLUSION: The nomogram model can effectively predict the likelihood of retrieving 12 LNs following RC surgery. The model also provides a valuable tool for preoperative risk stratification and personalized clinical decision-making.
METHODS: Patients who underwent radical RC surgery at Shanxi Cancer Hospital between 2015 and 2020 were retrospectively analyzed. Continuous variables were converted into categorical variables. Chi-square tests were used to identify key factors influencing the retrieval of 12 LNs. Significant variables were incorporated into a nomogram model. The model's discrimination ability was evaluated based on the receiver operating characteristic (ROC) curve, while model calibration was assessed using calibration plots. The clinical utility of the model was determined using decision curve analysis (DCA).
RESULTS: A total of 2,724 RC patients were included; 1,906 cases were assigned to the training dataset, while 818 were assigned to the internal validation dataset. Chi-square analysis identified age, T stage, N stage, tumor size, Carcinoembryonic Antigen, CA19-9, hemoglobin, and platelet count as significant factors associated with 12 LN retrieval. The nomogram indicated that T stage, N stage, and tumor size contributed most significantly. The areas under the ROC curves of the model were 0.669 for the training dataset and 0.689 for the internal validation dataset. The calibration plots showed good agreement between the predicted probabilities and actual outcomes. The DCA curves demonstrated a favorable net benefit across a wide range of threshold probabilities.
CONCLUSION: The nomogram model can effectively predict the likelihood of retrieving 12 LNs following RC surgery. The model also provides a valuable tool for preoperative risk stratification and personalized clinical decision-making.