BMC cancer · 2024
Accurate prediction of the lymph node status in ampullary duodenal carcinoma: potential guidance for clinical management.
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Ni Ran, Zhang Tianpeng, Mou Yixuan, Hu Zhiming, Gu Zongting
Аннотация
PURPOSE: This study aimed to identify the risk factors associated with ampullary duodenal carcinoma (a-DC) and develop a clinical model to dynamically and accurately predict the risk of lymph node metastasis (LNM) in a-DC patients.
METHODS: Data from 4077 patients (2004-2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010-2020) from Zhejiang Provincial People's Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram's accuracy and generalizability were externally validated..
RESULTS: After screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P < 0.001), tumor size (P < 0.001), grade (P < 0.001), and tumor extension (P < 0.001). The area under the curve of the nomogram was 74.8% in the training group and 88.9% in the external validation group. The calibration curves demonstrated that the LNM predictions made by the nomogram were in satisfactory agreement with the actual observed LNM. Additionally, the decision curve analysis curves indicated effective clinical utility of the nomogram.
CONCLUSIONS: A nomogram based on the LASSO-logistic analysis was constructed to predict a-DC LNM, demonstrating good performance and clinical application value.
METHODS: Data from 4077 patients (2004-2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010-2020) from Zhejiang Provincial People's Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram's accuracy and generalizability were externally validated..
RESULTS: After screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P < 0.001), tumor size (P < 0.001), grade (P < 0.001), and tumor extension (P < 0.001). The area under the curve of the nomogram was 74.8% in the training group and 88.9% in the external validation group. The calibration curves demonstrated that the LNM predictions made by the nomogram were in satisfactory agreement with the actual observed LNM. Additionally, the decision curve analysis curves indicated effective clinical utility of the nomogram.
CONCLUSIONS: A nomogram based on the LASSO-logistic analysis was constructed to predict a-DC LNM, demonstrating good performance and clinical application value.