A prognostic nomogram based on log odds of positive lymph nodes to predict the overall survival in biliary neuroendocrine neoplasms (NENs) patients after surgery

Jiang, Shimin; Yang, Feiya; Zhang, L.; Sang, Xinting; Lu, Xiangfeng; Zheng, Yangyang; Xu, Yiyao

doi:10.1007/s40618-022-01874-8

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The LODDS-based nomogram model for survival prediction of NETs was also applicable to lung and biliary tract sites, and its predictive power was also shown to be superior to the 8th AJCC TNM or SEER classification system by C-index or DCA, net reclassification index, and integrated discrimination improvement (IDI). 24 , 25 In addition, our KM analysis results also proved that it assists clinicians to make individual treatment decisions for improving CNET prognosis.…”

Section: Discussionsupporting

confidence: 54%

A Nomogram Based on the Log Odds of Positive Lymph Nodes Predicts the Prognosis of Patients with Colon Neuroendocrine Tumors After Surgery: A Surveillance, Epidemiology, and End Results Population-Based Study

Xue

Zhang²,

et al. 2023

Technol Cancer Res Treat

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Purpose This work focused on determining the highly efficient nodal classification system from American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) classification (eighth edition), positive lymph node, log odds of positive lymph nodes (LODDS), lymph node ratio, examined lymph node, and establishing the new nomogram for predicting cancer-specific survival in colon neuroendocrine tumors (CNETs). Methods From the Surveillance, Epidemiology, and End Results database, 943 CNET cases undergoing radical operation were enrolled, and randomized as training ( n = 663) or validation set ( n = 280). For the above 5 lymph node classification systems, their prediction performances were compared with C-index, Akaike information criterion (AIC), and area under the receiver operating characteristic curve. Univariate together with multivariate regression was carried out for identifying independent risk factors. Afterward, this work established 1 nomogram and confirmed its accuracy based on C-index, calibration curves, together with the area under the curve value. Besides, it was compared with the AJCC TNM classification system with regard to model prediction performance. Results LODSS achieved the greatest area under the curve and C-index, whereas the smallest AIC. Upon multivariate regression, age, histologic grade, T stage, M stage, and LODDS independently predicted the risk of CNETs. For the validation set, the C-index of the nomogram was 0.794, and the area under the curves at 1, 3, and 5 years was 0.826, 0.857, and 0.870, separately. Additionally, as revealed by the C-index, AIC, decision curve analysis, as well as Kaplan–Meier analysis, our nomogram had superior performance to the AJCC TNM classification system. Conclusions For postoperative patients with CNETs, the LODDS might achieve the best prediction performance. Moreover, the LODDS-based nomograms might show superior survival prediction performance to the AJCC TNM classification system (eighth edition).

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Section: Discussionsupporting

confidence: 54%