ArticleBMC cancer2025
Development and external validation of a machine learning-based prognostic model for small cell neuroendocrine cervical carcinoma: a multi-center study.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
backgroundSmall cell neuroendocrine cervical carcinoma (SCNECC) is a rare malignancy with a poor prognosis. The prognostic factors influencing SCNECC remain unclear. This study aimed to develop a prognostic model for SCNECC using machine learning (ML) techniques.
methodsWe collected 487 patients diagnosed with SCNECC in the SEER database from 2004 to 2021, dividing them into a training set and an internal validation set at a 7:3 ratio. Additionally, we gathered 300 SCNECC patients from 3 Chinese registries between 2005 and 2023 as an external validation set. Initially, we performed univariate Cox regression analyses on 22 candidate variables using the Mime package. Variables with a p-value < 0.05 were included. Subsequently, to determine the optimal prognostic model, a total of 10 commonly used ML algorithms were collected and subsequently combined into 117 unique combinations. Finally, we validated the best model's performance using multiple independent cohorts, assessing metrics such as the concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curves (ROC curves), and decision curve analyses (DCA).
resultsThe Stepwise Cox (StepCox) [forward] + Random Survival Forest (RSF) (SCR) model demonstrated the best predictive performance, with a C-index of 0.84 in the development set, 0.75 in the internal validation set, and 0.68 in the external validation set. It showed high prognostic value for 1-, 3-, and 5-year survival in SCNECC patients. SHAP-based interpretability analysis identified twenty key predictors that collectively enhanced the model's robustness.
conclusionThe SCR model has potential in predicting the prognosis of SCNECC, providing clinicians with decision support to identify high-risk patients, optimize treatment strategies, and ultimately improve clinical outcomes.
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