Predicting Risk of Lymph Node Metastasis in Neuroendocrine Carcinoma of Cervix: A Validated Nomogram Incorporating Neuroendocrine Markers and Clinical Parameters

TL;DR

This study creates a model to predict lymph node metastasis risk in cervical neuroendocrine carcinoma using clinical data and biomarkers, helping identify low-risk patients.

Contribution

A novel nomogram model combining neuroendocrine markers and clinical parameters to predict lymph node metastasis in cervical neuroendocrine carcinoma.

Findings

The nomogram model achieved high accuracy with C-indexes of 0.894 and 0.92 in training and validation cohorts.

CD56 positivity and specific clinical factors were identified as independent risk factors for lymph node metastasis.

The model can identify low-risk patients with a risk probability threshold of 0.20.

Abstract

Lymph node metastasis (LNM) is an important factor leading to poor prognosis of tumors. This study aims to predict the risk probability of LNM in neuroendocrine carcinoma of cervix (NECC).

202 and 92 patients were included as the training cohort and the validation cohort respectively. Logistics regression analysis was conducted to determine the risk factors related to LNM in the training cohort. The validity of the model was evaluated by the calibration curve and the consistency index. The receiver operating characteristic curve was used to determine the optimal threshold for predicting the risk of LNM. Then, it compared the predictive ability of the different models and their ability to identify low‐risk patients.

Multivariate logistic regression analysis confirmed that the depth of stromal invasion (p = 0.029), parametrium invasion (p = 0.046), lymphovascular space invasion (p = 0.011), cervical‐uterine junction invasion (p = 0.046), and positive CD56 (p = 0.008) were the independent risk factors for LNM, which were included in the construction of the nomogram model. Both the internal and external calibration curves showed that the model fits well. The C‐index of the training cohort and the validation cohort in this developed model (0.894 and 0.92, respectively) was superior to other models. The optimal threshold of risk probability of LNM predicted by the model was 0.20. Based on this threshold, this model showed a good recognition ability to identify low‐risk patients.

The nomogram model constructed by combining clinical parameters with neuroendocrine markers could effectively predict the risk probability of LNM in NECC and identify the low‐risk population.

This study creates a nomogram model to predict risk probability of lymph node metastasis in NECC based on neuroendocrine biomarkers (CD56) and clinical factors. This model is superior to other existing models through the comparison. And it enables clinicians to identify low‐risk patients (risk probability threshold: 0.20) to guide the individualized range of operation, potentially sparing them unnecessary lymphadenectomy and its associated morbidity.

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