Development of an improved diagnostic nomogram for preoperative prediction of small cell neuroendocrine cancer of the cervix

Abstract

Objectives: Accurate preoperative diagnosis of small cell neuroendocrine cancer of the cervix (SCNECC) is crucial for establishing the best treatment plan. This study aimed to develop an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information. Methods: A total of 105 pathologically confirmed cervical cancer patients (35 SCNECC, 70 non-SCNECC) from multiple centres with complete clinical and MR records were included. Whole lesion histogram analysis of the ADC was performed. Multivariate logistic regression analysis was used to develop diagnostic models based on clinical, morphological, and histogram data. The predictive performance in terms of discrimination, calibration, and clinical usefulness of the different models was assessed. A nomogram for preoperatively discriminating SCNECC was developed from the combined model. Results: In preoperative SCNECC diagnosis, the combined model, which had a diagnostic AUC (area under the curve) of 0.937 (95% CI: 0.887–0.987), outperformed the clinical-morphological model, which had an AUC of 0.869 (CI: 0.788–0.949), and the histogram model, which had an AUC of 0.872 (CI: 0.792–0.951). The calibration curve and decision curve analyses suggest that the combined model achieved good fitting and clinical utility. Conclusions: Non-invasive preoperative diagnosis of SCNECC can be achieved with high accuracy by integrating clinical, MR morphological, and ADC histogram features. The nomogram derived from the combined model can provide an easy-to-use clinical preoperative diagnostic tool for SCNECC. Advances in knowledge: It is clear that the therapeutic strategies for SCNECC are different from those for other pathological types of cervical cancer according to V 1.2021 of the NCCN clinical practice guidelines in oncology for cervical cancer. This research developed an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.