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.

Figure 1.

Feature workflow and study flowchart

Figure 2.

Chord diagram shows the correlation between three clinical-morphological features (Tumour size, HPV18, LN tumour ratio) and two histogram features (ADC_minimum and ADC_{inhomogeneity}), but there were no significant correlation within the two groups of features. The values around the circle represent the correlation coefficients. Each link represents a significant correlation according to Pearson’s correlation analysis (p < 0.05). The width of the link and the contact area between an arc and a circle indicate the relative strength between two features.

Figure 3.

Violin plot of the comparison of features (a) ADC_minimum, (b) ADC_{inhomogeneity} and (c) LN-tumour ratio. There were significant differences in these features between SCNECC and non-SCNECC in the combined model (p < 0.05). The width of the violin plot represents the probability density. The box plots represent the Q3, median, Q1 (the top, middle, and bottom lines of the box, respectively), the 95% CI (the vertical line on the two sides of the box) and outliers (black dots).

Figure 4.

(a) The ROC curves of the clinical-morphological model, histogram model and combined model. (b) The calibration curves of the three models in predicting SCNECC. The different coloured lines represent the performance of the three models, in which a closer fit to the ideal line represents a better prediction. P values of the Hosmer-Lemeshow test are shown in the legend, and p > 0.05 indicates that the model has a good fitting degree. (c) Decision curve analysis for the three models. Across the various threshold probabilities, the combined model curve showed great net benefit.

Figure 5.

The developed nomogram for predicting the probability of SCNECC. By summing the points of each feature and locating the sum on the total points scale, the estimated probability of SCNECC can be determined.