Development and validation of ADC-based nomogram model for predicting the prognostic factors in preoperative clinical early-stage cervical cancer patients.

Abstract

Purpose: To investigate the feasibility of ADC-based nomogram models for predicting cervical cancer (CC) subtype, lymphovascular space invasion (LVSI) and lymph node metastases (LNM) status in preoperative clinical early-stage CC patients.

Materials and methods: A total of 535 CC patients from three independent centers [center A (n = 251) for model training, and centers B (n = 193) and C (n = 91) for external validation] were included. Volumetric ADC histogram metrics (volume, minADC, meanADC, maxADC, skewness, kurtosis, entropy, P10_ADC, P25_ADC, P50_ADC, P75_ADC, and P90_ADC) derived the whole-tumor were calculated. Univariate and multivariate analyses were used to screen the independent predictors and develop nomogram models, with the area under the receiver operating characteristic curve (AUC) for predicting performance estimation.

Results: In differentiating adenosquamous carcinoma (ASC)/adenocarcinoma (AC) from squamous cell carcinoma (SCC), the independent predictors of P25_ADC, SCC antigen (SCC-Ag), and CA199 constructed the nomogram_1 model, with AUCs of 0.900 and 0.873 in training and validation sets, respectively. In differentiating AC from ASC, the independent predictors of P50_ADC and SCC-Ag constructed the nomogram_2 model, with AUCs of 0.837 and 0.829 in training and validation sets, respectively. Tumor volume is the only independent predictor of LVSI(+) and LNM(+), with AUCs of 0.608 and 0.694 in the training set, and 0.553 and 0.656 in the validation set, respectively.

Conclusion: The ADC-based nomogram models can effectively predict the CC subtypes, but might be insufficient in predicting the LVSI and LNM status in preoperative clinical early-stage patients.