Based on 3D-PDU and clinical characteristics nomogram for prediction of lymph node metastasis and lymph-vascular space invasion of early cervical cancer preoperatively.

PURPOSE: To develop and validate a nomogram based on 3D-PDU parameters and clinical characteristics to predict LNM and LVSI in early-stage cervical cancer preoperatively. MATERIALS AND METHODS: A total of first diagnosis 138 patients with cervical cancer who had undergone 3D-PDU examination before radical hysterectomy plus lymph dissection between 2014 and 2019 were enrolled for this study. Multivariate logistic regression analyses were performed to analyze the 3D-PDU parameters and selected clinicopathologic features and develop a nomogram to predict the probability of LNM and LVSI in the early stage. ROC curve was used to evaluate model differentiation, calibration curve and Hosmer-Lemeshow test were used to evaluate calibration, and DCA was used to evaluate clinical practicability. RESULTS: Menopause status, FIGO stage and VI were independent predictors of LNM. BMI and maximum tumor diameter were independent predictors of LVSI. The predicted AUC of the LNM and LSVI models were 0.845 (95%CI,0.765-0.926) and 0.714 (95%CI,0.615-0.813). Calibration curve and H-L test (LNM groups P = 0.478; LVSI P = 0.783) all showed that the predicted value of the model had a good fit with the actual observed value, and DCA indicated that the model had a good clinical net benefit. CONCLUSION: The proposed nomogram based on 3D-PDU parameters and clinical characteristics has been proposed to predict LNM and LVSI with high accuracy, demonstrating for the first time the potential of non-invasive prediction. The probability derived from this nomogram may have the potential to provide valuable guidance for physicians to develop clinical individualized treatment plans of FIGO patients with early cervical cancer.

Nomogram based on the O-RADS for predicting the malignancy risk of adnexal masses with complex ultrasound morphology.

OBJECTIVE: The accurate preoperative differentiation of benign and malignant adnexal masses, especially those with complex ultrasound morphology, remains a great challenge for junior sonographers. The purpose of this study was to develop and validate a nomogram based on the Ovarian-Adnexal Reporting and Data System (O-RADS) for predicting the malignancy risk of adnexal masses with complex ultrasound morphology. METHODS: A total of 243 patients with data on adnexal masses with complex ultrasound morphology from January 2019 to December 2020 were selected to establish the training cohort, while 106 patients with data from January 2021 to December 2021 served as the validation cohort. Univariate and multivariate analyses were used to determine independent risk factors for malignant tumors in the training cohort. Subsequently, a predictive nomogram model was developed and validated in the validation cohort. The calibration, discrimination, and clinical net benefit of the nomogram model were assessed separately by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, we compared this model to the O-RADS. RESULTS: The O-RADS category, an elevated CA125 level, acoustic shadowing and a papillary projection with color Doppler flow were the independent predictors and were incorporated into the nomogram model. The area under the ROC curve (AUC) of the nomogram model was 0.958 (95% CI, 0.932-0.984) in the training cohort. The specificity and sensitivity were 0.939 and 0.893, respectively. This nomogram also showed good discrimination in the validation cohort (AUC = 0.940, 95% CI, 0.899-0.981), with a sensitivity of 0.915 and specificity of 0.797. In addition, the nomogram model showed good calibration efficiency in both the training and validation cohorts. DCA indicated that the nomogram was clinically useful. Furthermore, the nomogram model had higher AUC and net benefit than the O-RADS. CONCLUSION: The nomogram based on the O-RADS showed a good predictive ability for the malignancy risk of adnexal masses with complex ultrasound morphology and could provide help for junior sonographers.