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Background Diagnostic type II endometrial carcinoma (EC) is considered more aggressive and has a poorer prognosis than type I EC; differentiation between them is helpful for preoperative clinical decision-making. However, the diagnostic value of the apparent diffusion coefficient (ADC) in differentiating them remains unclear. Purpose To investigate the value of ADC in differentiating type II EC from type I EC. Material and Methods Ninety-four patients with EC who underwent diffusion-weighted imaging (DWI) were retrospectively included and divided into type I and type II subgroups, based on the postoperative pathologic results. We analyzed the clinical characteristics, conventional magnetic resonance imaging manifestations, and ADC mean values (ADCmean), ADC minimum values (ADCmin), and ADC max values (ADCmax). Receiver operating characteristic (ROC) curve analysis was further used to assess the predictive performance. Results The ADCmean, ADCmin, and tumor size differed significantly between the two subtypes. The area under the ROC curve (AUC) for ADCmean and ADCmin was 0.787 (95% confidence interval [CI] = 0.692–0.88) and 0.835 (95% CI = 0.751–0.919) for predicting type II EC, respectively. The optimal cut-off value of ADCmean for prediction was 0.757 × 10–3 mm2/s with a sensitivity of 91%, a specificity of 58%, and an accuracy of 74%, while for ADCmin was 0.637 × 10–3 mm2/s with a sensitivity of 82%, a specificity of 73%, and an accuracy of 75%. Conclusion EC with lower ADCmean and ADCmin values derived from DWI, and a larger size, are indicative of type II EC.

Currently, there are no effective approaches for differentiating ovarian fibrothecoma (OF) from broad ligament myoma (BLM). This retrospective study aimed to construct a nomogram prediction model based on MRI to differentiate OF from BLM. The quantitative and qualitative MRI features of 41 OFs and 51 BLMs were compared. Three models were established based on the combination of these features. The ability of the models to differentiate between the two cancers was assessed by ROC analysis. A nomogram based on the best model was constructed for clinical application. The three models showed good performance in differentiating between OF and BLM. The areas under the curve (AUC) of the models based on quantitative and qualitative variables were 0.88 (95% CI: 0.79–0.96) and 0.85 (95% CI: 0.76–0.93), respectively. The combined model designed from the significant variables exhibited the best diagnostic performance with the highest AUC of 0.92 (95% CI: 0.86–0.98). Calibration of the nomogram showed that the predicted probability matched the actual probability well. Analysis of the decision curve demonstrated that the nomogram was clinically useful. Relative T1 value, stone paving sign, enhancement patterns, and ascites were identified as valuable predictors for identifying OF or BLM. The MRI-based nomogram can serve as a preoperative tool to differentiate OF from BLM.