A Clinical-DKI Fusion Model for Objective Pretreatment Prediction of Parametrial Invasion in Cervical Cancer

Current assessment of parametrial invasion (PMI) in cervical cancer relies on subjective gynecological palpation and conventional MRI, often leading to diagnostic inconsistencies and treatment inaccuracies. This study aimed to develop an objective model integrating Diffusion Kurtosis Imaging (DKI) parameters with clinical variables to improve PMI assessment.

This prospective study enrolled 90 patients with cervical cancer. All participants underwent 3.0T MRI, including DKI. Clinical variables and DKI parameters (mean diffusivity-MD, mean kurtosis-MK, axial kurtosis-Ka, radial kurtosis-Kr) were analyzed. Predictors of PMI were identified using multivariable logistic regression with Akaike Information Criterion (AIC) backward selection. Three models were constructed and compared: a Clinical model, a Clinical+ADC model, and a Clinical+DKI fusion model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation, and interpretability was assessed using SHapley Additive exPlanations (SHAP).

Multivariable analysis identified tumor size, MK, Kr and Ka as independent predictors of PMI (all P<0.05). The Clinical-DKI fusion model demonstrated significantly superior predictive performance, achieving an AUC of 0.87 (95% CI: 0.80-0.94), which was significantly higher than the Clinical-only model (AUC=0.77, P=0.017) and the Clinical+ADC model (AUC=0.77, P<0.013). The model's robustness was confirmed by 5-fold cross-validation (AUC=0.85). SHAP analysis confirmed MK as the most influential predictor CONCLUSION: The integration of DKI parameters, particularly MK, with clinical factors provides a non-invasive and objective tool for pretreatment PMI prediction, significantly improving upon conventional assessments.