Prediction of MCI progression to AD based on DTI-derived diffusion parameters: construction and validation of a nomogram

Objective: To construct and validate a nomogram that combines diffusion tensor imaging (DTI) parameters and clinically relevant features for predicting the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).Method: A retrospective analysis was conducted on the MRI and clinical data of 121 MCI patients, of whom 32 progressed to AD during a four-year follow-up period. The MCI patients were divided into training and validation sets at a ratio of 7:3. DTI features were extracted from MCI patient data in the training set, and their dimensionality was reduced to construct a radiomics signature (RS). Then, combining the RS with independent predictors of MCI disease progression, a joint model was constructed, and a nomogram was generated. Finally, the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the diagnostic and clinical efficacy of the nomogram based on the data from the validation set.Result: The AUCs of the RS in the training and validation sets were 0.81 and 0.84, with sensitivities of 0.87 and 0.78 and specificities of 0.71 and 0.81, respectively. Multiple logistic regression analysis showed that the RS, clinical dementia rating scale score, and Alzheimer's disease assessment scale score were the independent predictors of progression and thus used to construct the nomogram. The AUCs of the nomogram in the training and validation sets were 0.89 and 0.91, respectively, with sensitivities of 0.78 and 0.89 and specificities of 0.90 and 0.88, respectively. DCA showed that the nomogram was the most valuable model for predicting the progression of MCI to AD and that it provided greater net benefits than other analysed models.Conclusion: Changes in white matter fibre bundles can serve as predictive imaging markers for MCI disease progression, and the combination of white matter DTI features and relevant clinical features can be used to construct a nomogram with important predictive value for MCI disease progression.