A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer’s disease by using a predictive nomogram

Abstract

Predicting the probability of converting from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is still a challenging task. This study aims at providing a personalized MCI-to-AD conversion estimation by using a multipredictor nomogram that integrates neuroimaging features, cerebrospinal fluid (CSF) biomarker, and clinical assessments. To do so, 290 MCI patients were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of whom 76 has converted to AD and 214 remained with MCI. All subjects were randomly divided into a primary and validation cohort. Radiomics signature (Rad-sig) was obtained based on 17 cerebral cortex features selected by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Clinical factors and amyloid-beta peptide (Aβ) concentration were selected by using Spearman correlation between the converted and not-converted patients. Then, a nomogram that combines image features, clinical factor, and Aβ concentration was constructed and validated. Furthermore, we explored the associations between various predictors from the macro- to the microperspective by assessing gene expression patterns. Our results showed that the multipredictor nomogram (C-index 0.978 and 0.956 in both cohorts, respectively) outperformed the nomogram using either Rad-sig or Aβ concentration as individual predictors. Significant associations were found between neuropsychological scores, cerebral cortex features, Aβ levels, and underlying gene pathways. Our study may have a clinical impact as a powerful predictive tool for predicting the conversion probability of MCI and providing associations between cognitive impairment, structural changes, Aβ levels, and underlying biological patterns from the macro- to the microperspective.

Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery

“…Many studies have reported improvements in the predictive accuracy in models combining radiomic features or signatures with clinical risk factors ( 22 , 36 – 38 ). For example, the nomogram models developed by combining radiomic signatures and clinical factors have shown outstanding performance in the prediction of cognitive impairment ( 37 ) and in the differentiation of renal angiomyolipoma ( 38 ). This strategy was adopted in the present study and a radiomics nomogram was developed by incorporating the radiomics signature into the clinical nomogram.…”

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

“…Here, the selected features of the T1WI and T2WI sequences (p < 0.05) were combined as the T1-2WI features. All of the retained T1WI, T2WI and T1-2WI features were processed by dimensionality reduction using the LASSO method to improve the accuracy and degree of modelling fit [20]. Data within 1-standard error of the minimum criterion measure were used in this study.…”

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

“…Some studies revealed that a high-level latent feature learned from deep neural network could be potential tool for the diagnosis of AD and its prodromal stage, MCI 21 – 23 . Most studies using texture analysis or radiomics approaches have focused on the discrimination of diagnostic groups or the prediction of conversion to dementia 24 – 26 .…”