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Purpose This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes. Methods Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference. One hundred four MCI subjects with 48 months of follow-up were divided into low and high heterogeneity groups. Kaplan–Meier survival curve analysis was used to observe the importance of heterogeneity results for predicting MCI progression. Results The area under the receiver operating characteristic curve of the model in the training, internal test and external test sets was 0.7503, 0.7512 and 0.7514, respectively. There was a significantly positive correlation between age and radscore of CN subjects (r  = 0.501; P  < .001). The radscore of MCI subjects was significantly higher than that of matched CN subjects (P  < .001). The median radscore ratios of MCI to CN from four age groups (66–70y, 71–75y, 76–80y and 81–85y) were 1.611, 1.760, 1.340 and 1.266, respectively. The probability to progression of low and high heterogeneity groups had a significant difference (P  = .002). Conclusion When radscore is significantly higher than that of normal ageing, it is necessary to alert the possibility of cognitive impairment and deterioration. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a multimodal combinatorial model based on whole‐brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). Methods: This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4‐year follow‐up period. All patients underwent MRI scans, and radiomic features were extracted from the whole‐brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver‐operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. Results: The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum‐related DTI parameters between the low‐risk and high‐risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low‐risk and the high‐risk groups. Conclusions: The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice. [ABSTRACT FROM AUTHOR]

This paper explored the preparation of high activity and low-carbon ecological cementitious materials with coal gangue, and studied the main physical and chemical properties of low-carbon ecological cementitious materials in the silicon aluminum system. The results show that the prepared low-carbon ecological cementitious material has the same performance as P⋅O42.5 and P⋅O32.5 grade cement, and the comprehensive utilization rate of coal gangue based solid waste reaches 48% and 65%, respectively. Compared with ordinary Portland cement, the CO2 emissions from carbonate decomposition per ton of product are reduced by more than 50%, achieving a large proportion application of industrial solid waste of coal gangue and low-carbon manufacturing of cementitious materials. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was 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 4-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 were 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. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH)., Methods: A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set ( n  = 116), an internal validation set ( n  = 30), and an external validation set ( n  = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models., Results: CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)]., Conclusion: pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Hou, Jin, Zhang, Xu, Cui, Qin, Han, Yuan, Zheng, Peng, Shu and Gong.)