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To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs., (© 2023. The Author(s).)

Background: The mild cognitive impairment (MCI) stage among elderly individuals is very complex, and the level of diagnostic accuracy is far from ideal. Some studies have tried to improve the 'MCI due to Alzheimer's disease (AD)' classification by further stratifying these patients into subgroups. Depression-related symptoms may play an important role in helping to better define the MCI stage in elderly individuals., Objective: In this work, we explored functional and structural differences in the brains of patients with nondepressed MCI (nDMCI) and patients with MCI with depressive symptoms (DMCI), and we examined how these groups relate to AD atrophy patterns and cognitive functioning., Methods: Sixty-five participants underwent MRI exams and were divided into four groups: cognitively normal, nDMCI, DMCI, and AD. We compared the regional brain volumes, cortical thickness, and white matter microstructure measures using diffusion tensor imaging among groups. Additionally, we evaluated changes in functional connectivity using fMRI data., Results: In comparison to the nDMCI group, the DMCI patients had more pronounced atrophy in the hippocampus and amygdala. Additionally, DMCI patients had asymmetric damage in the limbic-frontal white matter connection. Furthermore, two medial posterior regions, the isthmus of cingulate gyrus and especially the lingual gyrus, had high importance in the structural and functional differentiation between the two groups., Conclusion: It is possible to differentiate nDMCI from DMCI patients using MRI techniques, which may contribute to a better characterization of subtypes of the MCI stage., Competing Interests: The authors declare no conflict of interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of West China School of Medicine/West China Hospital (WCSM/WCH) of Sichuan University.)

Background: Short-term memory binding (STMB) tests assess conjunctive binding, in which participants should remember the integration of features, such as shapes (or objects) and colors, forming a unique representation in memory. In this study, we investigated two STMB paradigms: change detection (CD) and free recall (FR)., Objective: To investigate the cognitive profile in the CD and FR tasks of three diagnostic groups: cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's clinical syndrome (ACS). In addition, we aimed to calculate and compare the accuracy of the CD and FR tasks to identify MCI and ACS., Methods: Participants were 24 CU, 24 MCI, and 37 ACS. The cognitive scores of the clinical groups were compared using analysis of variance (ANOVA) and receiver-operating characteristic (ROC) analyses were carried out to verify the accuracy of the STMB tasks., Results: In the CD task, CU was different from MCI and ACS (CU > MCI = ACS), while in the FR task all groups were different (CU > MCI > ACS). The ROC analyses showed an area under the curve (AUC) of 0.855 comparing CU with MCI for the CD task and 0.975 for the FR. The AUC comparing CU and ACS was 0.924 for the CD and 0.973 for the FR task. The FR task showed better accuracy to identify MCI patients, and the same accuracy to detect ACS., Conclusion: The present findings indicate that impairments in CD and FR of bound representations are features of the cognitive profiles of MCI and ACS patients., (© 2020 John Wiley & Sons Ltd.)