European radiology
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To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with growth hormone (GH)-secreting pituitary macroadenoma, and to compare the qTA with quantitative and qualitative T2-weighted relative signal intensity (rSI) and immunohistochemical evaluation. ⋯ • Machine learning-based texture analysis of T2-weighted MRI can correctly classify response to somatostatin analogues in more than four fifths of the patients. • Machine learning-based texture analysis performs better than qualitative and quantitative evaluation of relative T2 signal intensity and immunohistochemical evaluation. • About one third of the texture features may not be excellently reproducible, indicating that a reliability analysis is necessary before model development.
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This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade. ⋯ • The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC. • The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC. • Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.
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To investigate the structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features (MSA-C), a distinct subtype of MSA, characterized by predominant cerebellar symptoms. ⋯ • Structural connectivity of the motor subnetwork was explored in patients with multiple system atrophy with cerebellar features (MSA-C) using probabilistic tractography. • The motor subnetwork in MSA-C has significant alterations in both basal ganglia and cerebellar connectivity, with a higher extent of abnormality in the cerebellum. • These findings may be causally implicated for the motor features of cerebellar dysfunction and parkinsonism observed in MSA-C.
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The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC). ⋯ • We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance. • The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features. • Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients' survival compared with clinical TNM staging alone.
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To investigate the structural changes of the brain that correlate with physical frailty and cognitive impairments in Parkinson's disease (PD) patients. ⋯ • Physical frailty in PD was associated with decreased GMV in the frontal and occipital cortices, while cognitive impairment was associated with decreased GMV in the frontal, temporal, and occipital cortices. • Physical frailty and cognitive impairment were both associated with decreased GMV in the lateral occipital cortex, which is part of the ventral object-based visual pathway. • Decreased GMV in the lateral occipital cortex may serve as a potential imaging biomarker for physical frailty and cognitive impairment in PD.