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Brain iron deposition and cognitive decline in patients with cerebral small vessel disease : a quantitative susceptibility mapping study.
Alzheimers Res Ther
January 2025
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA, Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
Background: Quantitative susceptibility mapping (QSM) can study the susceptibility values of brain tissue which allows for noninvasive examination of local brain iron levels in both normal and pathological conditions.
Purpose: Our study compares brain iron deposition in gray matter (GM) nuclei between cerebral small vessel disease (CSVD) patients and healthy controls (HCs), exploring factors that affect iron deposition and cognitive function.
Materials And Methods: A total of 321 subjects were enrolled in this study.
Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.
BMC Cancer
January 2025
Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.
Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.
BMC Cancer
January 2025
Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, P. R. China.
Background: This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).
Methods: In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method.
ADC histogram analysis of tumor-infiltrating CD8+ T cell levels in meningioma.
Neurosurg Rev
January 2025
Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China.
To investigate the value of preoperative MRI features and ADC histogram analysis for evaluating tumor-infiltrating CD8+ T cells in meningiomas. In this single-center cross-sectional study, we conducted a retrospective analysis of clinical, imaging, and pathological data from 84 patients with meningioma and performed immunohistochemical staining to quantitatively evaluate CD8+ T cells. Using X-Tile software, we divided the patients into high-and low-CD8+ T cells groups based on cut-off values.
View Article and Find Full Text PDFMachine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.
Clin Rheumatol
January 2025
Department of Interventional Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China.
Objectives: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex interaction of clinical variables, and routine extensive US is impractical. Machine learning (ML) models, particularly those integrating the 18-joint ultrasound scoring system (US18), have shown potential to address these issues but remain underexplored.
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