BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively.
OBJECTIVE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively.
METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed.
RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891).
CONCLUSIONS: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

Glycogen plays essential roles in glucose metabolism. Imaging glycogen in the liver, the major glycogen reservoir in the body, may shed new light on many metabolic disorders. 13C magnetic resonance spectroscopy (MRS) has become the mainstream method for monitoring glycogen in the body. However, the equipment of special hardware to standard clinical magnetic resonance imaging (MRI) scanners limits its clinical applications. Herein, we utilized endogenous glycogen as a T 2-based relaxation contrast agent for imaging glycogen metabolism in the liver in vivo. The in vitro results demonstrated that the transverse relaxation rate of glycogen strongly correlates with the concentration, pH, and field strength. Based on the Swift-Connick theory, we characterized the exchange property of glycogen and measured the exchange rate of glycogen as 31,847 Hz at 37 °C. Besides, the viscosity and echo spacing showed no apparent effect on the transverse relaxation rate. This unique feature enables visualization of glycogen signaling in vivo through T 2-weighted MRI. Two hours-post intraperitoneal injection of glucagon, a clinical drug to promote glycogenolysis and gluconeogenesis, the signal intensity of the mice\'s liver increased by 1.8 times from the T 2-weighted imaging experiment due to the decomposition of glycogen. This study provides a convenient imaging strategy to non-invasively investigate glycogen metabolism in the liver, which may find clinical applications in metabolic diseases.

This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis. Clinical and magnetic resonance imaging (MRI) data of 302 patients with and without osteoarthritis were retrospectively collected from April 2022 to October 2023 at Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine. The participants were randomly divided into two groups (a training group, n = 211 and a testing group, n = 91). We used logistic regression to analyze clinical characteristics and established a clinical model. Radiomics signatures were developed by extracting radiomic features from the bone marrow edema area using MRI. A nomogram was developed based on the rad-score and clinical characteristics. The diagnostic performance of the three models was compared using the receiver operating characteristic curve and Delong\'s test. The accuracy and clinical application value of the nomogram were evaluated using calibration curve and decision curve analysis. Clinical characteristics such as age, radiographic grading, Western Ontario and McMaster Universities Arthritis Index score, and radiological features were significantly correlated with the diagnosis of osteoarthritis. The Rad score was constructed from 11 radiological features. A clinical model was developed to diagnose osteoarthritis (training group: area under the curve [AUC], 0.819; testing group: AUC, 0.815). Radiomics models were used to effectively diagnose osteoarthritis (training group,: AUC, 0.901; testing group: AUC, 0.841). The nomogram model composed of Rad score and clinical characteristics had better diagnostic performance than a simple clinical model (training group: AUC, 0.906; testing group: AUC, 0.845; p < 0.01). Based on DCA, the nomogram model can provide better diagnostic performance in most cases. In conclusion, the MRI-bone marrow edema-based radiomics-clinical nomogram model showed good performance in diagnosing early osteoarthritis.

UNASSIGNED: Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.
UNASSIGNED: To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.
UNASSIGNED: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model\'s predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.
UNASSIGNED: Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility.
UNASSIGNED: The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.

UNASSIGNED: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.
UNASSIGNED: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).
UNASSIGNED: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.
UNASSIGNED: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

UNASSIGNED: The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC).
UNASSIGNED: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix.
UNASSIGNED: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75-0.88) and 0.73 (0.64-0.80) in the training group and an AUC of 0.74 (0.61-0.85) and 0.62 (0.48-0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78-0.91) and 0.77 (0.64-0.87) in the training and test groups, respectively.
UNASSIGNED: A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes.

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.
磁共振成像（MRI）在缺血性脑卒中的诊断中扮演着重要的角色，准确分割梗死病灶对于介入治疗方法的选择以及评估患者预后效果有着重要的意义。针对现有分割方法对于多尺度脑卒中梗死病灶分割精度较差的问题，本文提出了一种新型的基于深度可分离卷积的编码器—解码器结构网络。首先，该网络将U型网络（U-Net）原有的卷积层模块替换为重新设计的深度可分离卷积模块；其次，引入改进型空洞空间金字塔池化（MASPP），扩大感受野，以加强多尺度特征的提取；再次，在网络的跳跃连接处加入注意力门（AG）模块，进一步增强网络对于多尺度目标的分割精度；最后使用缺血性脑卒中梗死分割2022年挑战赛（ISLES2022）数据集进行实验，本文算法在该数据集上的戴斯相似系数（DSC）、豪斯多夫距离（HD）、敏感度（SEN）、准确度（PRE）分别为0.816 5、3.668 1、0.889 2、0.894 6，优于其他主流分割算法。实验结果表明，本文方法能有效地提高梗死病灶的分割效果，有望为临床诊断和治疗提供可靠辅助。.

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists\' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.
基于电子计算机断层扫描（CT）影像的肺结节自动检测可以有效辅助肺癌诊治，但当前缺乏有效的交互工具将放射科医生的判读结果实时记录并反馈，以优化后台算法模型。本文设计并研发了一个支持CT图像肺结节辅助诊断的在线交互审查系统，通过预置模型检测出肺结节展示给医生，医生利用专业知识对检测的肺结节进行标注，然后根据标注结果采用主动学习策略对内置模型进行迭代优化，以持续提高模型的准确性。本文以开源肺结节数据集——肺结节分析2016（LUNA16）的5～9号子集进行迭代实验，随着迭代次数的增加，模型的准确率、调和分数和交并比指标稳定提升，准确率从0.213 9提高至0.565 6。本文研究结果表明，该系统能在使用深度分割模型辅助医生诊断的同时，最大程度地利用医生的反馈信息来优化模型，迭代提高模型的准确性，从而更好地辅助医生工作。.

Alzheimer\'s Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer\'s disease.
阿尔茨海默症（AD）是一种进行性神经退行性疾病。由于AD患者早期阶段的病症不明显，使得临床诊断中难以快速确诊，误诊率较高。目前关于AD早期诊断的相关研究中，较少关注受试者较长时间跨度上AD的进展变化。基于此，本文提出融合双时间点结构性磁共振成像（sMRI）的AD早期辅助诊断集成模型，尝试将受试者在两个时间点上获取的sMRI变化和临床信息纳入到预测模型的分析中，并使用三维卷积神经网络（3DCNN）和孪生神经网络模块从受试者两个时间点的sMRI中进行特征提取，同时用多层感知机（MLP）来对受试者的临床信息进行建模，尽可能从受试者的多模态数据中提取与AD相关的特征，提高集成模型的诊断性能。实验结果表明，基于本文模型，AD患者组与正常对照（NC）组的分类准确率为89%，转化为AD的轻度认知障碍（MCIc）组与NC组的分类准确率达88%，不转化为AD的轻度认知障碍（MCInc）组与MCIc组的分类准确率为69%，证实了本文所提方法在AD早期诊断中的有效性和高效性，有望在AD早期的临床诊断中起辅助支持的作用。.

BACKGROUND: The soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) is considered a biomarker of microglia activity. The objective of this study was to investigate the trajectory of CSF sTREM2 levels over time and examine its association with sex.
METHODS: A total of 1,017 participants from the Alzheimer\'s Disease Neuroimaging Initiative Study (ADNI) with at least one CSF sTREM2 record were included. The trajectory of CSF sTREM2 was analyzed using a growth curve model. The association between CSF sTREM2 levels and sex was assessed using linear mixed-effect models.
RESULTS: CSF sTREM2 levels were increased with age over time (P < 0.0001). No significant sex difference was observed in sTREM2 levels across the entire sample; however, among the APOE ε4 allele carriers, women exhibited significantly higher sTREM2 levels than men (β = 0.146, P = 0.002).
CONCLUSIONS: Our findings highlight the association between CSF sTREM2 levels and age-related increments, underscoring the potential influence of aging on sTREM2 dynamics. Furthermore, our observations indicate a noteworthy association between sex and CSF sTREM2 levels, particularly in individuals carrying the APOE ε4 allele.