Pub Date : 2025-06-01Epub Date: 2025-03-11DOI: 10.1016/j.metrad.2025.100136
Zhizhong Sun , Zidong Cao , Limin Ge , Yifan Li , Haoming Huang , Mingrui Li , Shijun Qiu
Type 2 Diabetes Mellitus (T2DM) is a common metabolic disorder that adversely affects cognitive function and heightens the risk of neurodegenerative diseases. This review examines cutting-edge developments in utilizing machine learning techniques to assess brain function changes in T2DM patients, with a focus on cognitive impairment (CI). Through a comprehensive search across major medical databases, we identified and evaluated six studies that used resting-state functional MRI (rs-fMRI) and machine learning classifiers to analyze brain connectivity patterns in T2DM patients. Our analysis indicates that machine learning methods can effectively distinguish between T2DM patients with and without CI, revealing abnormal functional connectivity patterns linked to cognitive decline. These findings suggest that machine learning combined with neuroimaging holds promising initial findings for guiding early interventions and treatment strategies, with the goal of mitigating CI in T2DM patients and improving clinical outcomes.
{"title":"Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review","authors":"Zhizhong Sun , Zidong Cao , Limin Ge , Yifan Li , Haoming Huang , Mingrui Li , Shijun Qiu","doi":"10.1016/j.metrad.2025.100136","DOIUrl":"10.1016/j.metrad.2025.100136","url":null,"abstract":"<div><div>Type 2 Diabetes Mellitus (T2DM) is a common metabolic disorder that adversely affects cognitive function and heightens the risk of neurodegenerative diseases. This review examines cutting-edge developments in utilizing machine learning techniques to assess brain function changes in T2DM patients, with a focus on cognitive impairment (CI). Through a comprehensive search across major medical databases, we identified and evaluated six studies that used resting-state functional MRI (rs-fMRI) and machine learning classifiers to analyze brain connectivity patterns in T2DM patients. Our analysis indicates that machine learning methods can effectively distinguish between T2DM patients with and without CI, revealing abnormal functional connectivity patterns linked to cognitive decline. These findings suggest that machine learning combined with neuroimaging holds promising initial findings for guiding early interventions and treatment strategies, with the goal of mitigating CI in T2DM patients and improving clinical outcomes.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-29DOI: 10.1016/j.metrad.2025.100150
Zhuoqi Ma , Lulu Bi , Paige Collins , Owen Leary , Maliha Imami , Zhusi Zhong , Shaolei Lu , Grayson Baird , Nikos Tapinos , Ugur Cetintemel , Harrison Bai , Jerrold Boxerman , Zhicheng Jiao
Purpose
In this study, we use large language models (LLMs) to integrate information from multi-source medical reports to enhance the accuracy of automated diagnostic classification and prognosis for brain tumors.
Materials and methods
Brain MRI reports from a cohort of 426 brain tumor patients were manually labeled for tumor presence and stability. Pathology reports from the same cohort were incorporated as an additional information source. A pre-trained LLM was used to extract features from the multi-source reports, and a Multi-layer perceptron (MLP) was trained for classification tasks. Model performance was evaluated on the test set using Micro F1 scores and AUROCs. The model’s zero-shot prognostic capability was validated on an independent cohort of 33 glioblastoma patients.
Results
Micro F1-score 0.849 (95%CI: 0.814, 0.880) for tumor presence classification and 0.929 (95%CI: 0.904, 0.954) for tumor stability classification are reached. Compared to using solely radiology reports, the developed model showed improvements on Micro F1 of 10.4 % for tumor presence and 5.6 % for stability classification. Log-rank tests confirmed significant distinction between the high- and low-risk patient groups stratified by model-predicted “Tumor Stability” label (p-value = 0.017), confirming the prognostic value of the model-generated labels.
Conclusion
This study developed a multi-source integration model based on LLMs for automated diagnostic classification and zero-shot prognosis of brain tumors. The integration of multi-source reports improved classification accuracy compared to single-source reports. Predicted tumor stability labels demonstrated survival prognostic capabilities. These findings confirm the potential of LLMs in brain tumor research, supporting precision diagnostics and prognosis.
{"title":"Large language model-based multi-source integration pipeline for automated diagnostic classification and zero-shot prognoses for brain tumor","authors":"Zhuoqi Ma , Lulu Bi , Paige Collins , Owen Leary , Maliha Imami , Zhusi Zhong , Shaolei Lu , Grayson Baird , Nikos Tapinos , Ugur Cetintemel , Harrison Bai , Jerrold Boxerman , Zhicheng Jiao","doi":"10.1016/j.metrad.2025.100150","DOIUrl":"10.1016/j.metrad.2025.100150","url":null,"abstract":"<div><h3>Purpose</h3><div>In this study, we use large language models (LLMs) to integrate information from multi-source medical reports to enhance the accuracy of automated diagnostic classification and prognosis for brain tumors.</div></div><div><h3>Materials and methods</h3><div>Brain MRI reports from a cohort of 426 brain tumor patients were manually labeled for tumor presence and stability. Pathology reports from the same cohort were incorporated as an additional information source. A pre-trained LLM was used to extract features from the multi-source reports, and a Multi-layer perceptron (MLP) was trained for classification tasks. Model performance was evaluated on the test set using Micro F1 scores and AUROCs. The model’s zero-shot prognostic capability was validated on an independent cohort of 33 glioblastoma patients.</div></div><div><h3>Results</h3><div>Micro F1-score 0.849 (95%CI: 0.814, 0.880) for tumor presence classification and 0.929 (95%CI: 0.904, 0.954) for tumor stability classification are reached. Compared to using solely radiology reports, the developed model showed improvements on Micro F1 of 10.4 % for tumor presence and 5.6 % for stability classification. Log-rank tests confirmed significant distinction between the high- and low-risk patient groups stratified by model-predicted “Tumor Stability” label (<em>p</em>-value = 0.017), confirming the prognostic value of the model-generated labels.</div></div><div><h3>Conclusion</h3><div>This study developed a multi-source integration model based on LLMs for automated diagnostic classification and zero-shot prognosis of brain tumors. The integration of multi-source reports improved classification accuracy compared to single-source reports. Predicted tumor stability labels demonstrated survival prognostic capabilities. These findings confirm the potential of LLMs in brain tumor research, supporting precision diagnostics and prognosis.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-18DOI: 10.1016/j.metrad.2025.100138
Ruixin Wang , Zhiyuan Wang , Yuanming Xiao , Xiaohui Liu , Guoping Tan , Jun Liu
Breast cancer is a major disease threatening the health of women worldwide. The advent of automated breast ultrasound (ABUS) has provided new possibilities for the early screening and diagnosis of breast cancer. Concurrently, artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems, driven by deep learning (DL), have advanced significantly over the past decade. Unlike traditional handheld ultrasound (HHUS), ABUS enables the separation of scanning and diagnosis, increasing the demand for CAD systems that hold significant clinical value. In recent years, DL has become a dominant force in AI development, playing a crucial role in CAD for across various medical imaging modalities. However, despite its prominence in AI-driven medical image analysis, a comprehensive review of its applications in ABUS is still lacking. This paper provides a detailed analysis of the latest advancements, existing challenges, and future research opportunities in this rapidly evolving field.
{"title":"Application of deep learning on automated breast ultrasound: Current developments, challenges, and opportunities","authors":"Ruixin Wang , Zhiyuan Wang , Yuanming Xiao , Xiaohui Liu , Guoping Tan , Jun Liu","doi":"10.1016/j.metrad.2025.100138","DOIUrl":"10.1016/j.metrad.2025.100138","url":null,"abstract":"<div><div>Breast cancer is a major disease threatening the health of women worldwide. The advent of automated breast ultrasound (ABUS) has provided new possibilities for the early screening and diagnosis of breast cancer. Concurrently, artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems, driven by deep learning (DL), have advanced significantly over the past decade. Unlike traditional handheld ultrasound (HHUS), ABUS enables the separation of scanning and diagnosis, increasing the demand for CAD systems that hold significant clinical value. In recent years, DL has become a dominant force in AI development, playing a crucial role in CAD for across various medical imaging modalities. However, despite its prominence in AI-driven medical image analysis, a comprehensive review of its applications in ABUS is still lacking. This paper provides a detailed analysis of the latest advancements, existing challenges, and future research opportunities in this rapidly evolving field.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-30DOI: 10.1016/j.metrad.2025.100152
Erin Beate Bjørkeli , Morteza Esmaeili
Objectives
To develop a deep learning model for simultaneous segmentation of glioma lesions and classification of IDH mutation and 1p/19q codeletion status using multimodal MRI.
Methods
We employed a CNN model with Encoder-Decoder architecture for segmentation, followed by fully connected layers for classification. The model was trained and validated using the BraTS 2020 dataset (132 examinations with known molecular status, split 80/20). Four MRI sequences iamges (T1, T1ce, T2, FLAIR) were used for analysis. Segmentation performance was evaluated using mean Dice Score (mDS) and mean Intersection over Union (mIoU). Classification was assessed using accuracy, sensitivity, and specificity.
Results
The model achieved the best segmentation performance with all four modalities (mDS validation = 0.73, mIoU validation = 0.62). Among single modalities, FLAIR performed best (mDS validation = 0.56, mIoU validation = 0.44). For classification, the combined four modalities achieved an overall accuracy of 0.98. However, classification precision for IDH and 1p19q was potentially limited by class imbalance.
Conclusion
Our CNN-based Encoder-Decoder model demonstrates the benefit of multimodal MRI for accurate glioma segmentation and shows promising results for molecular subtype classification. Future work will focus on addressing class imbalance and exploring feature integration to enhance classification performance.
目的建立一种基于多模态MRI的神经胶质瘤病变同时分割、IDH突变和1p/19q编码状态分类的深度学习模型。方法采用具有编码器-解码器架构的CNN模型进行分割，然后采用全连接层进行分类。该模型使用BraTS 2020数据集（132个已知分子状态的检查，分割80/20）进行训练和验证。4张MRI序列图像（T1, T1ce, T2， FLAIR）进行分析。使用平均Dice Score （mDS）和平均Intersection over Union （mIoU）来评估分割性能。分类采用准确性、敏感性和特异性进行评估。结果该模型在4种模式下均获得了最佳分割效果（mDS验证= 0.73，mIoU验证= 0.62）。在单一模式中，FLAIR表现最好（mDS验证= 0.56，mIoU验证= 0.44）。对于分类，组合四种模式的总体准确率为0.98。然而，IDH和1p19q的分类精度可能受到类别不平衡的限制。结论基于cnn的编码器-解码器模型证明了多模态MRI对胶质瘤精确分割的好处，并在分子亚型分类方面显示出令人鼓舞的结果。未来的工作将集中在解决类别不平衡和探索特征集成以提高分类性能上。
{"title":"Multi-task glioma segmentation and IDH mutation and 1p19q codeletion classification via a deep learning model on multimodal MRI","authors":"Erin Beate Bjørkeli , Morteza Esmaeili","doi":"10.1016/j.metrad.2025.100152","DOIUrl":"10.1016/j.metrad.2025.100152","url":null,"abstract":"<div><h3>Objectives</h3><div>To develop a deep learning model for simultaneous segmentation of glioma lesions and classification of IDH mutation and 1p/19q codeletion status using multimodal MRI.</div></div><div><h3>Methods</h3><div>We employed a CNN model with Encoder-Decoder architecture for segmentation, followed by fully connected layers for classification. The model was trained and validated using the BraTS 2020 dataset (132 examinations with known molecular status, split 80/20). Four MRI sequences iamges (T1, T1ce, T2, FLAIR) were used for analysis. Segmentation performance was evaluated using mean Dice Score (mDS) and mean Intersection over Union (mIoU). Classification was assessed using accuracy, sensitivity, and specificity.</div></div><div><h3>Results</h3><div>The model achieved the best segmentation performance with all four modalities (mDS validation = 0.73, mIoU validation = 0.62). Among single modalities, FLAIR performed best (mDS validation = 0.56, mIoU validation = 0.44). For classification, the combined four modalities achieved an overall accuracy of 0.98. However, classification precision for IDH and 1p19q was potentially limited by class imbalance.</div></div><div><h3>Conclusion</h3><div>Our CNN-based Encoder-Decoder model demonstrates the benefit of multimodal MRI for accurate glioma segmentation and shows promising results for molecular subtype classification. Future work will focus on addressing class imbalance and exploring feature integration to enhance classification performance.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-19DOI: 10.1016/j.metrad.2025.100155
Ziyu Liu, Suhang Shang
{"title":"Commentary on “Ferroptosis, M6A and immune checkpoint-related gene expression in the middle temporal gyrus of the Alzheimer's disease brain”","authors":"Ziyu Liu, Suhang Shang","doi":"10.1016/j.metrad.2025.100155","DOIUrl":"10.1016/j.metrad.2025.100155","url":null,"abstract":"","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-05DOI: 10.1016/j.metrad.2025.100147
Shan-Shan Li , Gao-Xiong Duan , De-Mao Deng
Premenstrual Syndrome (PMS) is a unique emotional disorder in women, characterized by a series of cyclical physical, emotional, behavioral, and cognitive symptoms that occur during the luteal phase of the menstrual cycle, often accompanied by significant functional impairment. Premenstrual Dysphoric Disorder (PMDD) is a severe form of PMS and is classified as a subtype of depressive disorders in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Neuroimaging studies have revealed structural and functional abnormalities in the limbic system of PMS/PMDD patients, particularly in areas such as the amygdala, hypothalamus, and hippocampus, which are closely related to clinical symptoms. These abnormalities may represent one of the central nervous mechanisms underlying PMS/PMDD. This review focuses on the structural and functional changes in the limbic system of PMS/PMDD patients as revealed by MRI, and summarizes the relevant research progress.
{"title":"MRI advances on structural and functional changes in limbic system with premenstrual syndrome","authors":"Shan-Shan Li , Gao-Xiong Duan , De-Mao Deng","doi":"10.1016/j.metrad.2025.100147","DOIUrl":"10.1016/j.metrad.2025.100147","url":null,"abstract":"<div><div>Premenstrual Syndrome (PMS) is a unique emotional disorder in women, characterized by a series of cyclical physical, emotional, behavioral, and cognitive symptoms that occur during the luteal phase of the menstrual cycle, often accompanied by significant functional impairment. Premenstrual Dysphoric Disorder (PMDD) is a severe form of PMS and is classified as a subtype of depressive disorders in the fifth edition of the <em>Diagnostic and Statistical Manual of Mental Disorders</em> (<em>DSM-5)</em>. Neuroimaging studies have revealed structural and functional abnormalities in the limbic system of PMS/PMDD patients, particularly in areas such as the amygdala, hypothalamus, and hippocampus, which are closely related to clinical symptoms. These abnormalities may represent one of the central nervous mechanisms underlying PMS/PMDD. This review focuses on the structural and functional changes in the limbic system of PMS/PMDD patients as revealed by MRI, and summarizes the relevant research progress.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144269987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2025-02-02DOI: 10.1016/j.metrad.2025.100135
Jin Liu, Haoting Wang, Lingjiang Li
For mental disorders, the identification of biomarkers with high specificity, sensitivity, and validity remains a major challenge due to their heterogeneity and symptomatic overlap across disorders. In this review, we provide an overview of current research on mental disorders and highlight two key strategies potentially capable of addressing these challenges: data integration and methodological innovation. Effective biomarker identification requires integrating large-scale, multicenter, and multidimensional data integration, including psychological, biological, physiological, and behavioral data. Innovative data acquisition technologies and analytical methods, alongside novel approaches such as leveraging treatment response to validate biomarkers, are equally pivotal for advancing the field. We anticipate that the progress in this domain will be bolstered by the integration of new methodologies and technologies.
{"title":"Rethinking the studies of diagnostic biomarkers for mental disorders","authors":"Jin Liu, Haoting Wang, Lingjiang Li","doi":"10.1016/j.metrad.2025.100135","DOIUrl":"10.1016/j.metrad.2025.100135","url":null,"abstract":"<div><div>For mental disorders, the identification of biomarkers with high specificity, sensitivity, and validity remains a major challenge due to their heterogeneity and symptomatic overlap across disorders. In this review, we provide an overview of current research on mental disorders and highlight two key strategies potentially capable of addressing these challenges: data integration and methodological innovation. Effective biomarker identification requires integrating large-scale, multicenter, and multidimensional data integration, including psychological, biological, physiological, and behavioral data. Innovative data acquisition technologies and analytical methods, alongside novel approaches such as leveraging treatment response to validate biomarkers, are equally pivotal for advancing the field. We anticipate that the progress in this domain will be bolstered by the integration of new methodologies and technologies.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 1","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-28DOI: 10.1016/j.metrad.2024.100123
Meizhi Li , Shangjie Wu , Xiao Liang , Chuanqi Gao , Muhua Hu , Zhu Chen , Pei He , Tingting Jia , Li Xiong
Four-dimensional (4D) flow Magnetic Resonance Imaging (MRI) technology has emerged as a valuable tool in angiography, offering unique insights into the hemodynamics and flow patterns. This research aims to explore the role of 4D flow MRI in advancing our understanding of Deep Vein Thrombosis (DVT), covering its applications in diagnosing, characterizing and mechanism of DVT, as well as its potential for guiding treatment strategies. The qualitative and quantitative information provided by 4D flow MRI enables a comprehensive assessment of blood flow in different vascular regions, shedding light on the relationship between hemodynamic changes and the onset and progression of vascular diseases. Nevertheless, most quantitative research findings for 4D hemodynamic indicators are lacking, and their use is mainly limited to examining arterial conditions. More exploration will be necessary to determine their applicability in studying venous vessels.
{"title":"The role of 4D flow MRI in deep vein thrombosis research","authors":"Meizhi Li , Shangjie Wu , Xiao Liang , Chuanqi Gao , Muhua Hu , Zhu Chen , Pei He , Tingting Jia , Li Xiong","doi":"10.1016/j.metrad.2024.100123","DOIUrl":"10.1016/j.metrad.2024.100123","url":null,"abstract":"<div><div>Four-dimensional (4D) flow Magnetic Resonance Imaging (MRI) technology has emerged as a valuable tool in angiography, offering unique insights into the hemodynamics and flow patterns. This research aims to explore the role of 4D flow MRI in advancing our understanding of Deep Vein Thrombosis (DVT), covering its applications in diagnosing, characterizing and mechanism of DVT, as well as its potential for guiding treatment strategies. The qualitative and quantitative information provided by 4D flow MRI enables a comprehensive assessment of blood flow in different vascular regions, shedding light on the relationship between hemodynamic changes and the onset and progression of vascular diseases. Nevertheless, most quantitative research findings for 4D hemodynamic indicators are lacking, and their use is mainly limited to examining arterial conditions. More exploration will be necessary to determine their applicability in studying venous vessels.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 1","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2025-03-18DOI: 10.1016/j.metrad.2025.100137
Meizhi Yi , Zeng Yule , Weijia Song , Tianyao Wang , Luokai Zhang , Can Hu , Yifeng Peng , Zhaoxiang Zhang , Liangwu Chen , Yan Wang , Huiting Wu , Zhaojie Peng , Xinhua Xiao , Jun Liu , Hong Zhou
Background and aims
Obesity in young adults has become a public health issue that cannot be ignored. Previous studies have shown that obesity, emotional stress and food addiction can interact with each other. However, the underlying pathophysiological and neurobehavioral mechanisms of them are still unclear. We aimed to assess the concordance between the microstructural alterations of white matter (WM) and the functional alterations in the glymphatic system in the context of obesity, and to investigate the impact of body mass index (BMI), emotional stress on the integrity of WM and the functionality of the brain's lymphatic system among the participants.
Methods
We applied neurite orientation dispersion and density imaging (NODDI) to monitor the modifications in the architecture of WM structure, and utilized diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) to evaluate the alterations in the functionality of the brain's glymphatic system. Imaging data were collected from 18 young individuals with obesity and food addiction (OFD), 28 young individuals with obesity but no food addiction and 32 young healthy controls (HC). We also explored the relationships among the WM structural alterations, cerebral lymphatic functional changes, BMI, emotional status, sleep quality and cognitive decline in the participants.
Results
Compared with HC, the changes in NODDI metrics mainly focused on increased ODIp, ODIs and ODItot in ONFD (P < 0.05). Compared with HC, the alterations in NODDI metrics mainly reflected in decreased Vic and Viso in OFD (P < 0.05). In addition, our results showed decreased Vic and Viso in OFD compared with ONFD (P < 0.05). We also found that the ODIp, ODIs and ODItot were significantly positively correlated with the BMI in the whole participants (P < 0.05). The partial correlation analysis disclosed a significant negative association between Vic and HAMD (P < 0.05), and between the Viso and HAMD for all obese patients (P < 0.05). Finally, our study found no difference among HC, OFD and ONFD in the DTI-ALPS index (P ≥ 0.05).
Conclusions
Widespread WM microstructural abnormalities were detected by NODDI in young obese patients, which might precede changes in brain glymphatic system function. Our study offers valuable insights into the degenerative trends observed in young individuals suffering from obesity and enhances our comprehension of the underlying biological mechanisms of WM microstructure alterations in depressed state in young individuals with obesity and food addiction.
{"title":"Microstructure changes of the brain preceded glymphatic function changes in young obesity with and without food addiction","authors":"Meizhi Yi , Zeng Yule , Weijia Song , Tianyao Wang , Luokai Zhang , Can Hu , Yifeng Peng , Zhaoxiang Zhang , Liangwu Chen , Yan Wang , Huiting Wu , Zhaojie Peng , Xinhua Xiao , Jun Liu , Hong Zhou","doi":"10.1016/j.metrad.2025.100137","DOIUrl":"10.1016/j.metrad.2025.100137","url":null,"abstract":"<div><h3>Background and aims</h3><div>Obesity in young adults has become a public health issue that cannot be ignored. Previous studies have shown that obesity, emotional stress and food addiction can interact with each other. However, the underlying pathophysiological and neurobehavioral mechanisms of them are still unclear. We aimed to assess the concordance between the microstructural alterations of white matter (WM) and the functional alterations in the glymphatic system in the context of obesity, and to investigate the impact of body mass index (BMI), emotional stress on the integrity of WM and the functionality of the brain's lymphatic system among the participants.</div></div><div><h3>Methods</h3><div>We applied neurite orientation dispersion and density imaging (NODDI) to monitor the modifications in the architecture of WM structure, and utilized diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) to evaluate the alterations in the functionality of the brain's glymphatic system. Imaging data were collected from 18 young individuals with obesity and food addiction (OFD), 28 young individuals with obesity but no food addiction and 32 young healthy controls (HC). We also explored the relationships among the WM structural alterations, cerebral lymphatic functional changes, BMI, emotional status, sleep quality and cognitive decline in the participants.</div></div><div><h3>Results</h3><div>Compared with HC, the changes in NODDI metrics mainly focused on increased ODIp, ODIs and ODItot in ONFD (<em>P</em> < 0.05). Compared with HC, the alterations in NODDI metrics mainly reflected in decreased Vic and Viso in OFD (<em>P</em> < 0.05). In addition, our results showed decreased Vic and Viso in OFD compared with ONFD (<em>P</em> < 0.05). We also found that the ODIp, ODIs and ODItot were significantly positively correlated with the BMI in the whole participants (<em>P</em> < 0.05). The partial correlation analysis disclosed a significant negative association between Vic and HAMD (<em>P</em> < 0.05), and between the Viso and HAMD for all obese patients (<em>P</em> < 0.05). Finally, our study found no difference among HC, OFD and ONFD in the DTI-ALPS index (<em>P</em> ≥ 0.05).</div></div><div><h3>Conclusions</h3><div>Widespread WM microstructural abnormalities were detected by NODDI in young obese patients, which might precede changes in brain glymphatic system function. Our study offers valuable insights into the degenerative trends observed in young individuals suffering from obesity and enhances our comprehension of the underlying biological mechanisms of WM microstructure alterations in depressed state in young individuals with obesity and food addiction.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 1","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coronary Artery Calcification (CAC) is a characteristic pathological alteration in the progression of coronary atherosclerosis and is considered an independent predictor of Major Adverse Cardiovascular Events (MACE). The distribution, pathological classification, and quantitative evaluation of CAC are pivotal factors influencing the incidence of MACE and guiding intracoronary interventions. Deep learning methods, a widely explored domain in artificial intelligence, achieve learning and understanding of big data by constructing multi-layer neural network models. This robust approach offers significant support for intelligent medical image diagnosis within clinical settings. Currently, deep learning methods have been applied to the identification and quantification of coronary artery calcification plaques, which not only improve diagnostic efficiency but also contribute to the early prevention and treatment of patients at moderate to low risk. This article reviews the progress of deep learning applications in coronary artery calcification to gain a comprehensive understanding of this field.
{"title":"Advancements in the application of deep learning for coronary artery calcification","authors":"Ke-Xin Tang, Yan-Lin Wu, Su-Kang Shan, Ling-Qing Yuan","doi":"10.1016/j.metrad.2025.100134","DOIUrl":"10.1016/j.metrad.2025.100134","url":null,"abstract":"<div><div>Coronary Artery Calcification (CAC) is a characteristic pathological alteration in the progression of coronary atherosclerosis and is considered an independent predictor of Major Adverse Cardiovascular Events (MACE). The distribution, pathological classification, and quantitative evaluation of CAC are pivotal factors influencing the incidence of MACE and guiding intracoronary interventions. Deep learning methods, a widely explored domain in artificial intelligence, achieve learning and understanding of big data by constructing multi-layer neural network models. This robust approach offers significant support for intelligent medical image diagnosis within clinical settings. Currently, deep learning methods have been applied to the identification and quantification of coronary artery calcification plaques, which not only improve diagnostic efficiency but also contribute to the early prevention and treatment of patients at moderate to low risk. This article reviews the progress of deep learning applications in coronary artery calcification to gain a comprehensive understanding of this field.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 1","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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