Meta-Radiology最新文献

{"title":"Response to “Commentary on Ferroptosis, M6A and immune checkpoint-related gene expression in the middle temporal gyrus of the Alzheimer's disease brain”","authors":"Jingwen Hu , Kai Yuan , Suping Cai","doi":"10.1016/j.metrad.2025.100156","DOIUrl":"10.1016/j.metrad.2025.100156","url":null,"abstract":"","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135203","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}

{"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":"2025-05-19","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}

{"title":"Reference ranges of CT quantitative indexes based on paired inspiratory and expiratory chest CT in healthy Chinese adults: A community-based cohort study","authors":"Ziwei Zhang ,&nbsp;Xiuxiu Zhou ,&nbsp;Yi Xia ,&nbsp;Qianxi Jin ,&nbsp;Yu Guan ,&nbsp;Taohu Zhou ,&nbsp;Yueze Li ,&nbsp;Shiyuan Liu ,&nbsp;Li Fan","doi":"10.1016/j.metrad.2025.100154","DOIUrl":"10.1016/j.metrad.2025.100154","url":null,"abstract":"<div><h3>Purpose</h3><div>Computed tomography (CT) screening has established itself as the routine method to monitor lung conditions by providing pulmonary structural and functional information. However, the reference ranges of CT indices are still lacking. Therefore, this study aimed to provide reference ranges for CT quantitative indexes in healthy middle-aged and elderly Chinese population.</div></div><div><h3>Methods</h3><div>A total of 783 healthy Chinese adults attending our hospital for the NELCIN-B3 protocol were enrolled. Paired inspiratory and expiratory chest CT and pulmonary function tests (PFTs) were performed in all subjects, CT indices based on density and parametric response map (PRM) were obtained, and PFTs and CT data were retrospectively collected. The reference range was obtained with the Lambda-Mu-Sigma model. Independent-sample <em>t</em>-test/one-way ANOVA analysis or Mann-Whitney test/Kruskal-Wallis test were conducted to compare mean values between different groups. Spearman correlation analysis was used to evaluate the correlation between spirometric and CT quantitative indices.</div></div><div><h3>Results</h3><div>A total of 783 healthy subjects (255 men, 67 (63–69) years) were included. The reference ranges of lung volume, the percentage of low attenuation area (LAA (%)), PRM^Emphy (%) and PRM^fSAD (%) concerning age based on gender were established. LAA (%), PRM^Emphy (%) and PRM^fSAD (%) were significant higher in the upper lobes than in the lower lobes. There was a strong positive correlation between PRM^Emphy (%) and PRM^fSAD (%) (r ​= ​0.62, p ​&lt; ​0.001).</div></div><div><h3>Conclusions</h3><div>We established Chinese reference ranges for CT quantitative indexes in the population aged from 40 to 75 years old.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089642","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}

{"title":"Critical review of patient outcome study in head and neck cancer radiotherapy","authors":"Jingyuan Chen ,&nbsp;Yunze Yang ,&nbsp;Chenbin Liu ,&nbsp;Hongying Feng ,&nbsp;Jason M. Holmes ,&nbsp;Lian Zhang ,&nbsp;Steven J. Frank ,&nbsp;Charles B. Simone II ,&nbsp;Daniel J. Ma ,&nbsp;Samir H. Patel ,&nbsp;Wei Liu","doi":"10.1016/j.metrad.2025.100151","DOIUrl":"10.1016/j.metrad.2025.100151","url":null,"abstract":"<div><div>In modern radiation therapy for head and neck cancers, the treatment related toxicities remain a significant clinical challenge. This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy. Three transformative methodological advances are reviewed: radiomics, AI-based algorithms, and causal inference frameworks. The integration of linear energy transfer in patient outcomes study, which has uncovered critical mechanisms behind unexpected toxicity, was also introduced for proton therapy. While radiomics has transformed medical image analysis through comprehensive quantitative characterization, AI models have demonstrated markedly superior predictive capabilities over traditional approaches, offering promising avenues for personalized radiation therapy with reduced toxicity profiles. However, the field faces significant challenges in translating statistical correlations from real-world data into interventional clinical insights. We highlight how causal inference methods can bridge this gap by providing a rigorous framework for identifying treatment effects. Looking ahead, we envision that combining these complementary approaches, especially the interventional prediction models, will enable more personalized treatment strategies, ultimately improving both tumor control and quality of life for head and neck cancer patients treated with radiation therapy.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 3","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722752","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}

{"title":"Large language model-based multi-source integration pipeline for automated diagnostic classification and zero-shot prognoses for brain tumor","authors":"Zhuoqi Ma ,&nbsp;Lulu Bi ,&nbsp;Paige Collins ,&nbsp;Owen Leary ,&nbsp;Maliha Imami ,&nbsp;Zhusi Zhong ,&nbsp;Shaolei Lu ,&nbsp;Grayson Baird ,&nbsp;Nikos Tapinos ,&nbsp;Ugur Cetintemel ,&nbsp;Harrison Bai ,&nbsp;Jerrold Boxerman ,&nbsp;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":"2025-04-29","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}

{"title":"Potential neural mechanisms and imaging changes in type 2 diabetes with cognitive impairment","authors":"Jiahe Wang ,&nbsp;Die Shen ,&nbsp;Kun Wang ,&nbsp;Ziyu Diao ,&nbsp;Xuan Huang ,&nbsp;Jianyu Li ,&nbsp;Shijun Qiu","doi":"10.1016/j.metrad.2025.100149","DOIUrl":"10.1016/j.metrad.2025.100149","url":null,"abstract":"<div><div>Many studies have demonstrated that type 2 diabetes mellitus (T2DM) can lead to various complications. In this review, we examine the central nervous system (CNS) symptoms it causes, focusing on cognitive impairment (CI). T2DM and CI share several biological mechanisms, and multimodal imaging techniques facilitate the identification of brain alterations in patients ​T2DM with cognitive impairment (T2DM-CI). This review specifically discusses the potential pathophysiological mechanisms of T2DM-CI and the associated changes in brain structure and function. We propose that early, sustained attention to CNS alterations in T2DM is crucial for developing effective prevention and management strategies.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 3","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722655","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}

{"title":"Imaging transcriptional pattern of the substantia Nigra predicts response to antipsychotics in schizophrenia","authors":"Long-Biao Cui,&nbsp;Hai-Jun Zhang,&nbsp;Xiao-Hui Wang","doi":"10.1016/j.metrad.2025.100146","DOIUrl":"10.1016/j.metrad.2025.100146","url":null,"abstract":"","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089641","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}

{"title":"Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review","authors":"Zhizhong Sun ,&nbsp;Zidong Cao ,&nbsp;Limin Ge ,&nbsp;Yifan Li ,&nbsp;Haoming Huang ,&nbsp;Mingrui Li ,&nbsp;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":"2025-03-11","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}

{"title":"The role of 4D flow MRI in deep vein thrombosis research","authors":"Meizhi Li ,&nbsp;Shangjie Wu ,&nbsp;Xiao Liang ,&nbsp;Chuanqi Gao ,&nbsp;Muhua Hu ,&nbsp;Zhu Chen ,&nbsp;Pei He ,&nbsp;Tingting Jia ,&nbsp;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}

{"title":"Microstructure changes of the brain preceded glymphatic function changes in young obesity with and without food addiction","authors":"Meizhi Yi ,&nbsp;Zeng Yule ,&nbsp;Weijia Song ,&nbsp;Tianyao Wang ,&nbsp;Luokai Zhang ,&nbsp;Can Hu ,&nbsp;Yifeng Peng ,&nbsp;Zhaoxiang Zhang ,&nbsp;Liangwu Chen ,&nbsp;Yan Wang ,&nbsp;Huiting Wu ,&nbsp;Zhaojie Peng ,&nbsp;Xinhua Xiao ,&nbsp;Jun Liu ,&nbsp;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> ​&lt; ​0.05). Compared with HC, the alterations in NODDI metrics mainly reflected in decreased Vic and Viso in OFD (<em>P</em> ​&lt; ​0.05). In addition, our results showed decreased Vic and Viso in OFD compared with ONFD (<em>P</em> ​&lt; ​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> ​&lt; ​0.05). The partial correlation analysis disclosed a significant negative association between Vic and HAMD (<em>P</em> ​&lt; ​0.05), and between the Viso and HAMD for all obese patients (<em>P</em> ​&lt; ​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}