Pub Date : 2026-02-20DOI: 10.1186/s12880-026-02239-w
Peiying Hua, Jessica M Sin, Eric R Henderson, Jason Ha, Darcy A Kerr, Saeed Hassanpour
{"title":"Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs.","authors":"Peiying Hua, Jessica M Sin, Eric R Henderson, Jason Ha, Darcy A Kerr, Saeed Hassanpour","doi":"10.1186/s12880-026-02239-w","DOIUrl":"10.1186/s12880-026-02239-w","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1186/s12880-026-02233-2
Shuo Shao, Xueqi Zhou, Yingying Qiao, Ning Zheng, Dexin Yu, Lei Feng
{"title":"Free water in white matter links focal hemodynamic compromise to cognitive impairment in asymptomatic extracranial carotid artery stenosis.","authors":"Shuo Shao, Xueqi Zhou, Yingying Qiao, Ning Zheng, Dexin Yu, Lei Feng","doi":"10.1186/s12880-026-02233-2","DOIUrl":"https://doi.org/10.1186/s12880-026-02233-2","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1186/s12880-026-02168-8
Ramin Rasi, Albert Guvenis
{"title":"Subregional limbic radiomics on FDG-PET provides accurate early detection of Alzheimer's disease.","authors":"Ramin Rasi, Albert Guvenis","doi":"10.1186/s12880-026-02168-8","DOIUrl":"10.1186/s12880-026-02168-8","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":"98"},"PeriodicalIF":3.2,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12922427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1186/s12880-026-02229-y
Yunjing Zhu, Qunhui Chen, Huiyuan Zhu, Kai Nie, Li Zhu, Lingming Yu, Guangyu Tao, Jun Xing, Shaojie Li, Yanbing Sun, Qiming Ni, Weizheng Kong, Hong Yu, Lin Zhu
{"title":"The impact of chest computed tomography-defined emphysema on extrapulmonary metastases in patients with lung cancer.","authors":"Yunjing Zhu, Qunhui Chen, Huiyuan Zhu, Kai Nie, Li Zhu, Lingming Yu, Guangyu Tao, Jun Xing, Shaojie Li, Yanbing Sun, Qiming Ni, Weizheng Kong, Hong Yu, Lin Zhu","doi":"10.1186/s12880-026-02229-y","DOIUrl":"https://doi.org/10.1186/s12880-026-02229-y","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-17DOI: 10.1186/s12880-026-02213-6
Fei Qi, Qian Ye, Chang Xu, Jianlin Wang, Liugang Gao
Accurate segmentation of esophageal cancers in CT images is crucial for disease treatment planning but remains difficult due to variable tumor morphology, low contrast with surrounding tissues, and blurred boundaries. We propose MDPNet, a Multi-scale Difference Perception Network for accurate esophageal cancer segmentation in CT images. MDPNet integrates three key modules, a Dynamic Feature Enhancement (DFE) strategy for global and local context fusion, a Cross-level Difference Modeling (CDM) module to highlight foreground-background differences, and a Multi-stage Foreground Enhancement (MFE) mechanism for progressive boundary refinement. Experiments on the self-built ECD 2D dataset and an external test set show that MDPNet achieves the best performance among state-of-the-art methods, with Dice coefficients of 0.82 and 0.78, respectively. MDPNet effectively improves segmentation accuracy and generalization, demonstrating preliminary generalization capability on our multi-center test sets, suggesting its potential as a decision-support tool.
{"title":"MDPNet: a multi-scale difference perception network for esophageal cancer segmentation in CT images.","authors":"Fei Qi, Qian Ye, Chang Xu, Jianlin Wang, Liugang Gao","doi":"10.1186/s12880-026-02213-6","DOIUrl":"https://doi.org/10.1186/s12880-026-02213-6","url":null,"abstract":"<p><p>Accurate segmentation of esophageal cancers in CT images is crucial for disease treatment planning but remains difficult due to variable tumor morphology, low contrast with surrounding tissues, and blurred boundaries. We propose MDPNet, a Multi-scale Difference Perception Network for accurate esophageal cancer segmentation in CT images. MDPNet integrates three key modules, a Dynamic Feature Enhancement (DFE) strategy for global and local context fusion, a Cross-level Difference Modeling (CDM) module to highlight foreground-background differences, and a Multi-stage Foreground Enhancement (MFE) mechanism for progressive boundary refinement. Experiments on the self-built ECD 2D dataset and an external test set show that MDPNet achieves the best performance among state-of-the-art methods, with Dice coefficients of 0.82 and 0.78, respectively. MDPNet effectively improves segmentation accuracy and generalization, demonstrating preliminary generalization capability on our multi-center test sets, suggesting its potential as a decision-support tool.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146212041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: To develop and evaluate a deep learning-based method for fully automatic segmentation of the knee joint and to diagnose and classify knee osteoarthritis (KOA ) using prior information.
Methods: All X-rays were obtained from the Osteoarthritis Initiative (OAI). We comprehensively consider the a priori information of knee data, redesign the KOA assessment process, and propose the Anchor-free Knee Probability Calculation Net (AKPCNet) knee joint region of interest extraction algorithm by calculating the probability of each point as the left and right knee joint centroids, determining the location of the centroid of the knee joint, and then extracting the region of fixed size resolution around the centroid. We propose Attention Pooling, a global pooling optimization algorithm, and an attention pooling based low-order feature reinforcement network (APLFRNet) to improve the KL classification accuracy of KOA. The performance of the classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and balancing accuracy.
Results: In total, 35,000 knee radiographs (anteroposterior view) were obtained from the Osteoarthritis Initiative (OAI). The accuracy of automatic recognition of left and right knee joint center points was 97.8% and 97.4%, respectively. The balancing accuracy of knee osteoarthritis assessment of five classifications according to the grading was 73.23%, the balancing accuracy of three classifications (KL0-1 vs.KL2vs.KL3-4) for estimating severity reached 82.22%, the accuracy rate of two classifications (KL0-1vs. KL2-4) for diagnosis was 87.6%. The accuracy of early diagnosis (KL0 vs. KL1 and KL0 vs. KL2) was 66.58% and 87.1%, respectively average areas under the curve of the five classifications, three classifications, and two classifications were 0.90,0.95 and 0.94 respectively.
Conclusions: Our new algorithm can accurately assess the severity of knee osteoarthritis (KOA) and provide decision support for research and clinical practice.
目的：开发和评估一种基于深度学习的膝关节全自动分割方法，并利用先验信息对膝关节骨关节炎（KOA）进行诊断和分类。方法：所有x线片均取自骨关节炎倡议（OAI）。综合考虑膝关节数据的先验信息，重新设计KOA评估流程，提出无锚膝关节概率计算网（Anchor-free knee Probability Calculation Net, AKPCNet）膝关节感兴趣区域提取算法，通过计算每个点作为左右膝关节质心的概率，确定膝关节质心的位置，然后在质心周围提取固定大小分辨率的区域。为了提高KOA的KL分类精度，我们提出了全局池化优化算法Attention Pooling和基于注意力池的低阶特征强化网络（APLFRNet）。使用受试者工作特征曲线下面积（ROC AUC）和平衡精度来评估分类模型的性能。结果：共获得骨关节炎倡议（OAI）的35000张膝关节x线片（正位）。左、右膝关节中心点的自动识别准确率分别为97.8%和97.4%。膝关节骨关节炎的平衡精度评估的五分类根据分级为73.23%,平衡精度的三个分类(KL0-1 vs.KL2vs。KL3-4)对严重程度的估计准确率达到82.22%，两种分类（KL0-1vs.）KL2-4)诊断为87.6%。早期诊断（KL0对KL1、KL0对KL2）的准确率分别为66.58%、87.1%，五分、三分、二分的平均曲线下面积分别为0.90、0.95、0.94。结论：新算法能准确评估膝关节骨性关节炎（KOA）的严重程度，为研究和临床实践提供决策支持。
{"title":"Novel algorithm for knee localization and diagnosis and grading of knee osteoarthritis based on a priori information: data from OAI.","authors":"Chunbo Deng, Chengbao Peng, Yingwei Sun, Guan Wang, Jing Liu, Xueyong Liu","doi":"10.1186/s12880-026-02235-0","DOIUrl":"https://doi.org/10.1186/s12880-026-02235-0","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and evaluate a deep learning-based method for fully automatic segmentation of the knee joint and to diagnose and classify knee osteoarthritis (KOA ) using prior information.</p><p><strong>Methods: </strong>All X-rays were obtained from the Osteoarthritis Initiative (OAI). We comprehensively consider the a priori information of knee data, redesign the KOA assessment process, and propose the Anchor-free Knee Probability Calculation Net (AKPCNet) knee joint region of interest extraction algorithm by calculating the probability of each point as the left and right knee joint centroids, determining the location of the centroid of the knee joint, and then extracting the region of fixed size resolution around the centroid. We propose Attention Pooling, a global pooling optimization algorithm, and an attention pooling based low-order feature reinforcement network (APLFRNet) to improve the KL classification accuracy of KOA. The performance of the classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and balancing accuracy.</p><p><strong>Results: </strong>In total, 35,000 knee radiographs (anteroposterior view) were obtained from the Osteoarthritis Initiative (OAI). The accuracy of automatic recognition of left and right knee joint center points was 97.8% and 97.4%, respectively. The balancing accuracy of knee osteoarthritis assessment of five classifications according to the grading was 73.23%, the balancing accuracy of three classifications (KL0-1 vs.KL2vs.KL3-4) for estimating severity reached 82.22%, the accuracy rate of two classifications (KL0-1vs. KL2-4) for diagnosis was 87.6%. The accuracy of early diagnosis (KL0 vs. KL1 and KL0 vs. KL2) was 66.58% and 87.1%, respectively average areas under the curve of the five classifications, three classifications, and two classifications were 0.90,0.95 and 0.94 respectively.</p><p><strong>Conclusions: </strong>Our new algorithm can accurately assess the severity of knee osteoarthritis (KOA) and provide decision support for research and clinical practice.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146212111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis.","authors":"Manxin Yin, Chunhai Yu, Qiaona Su, Xin Song, Qing Zhao, Jianxin Zhang","doi":"10.1186/s12880-026-02203-8","DOIUrl":"https://doi.org/10.1186/s12880-026-02203-8","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146206665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号