Pub Date : 2026-02-03DOI: 10.1186/s12880-026-02200-x
Ping Yin, Fei Zheng, Ying Liu, Simon Sun, Lin Lu, Hao Yang, Pengfei Geng, Jialiang Ren, Binsheng Zhao, Lawrence H Schwartz, Nan Hong
{"title":"Predicting neoadjuvant chemotherapy response in osteosarcoma using T2-based MRI delta-radiomics: a retrospective study.","authors":"Ping Yin, Fei Zheng, Ying Liu, Simon Sun, Lin Lu, Hao Yang, Pengfei Geng, Jialiang Ren, Binsheng Zhao, Lawrence H Schwartz, Nan Hong","doi":"10.1186/s12880-026-02200-x","DOIUrl":"https://doi.org/10.1186/s12880-026-02200-x","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112131","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 : 2026-02-03DOI: 10.1186/s12880-026-02193-7
Busra Nur Gokkurt Yilmaz, Birkan Eyup Yilmaz, Furkan Ozbey
{"title":"Effect of mandibular second premolar hypodontia on trabecular bone structure: a fractal analysis approach.","authors":"Busra Nur Gokkurt Yilmaz, Birkan Eyup Yilmaz, Furkan Ozbey","doi":"10.1186/s12880-026-02193-7","DOIUrl":"https://doi.org/10.1186/s12880-026-02193-7","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111988","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 : 2026-02-03DOI: 10.1186/s12880-026-02196-4
Minchul Kim, Yunseo Choi, Jung Hyun Park, Chul-Kee Park, Sung Hye Park, Seung Hong Choi, Kyu Sung Choi
{"title":"Pre-treatment subventricular zone perfusion asymmetry predicts vertical recurrence in glioblastoma.","authors":"Minchul Kim, Yunseo Choi, Jung Hyun Park, Chul-Kee Park, Sung Hye Park, Seung Hong Choi, Kyu Sung Choi","doi":"10.1186/s12880-026-02196-4","DOIUrl":"https://doi.org/10.1186/s12880-026-02196-4","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112121","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 : 2026-02-02DOI: 10.1186/s12880-026-02180-y
Zijun Li, Haotian Chen, Lihua Qiu, Hongwei Li, Shujiao Li, Yishuang Wang, Xiaojia Li, Fang Ye, Yuting Wang
{"title":"Gray matter atrophy mediated the association between glymphatic function and cognition in Alzheimer's disease: a multicenter DTI-ALPS study.","authors":"Zijun Li, Haotian Chen, Lihua Qiu, Hongwei Li, Shujiao Li, Yishuang Wang, Xiaojia Li, Fang Ye, Yuting Wang","doi":"10.1186/s12880-026-02180-y","DOIUrl":"https://doi.org/10.1186/s12880-026-02180-y","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103786","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}
Background: Magnetic resonance imaging (MRI) radiomics has shown promise in glioma grading and isocitrate dehydrogenase (IDH) mutation prediction, but traditional whole-tumor approaches overlook intratumoral heterogeneity, limiting diagnostic accuracy and interpretability. This study aims to explore cellularity habitat-based MRI radiomics for precise grading and IDH mutation status prediction in adult-type diffuse glioma (ADG).
Methods: A total of 625 ADG patients were retrospectively collected. Whole-tumor volumes of interest (VOIs) were delineated on four conventional MRI sequences (T1WI, T2WI, T2-FLAIR, and CE-T1WI) and segmented into three cellularity habitats using apparent diffusion coefficient (ADC)-based K-means clustering: H1 (low ADC), H2 (medium ADC), and H3 (high ADC). Radiomic features were extracted from individual and combined habitats, and predictive models were developed using a disentangled-learning-based multi-sequence fusion network (DMSFN). Performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).
Results: The optimal habitats for ADG grading (Grade 2 vs. Grade 3 + 4, Grade 2 + 3 vs. Grade 4) and IDH prediction were H1 + 2, H1 + 2, and H2 + 3, respectively. Combining T1WI, CE-T1WI, and T2-FLAIR sequences yielded the highest AUCs of 0.9360, 0.9605, and 0.8721 in the training set, and 0.8070, 0.8236, and 0.8180 in the independent test set. Shapley Additive exPlanation (SHAP) analysis identified key radiomic features contributing to model predictions, with CE-T1WI features consistently demonstrating high discriminative power.
Conclusions: Integrating ADC-derived cellularity habitats with MRI radiomics significantly improves the accuracy and biological interpretability of ADG grading and IDH mutation status prediction, offering a robust, non-invasive approach for glioma characterization.
{"title":"Cellularity habitat-based MRI radiomics for non-invasive grading and IDH mutation prediction in adult-type diffuse glioma.","authors":"Fangrong Liang, Xin Zhen, Jiaxin Lin, Junjie Li, Jie Zhan, Ruili Wei, Wanli Zhang, Yujie He, Mengni Wu, Yongzhou Xu, Yaou Liu, Shengsheng Lai, Ruimeng Yang","doi":"10.1186/s12880-026-02182-w","DOIUrl":"https://doi.org/10.1186/s12880-026-02182-w","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance imaging (MRI) radiomics has shown promise in glioma grading and isocitrate dehydrogenase (IDH) mutation prediction, but traditional whole-tumor approaches overlook intratumoral heterogeneity, limiting diagnostic accuracy and interpretability. This study aims to explore cellularity habitat-based MRI radiomics for precise grading and IDH mutation status prediction in adult-type diffuse glioma (ADG).</p><p><strong>Methods: </strong>A total of 625 ADG patients were retrospectively collected. Whole-tumor volumes of interest (VOIs) were delineated on four conventional MRI sequences (T1WI, T2WI, T2-FLAIR, and CE-T1WI) and segmented into three cellularity habitats using apparent diffusion coefficient (ADC)-based K-means clustering: H<sub>1</sub> (low ADC), H<sub>2</sub> (medium ADC), and H<sub>3</sub> (high ADC). Radiomic features were extracted from individual and combined habitats, and predictive models were developed using a disentangled-learning-based multi-sequence fusion network (DMSFN). Performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).</p><p><strong>Results: </strong>The optimal habitats for ADG grading (Grade 2 vs. Grade 3 + 4, Grade 2 + 3 vs. Grade 4) and IDH prediction were H<sub>1 + 2</sub>, H<sub>1 + 2</sub>, and H<sub>2 + 3</sub>, respectively. Combining T1WI, CE-T1WI, and T2-FLAIR sequences yielded the highest AUCs of 0.9360, 0.9605, and 0.8721 in the training set, and 0.8070, 0.8236, and 0.8180 in the independent test set. Shapley Additive exPlanation (SHAP) analysis identified key radiomic features contributing to model predictions, with CE-T1WI features consistently demonstrating high discriminative power.</p><p><strong>Conclusions: </strong>Integrating ADC-derived cellularity habitats with MRI radiomics significantly improves the accuracy and biological interpretability of ADG grading and IDH mutation status prediction, offering a robust, non-invasive approach for glioma characterization.</p><p><strong>Trial registration: </strong>Retrospectively registered.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096882","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}
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