BMC Medical Imaging最新文献

{"title":"A radiomics model based on diffusion-weighted imaging developed using machine learning enables prediction of microsatellite instability in endometrial cancer.","authors":"Meng Zhang, Xuejia Wang, Zhong Li, Wenling Liu, Xuekun Li, Xingxing Jin, Jinxia Guo, Kaiyu Wang, Yuxia Li, Jipeng Ren","doi":"10.1186/s12880-025-01944-2","DOIUrl":"https://doi.org/10.1186/s12880-025-01944-2","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"405"},"PeriodicalIF":3.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145249585","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":"Semi-quantitative study of magnetic resonance imaging features of disc-condylar complex in patients with anterior disc displacement without reduction of temporomandibular joint.","authors":"Meng Liao, Xuehui Chen, Kai Liu, Honglin He, Yu Tao, Dan Zhang, Ting Hu, Bibin Duan","doi":"10.1186/s12880-025-01888-7","DOIUrl":"10.1186/s12880-025-01888-7","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"403"},"PeriodicalIF":3.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237853","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}

{"title":"Enhanced diagnostic performance for subcentimeter hepatocellular carcinoma using a novel criterion integrating serum AFP levels and gadolinium-based contrast-enhanced MRI features.","authors":"Xuying Chen, Haoran Dai, Jing Liu, Siyuan Ji, Yuyao Xiao, Xinde Zheng, Kai Hou, Chun Yang","doi":"10.1186/s12880-025-01949-x","DOIUrl":"10.1186/s12880-025-01949-x","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the effectiveness of LI-RADS v2018 and r-LI-RADS in diagnosing subcentimeter hepatocellular carcinoma (HCC) and to evaluate the potential value of serum alpha-fetoprotein (AFP) in conjunction with gadolinium-based contrast-enhanced MRI (CE-MRI) for assessing these lesions.</p><p><strong>Methods: </strong>This retrospective study included 179 untreated, high-risk patients with microlesions (< 1 cm) from 2015 to 2023. Of these, 92 lesions were pathologically confirmed as HCC, the remaining 87 were non-HCC. Two radiologists independently rated imaging features using LI-RADS and r-LI-RADS. The optimal AFP threshold for HCC diagnosis was determined by the Youden index. Logistic regression analyses identified independent predictors of micro-HCC, leading to the development of new diagnostic criteria.</p><p><strong>Results: </strong>Multivariate analysis identified AFP > 12.15 ng/mL, non-peripheral arterial phase enhancement, diffusion restriction, fat deposition, and enhancing capsule as key independent factors for diagnosing micro-HCC. A new criterion requiring at least three positive factors improved sensitivity to 78.3% vs. 58.7% for LR-4 (p = 0.001), with similar specificity (83.9% vs. 81.6%, p = 0.824). It also outperformed r-LR-5/4 and r-LR-4 in sensitivity (78.3% vs. 59.8% and 58.7%, both p = 0.001), without impacting specificity.</p><p><strong>Conclusion: </strong>A new criterion (at least three positive findings among AFP > 12.15 ng/mL, non-peripheral arterial phase enhancement, diffusion restriction, fat deposition, and enhancing capsule) significantly improves the diagnostic sensitivity for subcentimeter HCC while maintaining high specificity, demonstrating clear advantages over the LR-4, r-LR-5/4, and r-LR-4 standards of LI-RADS.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"404"},"PeriodicalIF":3.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237877","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}

{"title":"Dosiomic and radiomic features within radiotherapy target volume for predicting the treatment response in patients with glioma after radiotherapy.","authors":"Yixin Wang, Yongkang Zhang, Lin Lin, Zongtao Hu, Hongzhi Wang","doi":"10.1186/s12880-025-01955-z","DOIUrl":"10.1186/s12880-025-01955-z","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop interpretable machine learning models using radiomic and dosiomic features from radiotherapy target volumes to predict treatment response in glioma patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 176 glioma patients. Treatment response was categorized into disease control rate (DCR) and non-DCR groups (training cohort: 71 vs. 44; validation cohort: 34 vs. 27). Five regions of interest (ROIs) were identified: gross tumor volume (GTV), gross tumor volume with tumor bed (GTVtb), clinical target volume (CTV), GTV-GTV and CTV-GTVtb. For each ROI, radiomic features and dosiomic features were separately extracted from CT images and dose maps. Feature selection was performed. Six dosimetric parameters and six clinical variables were also included in model development. Five predictive models were constructed using four machine learning algorithms: Radiomic, Dosiomic, Dose-Volume Histogram (DVH), Combined (integrating clinical, radiomic, dosiomic, and DVH features), and Clinical models. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). SHAP analysis was applied to explain model predictions.</p><p><strong>Results: </strong>The CTV_combined support vector machine (SVM) model achieved the best performance, with an AUC of 0.728 in the validation cohort. SHAP summary plots showed that dosiomic features contributed significantly to prediction. Force plots further illustrated how individual features affected classification outcomes.</p><p><strong>Conclusion: </strong>The SHAP-interpretable CTV_combined SVM model demonstrated strong predictive ability for treatment response in glioma patients. This approach may support radiation oncologists in identifying the underlying pathological mechanisms of poor treatment response and adjusting dose distribution accordingly, thereby aiding the development of personalized radiotherapy strategies.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"402"},"PeriodicalIF":3.2,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145211567","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}

{"title":"3DSN-net: dual-tandem attention mechanism interaction network for breast tumor classification.","authors":"Lin Li, Mingrui Wang, Dongjie Li, Tao Yang","doi":"10.1186/s12880-025-01936-2","DOIUrl":"10.1186/s12880-025-01936-2","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"393"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190873","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}

{"title":"Hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase: differentiation using Gd-EOB-DTPA enhanced MRI radiomics and deep learning features.","authors":"Hao-Yu Mao, Jing-Cheng Hu, Tao Zhang, Yan-Fen Fan, Xi-Ming Wang, Chun-Hong Hu, Yi-Xing Yu","doi":"10.1186/s12880-025-01927-3","DOIUrl":"10.1186/s12880-025-01927-3","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"397"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190799","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}

{"title":"Nomogram to predict the EGFR mutation status in stage III-IV solid lung adenocarcinoma patients.","authors":"Wenjian Tang, Yuan Kang, Bo Lan, Zhiqiang Zhang, Jiawei Feng, Feng Li, Xinyi Zeng, Junyuan Zhong, Shuhua Luo, Jianping Zhong","doi":"10.1186/s12880-025-01922-8","DOIUrl":"10.1186/s12880-025-01922-8","url":null,"abstract":"<p><strong>Background: </strong>To assess the clinical characteristics and CT findings associated with epidermal growth factor receptor (EGFR) mutation status in stage III-IV solid lung adenocarcinoma (LAD) patients.</p><p><strong>Methods: </strong>In this retrospective study, stage III-IV solid LAD patients who underwent chest CT from January 2015 to July 2025 were included. Clinical characteristics and CT findings significantly associated with the EGFR mutation status were identified via multivariable logistic regression.</p><p><strong>Results: </strong>A total of 420 patients with stage III-IV solid LAD were included (training cohort: 375 patients, from January 2015 to April 2024; validation cohort: 45 patients, from May 2024 to July 2025). Compared with wild-type EGFR patients, EGFR-mutant LAD were significantly younger (< 60 years), more likely to be female, nonsmokers, and to have stage IV disease. In terms of CT findings, patients with mutant EGFR were more likely to have a tumor size < 4.5 cm, a well-defined tumor boundary, a vessel convergence sign, pleural indentation and obstructive pneumonia or atelectasis. In the multivariable analysis, age (OR, 0.428; 95% CI 0.242-0.756), sex (OR, 0.200; 95% CI 0.112-0.356), overall stage (OR, 2.230; 95% CI 1.141-4.359), tumor size (OR, 0.474; 95% CI 0.260-0.864, P = 0.015), tumor boundary (OR, 3.461; 95% CI 1.877-6.382), the presence of the vessel convergence sign (OR, 2.869; 95% CI 1.675-4.913), and obstructive pneumonia or atelectasis (OR, 3.870; 95% CI 2.028-7.385) were identified as factors that independently predict the EGFR mutation status. We further constructed a nomogram for predicting the EGFR mutation status via a logistic regression model. Logit (P) = 0.197 + (-1.599) × sex + (-0.850) × age + 0.900 × overall stage + (-0.762) × tumor size + 1.246 × tumor boundary + 1.042 × vessel convergence sign + 1.367 × obstructive pneumonia or atelectasis. The area under the curve (AUC) of the nomogram in training cohort was 0.829 (95% CI: 0.783, 0.876). In the validation cohort, the AUC was 0.826 (95% CI: 0.681, 0.970).</p><p><strong>Conclusions: </strong>A nomogram including sex, age, overall stage, tumor size, tumor boundary, the vessel convergence sign, and obstructive pneumonia or atelectasis, was helpful in predicting the EGFR mutation status in stage III-IV solid LAD patients.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"391"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190909","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}

{"title":"Preoperative prediction of central lymph node metastasis in clinically lymph node negative papillary thyroid microcarcinoma: a nomogram based on clinical and ultrasound features.","authors":"Minying Zhong, Deli Chen, Jieyi Ye, Yinting Chen, Chi Ma, Sixin Cheng, WeiJun Huang, Shijun Qiu","doi":"10.1186/s12880-025-01920-w","DOIUrl":"10.1186/s12880-025-01920-w","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative assessment of central lymph nodes is crucial for determining the extent of surgery for papillary thyroid microcarcinoma (PTMC). Patients who are clinically lymph node negative (cN0) lack clinical evidence of central lymph node metastasis (CLNM) on preoperative ultrasonography or computed tomography. This study aimed to identify clinical factors associated with CLNM based on ultrasonographic features and clinical data, and to develop a nomogram for personalised clinical decision-making.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on patients diagnosed with cN0 PTMC who were treated at the Vascular Thyroid Surgery Department of the Hospital from December 2020 to February 2022, totaling 834 individuals. The patients were divided into CLNM and non-CLNM groups based on postoperative pathology. The clinical characteristics and ultrasonographic features of the PTMC were collected. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was applied in R for feature selection. A nomogram was then developed based on multivariable logistic regression using the predictors selected by the LASSO algorithm. The receiver operating characteristic curve and Hosmer-Lemeshow test were used to assess the discrimination and calibration of the nomogram model, respectively. Decision curve analysis (DCA) was performed using the Risk Model Decision Analysis package to evaluate the clinical utility of the model in the validation dataset.</p><p><strong>Results: </strong>Six variables associated with patients with PTMC were identified through LASSO shrinkage and selection operator regression analysis and used to establish the nomogram. The predictive model showed an area under the receiver operating characteristic curve (AUC) of 0.719 (95% confidence interval (CI) 0.681-0.757), and in internal validation, the AUC was 0.717 (95% CI 0.683-0.754). The calibration curve indicated a good fit for the model, and the Hosmer-Lemeshow test demonstrated a close match between the predicted and observed values (P = 0.437). DCA revealed that applying the nomogram to predict the risk of CLNM would be beneficial for patients with PTMC when the threshold probability was between > 12.5% and < 75%.</p><p><strong>Conclusion: </strong>The LASSO regression model nomogram based on clinical risk factors and ultrasonographic features is valuable in predicting CLNM in cN0 PTMC, and can assist surgeons in making more personalised clinical decisions.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"392"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190960","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}

{"title":"Assessment of non-alcoholic fatty liver disease in suspected coronary artery disease patients: prognostic value and incremental predictive utility over cardiovascular risk factors and CCTA.","authors":"Changqin Jiang, Qing Yang, Xueyan Hou, Mengnan Zhang, Zhiqiang Yang, Ruili Qin, Xixia Sun, Xiaoling Tao, Qiang Feng, Zhaoqian Wang","doi":"10.1186/s12880-025-01919-3","DOIUrl":"10.1186/s12880-025-01919-3","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"398"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190850","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}

{"title":"ResViT-GANNet: a deep learning framework for classifying breast cancer histopathology images using multimodal attention and GAN-based augmentation.","authors":"Yuee Zhou, Fengqing Jin, Guodong Suo, Jianlan Yang","doi":"10.1186/s12880-025-01940-6","DOIUrl":"10.1186/s12880-025-01940-6","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"401"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145190884","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}