BMC Medical Imaging最新文献

{"title":"A similarity-aware network with contrastive optimization for biomedical image segmentation.","authors":"Rongjia Lin, Zhidong Yang, Ziheng Xu, Na Gao, Rui Yan, Xiuhui Shi, Senlin Lin, Bowen Huang","doi":"10.1186/s12880-026-02644-1","DOIUrl":"10.1186/s12880-026-02644-1","url":null,"abstract":"<p><strong>Background: </strong>Accurate biomedical image segmentation is crucial for clinical diagnosis. Convolutional neural networks and Transformer-based models have been widely used for biomedical image segmentation and have improved segmentation accuracy across multiple imaging modalities. However, most existing methods rely heavily on supervised learning with large-scale annotated datasets, while professionally annotated biomedical images remain scarce. In addition, the geometric consistency available from augmented and unlabeled images is not always explicitly exploited.</p><p><strong>Methods: </strong>We proposed a similarity-aware network with contrastive optimization for biomedical image segmentation, termed SimBIS. SimBIS supplements pixel-wise supervised learning with an output-space consistency objective. Specifically, segmentation predictions generated from augmented views of the same object are projected into a seven-dimensional Hu-moment space, and the discrepancy between these projections is minimized during training. The Hu-moment is used as a parameter-free auxiliary descriptor of global mask geometry, which is invariant to translation, isotropic scaling, and in-plane rotation. Fine-grained and irregular boundaries are still optimized through the pixel-level supervised loss.</p><p><strong>Results: </strong>SimBIS improved mDice and mIoU on both LID and SID datasets across multiple classic segmentation baselines. On publicly available biomedical segmentation tasks, including Kvasir-Seg, CVC-ClinicDB, and ISIC 2018, SimBIS not only improved the performance of the original backbone models but also outperformed other state-of-the-art methods on various metrics. Visual segmentation results further showed that SimBIS can effectively segment small targets and distinguish challenging structures, producing more accurate and consistent contours than existing models.</p><p><strong>Conclusions: </strong>SimBIS improves biomedical image segmentation by incorporating Hu-moment-based global shape consistency into end-to-end optimization while retaining pixel-wise supervision. It effectively leverages augmented and unlabeled data without introducing additional trainable parameters or inference-time cost, demonstrating strong potential for annotation-efficient biomedical image segmentation.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148705284","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":"Online adaptive radiotherapy in pelvic and thoracic cancers - comparing toxicities, clinical outcomes and technical parameters between conventional image-guided radiotherapy and online adaptive radiotherapy - the study protocol for the prospective, registry-based cohort study (PRoART).","authors":"Laura Anna Fischer, Jann Fischer, Niklas Christian Scheele, Leif Hendrik Droege, Martin Leu, Manuel Guhlich, Jan Tobias Oelmann-Avendano, Andrea Hille, Stephanie Bendrich, Sandra Donath, Olga Knaus, David Alexander Ziegler, Carla Marie Zwerenz, Mahalia Zoe Anczykowski, Hanne Elisabeth Ammon, Pia Franziska Luise Bergau, Charlotta Friederike Pagel, Markus Anton Schirmer, Alina Renata Wenzel, Jasper Frohn, Jona Bensberg, Arne Strauß, Thomas Asendorf, Daniela Schmitt, Stefan Rieken, Rami Atejah El Shafie","doi":"10.1186/s12880-026-02622-7","DOIUrl":"10.1186/s12880-026-02622-7","url":null,"abstract":"<p><strong>Background: </strong>Conventional image-guided radiotherapy (IGRT) typically relies on a computed tomography (CT)-based treatment planning process (planning CT, pCT) performed prior to the start of treatment. During this process, a patient-specific treatment plan is generated, which is then delivered to the patient with a linear accelerator on a daily basis, usually with image guidance to compensate for variations in patient setup. However, daily interfractional anatomical variations in target position, shape, and volume, as well as in surrounding organs at risk (OARs), can only be addressed indirectly by adding safety margins, resulting in larger irradiated volumes and potentially increased toxicity. Online adaptive radiotherapy (oART) is a promising and innovative technique to reduce such margins and, consequently, treatment-related toxicity. It uses daily imaging to generate a \"plan of the day\" that is aligned with the current patient anatomy, often supported by artificial intelligence (AI), while the patient remains on the treatment couch. Through daily image-guided re-optimization of the radiation treatment (RT) plan on the anatomy of the day, target coverage may also be improved. This approach is particularly attractive in the pelvic region, where high interfractional anatomical variability, for example due to peristalsis or changes in bladder and rectal filling, is frequently observed.</p><p><strong>Methods: </strong>This prospective registry-based cohort study will include patients with pelvic or thoracic tumors with an indication for RT treated with IGRT or oART using the Varian Ethos™ system. The primary endpoint is defined as a 10% reduction in the rate of acute RT related toxicity (≥Common Terminology Criteria for Adverse Events (CTCAE) II°, v5.0) using oART. Secondary endpoints encompass clinical outcomes including late toxicities, tumor control rates, and patient-reported outcomes (PROs), as well as technical factors such as target volume, coverage, dose to OARs and anatomical variability score. While the cohort study compares IGRT versus oART for primary and secondary clinical endpoints, it also evaluates the real oART scenario against two hypothetical control scenarios for technical endpoints.</p><p><strong>Discussion: </strong>The introduction of oART promises a reduction in toxicities and improved target volume coverage, potentially resulting in enhanced tumor control rates. It is poised to be a pioneering technology in the field of radiation oncology. Given the absence of a direct comparison between IGRT and oART thus far, the prospective registry-based cohort study PRoART aims to address this gap.</p><p><strong>Trial registration: </strong>Clinical trial number NCT06185062, Clinicaltrials.gov. Registered 12/14/2023. Last update 07/29/2026.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148676885","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":"A multimodal machine learning model for predicting reproductive outcomes in the rare congenital hypogonadotropic hypogonadism after treatment.","authors":"Xiaomeng Li, Chuyin Ruan, Zebin Wu, Sina Wang, Tianli Liang, Derun Pan, Ying Cao, Weiguo Chen","doi":"10.1186/s12880-026-02634-3","DOIUrl":"https://doi.org/10.1186/s12880-026-02634-3","url":null,"abstract":"<p><strong>Objective: </strong>Testicular volume serves as a crucial indicator for evaluating therapeutic outcomes in patients with congenital hypogonadotropic hypogonadism (CHH), which is a rare disease. We aim to develop a multimodal machine learning model that integrates clinical data, testicular ultrasonography, and pituitary magnetic resonance imaging (MRI) radiomic features to predict whether testicular volume exceeds 4 mL following treatment in CHH patients.</p><p><strong>Methods: </strong>This study retrospectively included 66 male patients with CHH from 2015 to 2024, with a follow-up period of 12 to 36 months. All patients received gonadotropin or GnRH pulse pump therapy. The patients' visit information was systematically collected, and the peak testicular volume during follow-up was used as the outcome. Multiple machine learning prediction models were constructed using clinical information, initial-visit testicular ultrasound images, and pituitary MRI images, and were evaluated using ten-fold cross-validation. Model performance was compared using the Delong test. Model interpretability was analyzed using SHapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>A total of 28 patients achieved a testicular volume greater than 4 mL after treatment, indicating favorable reproductive outcomes. The multimodal model that integrated all information demonstrated the highest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.932 (95% CI: 0.891-0.997), outperforming the clinical model (AUC = 0.863, 95% CI: 0.747-1.000). The best AUCs for models based solely on ultrasound and MRI radiomic features were 0.798 (95% CI: 0.697-1.000) and 0.680 (95% CI: 0.526-0.868), respectively. The combined imaging model (ultrasound + MRI) yielded an AUC of 0.759 (95% CI: 0.510-0.969). Among the top 10 SHAP-ranked features in the multimodal model, two originated from ultrasound, one from MRI, and seven from clinical variables.</p><p><strong>Conclusion: </strong>The multimodal model, which integrated clinical factors with radiomic features from both ultrasound and magnetic resonance imaging, demonstrated favorable performance in predicting reproductive outcomes in patients with CHH after treatment.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294272","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":"Development and validation of a preoperative biparametric MRI-based habitat model for risk stratification of non-muscle-invasive bladder cancer.","authors":"Pingping He, Ruiquan Chen, Qiaozhen Zheng, Minxuan Tian, Ming Chen, Wei Guo, Ruixiong You","doi":"10.1186/s12880-026-02630-7","DOIUrl":"https://doi.org/10.1186/s12880-026-02630-7","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative risk stratification of non-muscle-invasive bladder cancer (NMIBC) is essential for developing individualized treatment plans. This study developed an interpretable habitat model based on biparametric MRI to improve preoperative risk stratification in patients with NMIBC.</p><p><strong>Methods: </strong>This two-center study enrolled a total of 206 patients. The internal dataset (n = 147) included the training set (n = 81), test set (n = 35), and time-sequential validation set (n = 31). The external dataset included 59 patients. The primary outcome was the dichotomized risk stratification according to the European Association of Urology (EAU) guidelines (low-intermediate risk vs. high-very high risk). K-means clustering was applied to segment tumors into habitat subregions. Following habitat feature extraction and selection, six habitat models were constructed, including support vector machine (SVM), logistic regression, linear discriminant analysis (LDA), Gaussian process classifier, random forest, and multilayer perceptron (MLP). For comparison, a VI-RADS model and a clinical prediction model were also developed. SHapley Additive exPlanations (SHAP) analysis was used to interpret the optimal model and visualize its decision-making process. Furthermore, we conducted propensity score matching (PSM) analysis to evaluate the generalizability of the habitat models.</p><p><strong>Results: </strong>All habitat models exhibited good diagnostic performance. The SVM habitat model achieved the highest AUC in both the training set (AUC = 0.95, 95% CI: 0.85-1.00) and the external validation set (AUC = 0.82, 95% CI: 0.61-1.00). It showed a trend toward better performance compared to the clinical model (AUC = 0.68, 95% CI: 0.51-0.83) and the VI-RADS model (AUC = 0.75, 95% CI: 0.58-0.92) in both sets, although the differences did not reach statistical significance. After PSM balancing all baseline covariates (all SMD < 0.1), SVM still maintained stable predictive capacity (AUC = 0.72, sensitivity = 0.769) in matched external cohort, verifying good cross-center generalization.</p><p><strong>Conclusion: </strong>Our biparametric MRI-based habitat models, particularly the SVM classifier, showed a trend toward improved preoperative risk stratification of NMIBC. These findings require large multicenter prospective validation before clinical individualized treatment decisions and follow-up planning.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294305","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":"Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.","authors":"Huanhuan Xie, Xiting Peng, Xingzhi Chen, Yanting Yu, Zhang Yi, Wei Meng, Chencui Huang, Yi You, Fei Dong, Minming Zhang, Xiaojun Guan, Xiaojun Xu","doi":"10.1186/s12880-026-02632-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02632-5","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers.</p><p><strong>Methods: </strong>A retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training (n = 280) and test (n = 120) cohorts.</p><p><strong>Results: </strong>Spatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively).</p><p><strong>Conclusions: </strong>Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13718091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294268","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":"Clinical value of a CT radiomics model for predicting myocardial injury after cancer therapy.","authors":"Fei Zhao, Xiang Hu, Heyao Xu, Jingwen Liu, Weijia Li, Yuhang Wu, Kangning Liu, Cuixia Wen, Chong Zhou, Xiaojin Wu","doi":"10.1186/s12880-026-02608-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02608-5","url":null,"abstract":"<p><strong>Background: </strong>Early identification of patients at risk of cancer therapy-related myocardial injury remains challenging. Conventional surveillance mainly relies on echocardiographic indices and circulating biomarkers, which often indicate injury after myocardial damage has already occurred. This study aimed to determine whether left ventricular myocardial radiomics features extracted from pretreatment chest computed tomography could predict myocardial injury after cancer therapy.</p><p><strong>Methods: </strong>This single-center retrospective study included patients with malignant tumors who received anticancer therapy at Xuzhou Central Hospital between January 2020 and December 2025. A total of 129 patients with complete imaging and outcome data entered the radiomics modelling workflow. One patient with missing clinical covariates was excluded from baseline comparisons and logistic regression analyses, leaving 128 complete clinical cases. Left ventricular myocardial regions of interest were manually delineated on pretreatment chest computed tomography images. Radiomics features were screened using inter-observer reproducibility analysis, variance filtering, Spearman correlation analysis, and Elastic Net regularization. Clinical, radiomics, and combined models were developed and evaluated using receiver operating characteristic analysis, calibration assessment, the Hosmer-Lemeshow test, decision curve analysis, and five-fold cross-validation.</p><p><strong>Results: </strong>In the complete clinical cohort, alcohol use and hypertension differed significantly between patients with and without myocardial injury. In univariable logistic regression, alcohol use, hypertension, absolute neutrophil count, diabetes, and NLR met the candidate threshold for multivariable analysis. No clinical variable remained independently significant in multivariable logistic regression, although alcohol use showed a borderline association. Five radiomics features were retained in the final model. In the test set, the areas under the curve of the clinical, radiomics, and combined models were 0.601, 0.867, and 0.805, respectively. The radiomics model showed significantly better discrimination than the clinical model, whereas its difference from the combined model was not statistically significant. The radiomics model achieved a sensitivity of 0.692 and a specificity of 0.962 at the optimal cutoff. Calibration should be interpreted cautiously because the Hosmer-Lemeshow p value was close to the conventional significance threshold.</p><p><strong>Conclusions: </strong>A pretreatment chest computed tomography-based myocardial radiomics model showed preliminary potential for identifying patients at increased risk of myocardial injury after cancer therapy. This approach may support low-burden baseline cardiotoxicity risk assessment using imaging data already acquired during routine cancer care. Larger external cohorts are required for validation.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294346","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":"An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children.","authors":"Zhaopu Li, Yang Li, Zhongjing Zhang, Jiaqing Lin, Tao Xu, Wei Zhu, Zhaokun Guo, Kun Yang","doi":"10.1186/s12880-026-02628-1","DOIUrl":"https://doi.org/10.1186/s12880-026-02628-1","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative discrimination between uncomplicated and complicated acute appendicitis in children is critical for optimal surgical decision-making but remains a clinical challenge. Current reliance on conventional imaging and laboratory findings lacks sufficient precision, necessitating the development of more reliable predictive tools.</p><p><strong>Methods: </strong>We conducted a multicenter retrospective study to develop and validate a machine-learning model integrating CT radiomics and clinical variables. Data from 287 pediatric patients (aged 2-14 years) at Xiaogan Central Hospital (Jan 2017-May 2025) were randomly divided into a training (n = 201) and an internal-validation cohort (n = 86). An independent external-validation cohort comprised 164 patients from Yichang Central People's Hospital (Nov 2023-May 2025). Preoperative CT images and clinical data were processed; features with < 30% missing values were imputed. An automated modeling pipeline constructed 100 candidate models by combining 10 feature-selection algorithms with 10 machine-learning frameworks. Model performance was assessed using receiver operating characteristic curve analysis, with interpretability provided by SHapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>The optimal model, chi square feature selection combined with AdaBoost, demonstrated robust discriminative ability. It achieved AUCs of 0.900 (95% CI: 0.832-0.958), 0.889 (95% CI: 0.841-0.937), and 0.893 (95% CI: 0.855-0.931) in the training, internal-validation, and external-validation cohorts, respectively. The model achieved a sensitivity of 87% and specificity of 85% in the external-validation cohort. SHAP analysis identified radiomic texture features (RootMeanSquared, Strength, Skewness), appendicolith presence, and maximum body temperature as the most important predictors.</p><p><strong>Conclusion: </strong>A CT radiomics-based machine learning model integrating clinical features can accurately differentiate complicated from uncomplicated appendicitis in children, providing an interpretable framework to support preoperative surgical decision-making.</p><p><strong>Trial registration: </strong>Not applicable (retrospective diagnostic study).</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13718123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149294330","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":"Gliomas grading prediction using multicenter 3T Magnetic Resonance Spectroscopy.","authors":"Di Guo, Kaiwei Xie, Jiacheng Lin, Jianshu Chen, Jialue Zhang, Meijin Lin, Xiaobo Qu, Wanjun Hu, Jianhua Wang, Dongyun Liang","doi":"10.1186/s12880-026-02640-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02640-5","url":null,"abstract":"<p><strong>Background: </strong>Magnetic Resonance Spectroscopy (MRS) is a non-invasive imaging technique that detects various metabolites in brain tissues, making it essential for the grading of brain gliomas. MRS measures key metabolites such as N-acetyl-aspartate (NAA), choline (Cho), and creatine (Cr), whose ratio changes provide valuable information for glioma grading, guiding diagnosis, treatment, and prognosis. Current studies are largely single-center, with grading criteria differing across hospitals. This work aims to identify reliable and consistent metabolite ratios for glioma grading by jointly analyzing multicenter 3T single-voxel MRS of brain gliomas. The grading accuracy of these metabolite ratios is evaluated to establish a more standardized diagnostic basis.</p><p><strong>Materials and methods: </strong>We retrospectively included the MRS data from 60 glioma patients from 3 hospitals. MRS results included 4 metabolite ratios: NAA/Cr, Cho/NAA, Cho/Cr, and NAA/(Cho + Cr). The Shapiro-Wilk test was employed to ascertain the normality of the data, and the differences were calculated using either an independent t-test or a Mann-Whitney U-test. The classification performance was evaluated using ROC curve and accuracy analysis.</p><p><strong>Results: </strong>Across multicenter data, statistically significant differences were found in Cho/NAA and Cho/Cr ratios between low- and high-grade gliomas (P < 0.05). ROC analysis showed AUCs of 0.73 for Cho/NAA and 0.74 for Cho/Cr, indicating good classification performance. Validation results showed 75% accuracy, supporting their potential for clinical glioma grading. The optimal cut-off value trained from any two hospitals is validated at a third hospital, and the accuracies of Cho/Cr are 77.78%, 100%, and 65%, respectively.</p><p><strong>Conclusion: </strong>Multicenter data analysis confirmed the good classification accuracy of Cho/NAA and Cho/Cr in glioma grading, providing a reliable basis for clinical treatment and prognosis planning. This aligns with findings from other studies.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149284964","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":"Combined radiological approach for characterization of solid renal masses: a proposed strategy for integrating the RENAL nephrometry score into the clear cell likelihood score.","authors":"İlhan Hekimsoy, Aziz Anıl Tan, Banu Sarsık Kumbaracı, Ezgi Güler","doi":"10.1186/s12880-026-02631-6","DOIUrl":"10.1186/s12880-026-02631-6","url":null,"abstract":"<p><strong>Objective: </strong>To systematically evaluate whether the combined application of RENAL nephrometry and clear cell likelihood score (ccLS) could improve the diagnostic reliability of the ccLS in distinguishing clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>This retrospective single-center study encompassed 140 histopathologically confirmed solid renal masses in 130 patients who underwent preoperative MRI between March 2011 and January 2025. Two radiologists independently assigned the RENAL nephrometry and ccLS for each lesion. The diagnostic performance of the ccLS and the combined radiological approach was assessed using receiver operating characteristic curve analysis in the whole study cohort and two subgroups stratified by lesion diameter (≤ 4 cm vs. >4 cm). Interreader consistency was evaluated using the kappa coefficient.</p><p><strong>Results: </strong>Overall, 74 (52.9%) ccRCC and 66 (47.1%) non-ccRCC lesions were enrolled. Diagnostic performance analysis yielded a negative predictive value of 90.9%, a specificity of 30.3%, and an AUC of 0.638 for excluding ccRCC when lesions with a ccLS of 2 or lower were accepted as non-ccRCC. With the combined approach, considering low-complexity lesions per the nephrometry score with ccLS of 3 as non-ccRCC improved diagnostic performance using a ccLS cut-off of ≤ 2 for excluding ccRCC (AUC = 0.684, p = 0.022). The combined approach exhibited better performance in lesions ≤ 4 cm for excluding ccRCC among ccLS 3 lesions (p = 0.042). Interobserver consistency was found to be near-perfect (κ = 0.927) and substantial (κ = 0.70) for the nephrometry and ccLS, respectively.</p><p><strong>Conclusion: </strong>In this preliminary single-center cohort, the combined radiological approach was systematically evaluated for both predicting and excluding ccRCC. Its benefit was concentrated in excluding ccRCC among indeterminate ccLS 3 lesions ≤ 4 cm, reducing false-positive outcomes and enhancing diagnostic confidence and suggesting a role in risk stratification rather than detection; external validation is required before clinical adoption.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13640385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149103896","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":"Diagnostic accuracy of MRI for preoperative mapping of anal fistula: a prospective study of 204 patients with surgical correlation.","authors":"Minh Duc Pham, Thanh Thao Nguyen, Huu Tri Nguyen, Ngoc Trinh Thi Pham","doi":"10.1186/s12880-026-02616-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02616-5","url":null,"abstract":"<p><strong>Background: </strong>Anal fistula is a chronic abnormality of communication between the anal canal or rectum and the perianal skin. Accurate preoperative assessment is essential for effective operative planning, reducing fistula recurrence and preservation of anal sphincter function. Magnetic resonance imaging (MRI) is now a reliable tool for assessing fistula anatomy and complexity. This study aimed to classify anal fistulas using the Parks, St. James's University Hospital (SJUH), and Garg classification systems, and to evaluate the diagnostic accuracy of MRI compared with surgical findings.</p><p><strong>Methods: </strong>A prospective study was conducted on 204 patients with a clinical diagnosis of anal fistulas who underwent preoperative MRI and surgery at Hue Central Hospital and Hue University of Medicine and Pharmacy Hospital between October 2022 and December 2024. MRI findings of primary tracts and extensions were recorded. Diagnostic accuracy parameters were calculated using surgical findings as the gold standard.</p><p><strong>Results: </strong>Among 204 patients (185 men and 19 women), 219 primary fistulous tracts were identified, of which Parks type II (70.3%), SJUH grades IV (40.6%) and Garg grades IV (37.4%) were most frequent. Diagnostic accuracy was excellent for primary tract (97.5%), secondary tract (95.1%), abscess (92.6%), and horseshoe extension (98.0%). The agreement with surgical results was almost perfect for secondary tracts (κ = 0.828) and abscesses (κ = 0.825). MRI also maintained high diagnostic performance in recurrent fistulas, with overall accuracies ranging from 91.4% to 98.6%.</p><p><strong>Conclusion: </strong>MRI is a highly accurate and valuable imaging modality for anal fistula. It allows comprehensive classification and detailed mapping of primary tracts and extensions. MRI provides comprehensive preoperative mapping and may facilitate individualized surgical planning, particularly for complex or recurrent anal fistulas.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13711716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149285036","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}