BMC Medical Imaging最新文献

{"title":"Relative colon wall attenuation - distinguishing ischemia from other colon wall changes.","authors":"Jonas Oppenheimer, Sophia Lüken, Christian Schineis, Elena Sonnenberg, Hans Tepe, Ingo Steffen, Thomas Elgeti, Lars-Arne Schaafs","doi":"10.1186/s12880-026-02713-5","DOIUrl":"10.1186/s12880-026-02713-5","url":null,"abstract":"<p><strong>Background: </strong>Differentiation of bowel wall changes between ischemia and other causes in CT imaging can prove challenging, particularly in colon wall. Therefore, measurement of relative colon wall attenuation and contrast uptake were evaluated for aiding in diagnosis of colon ischemia.</p><p><strong>Methods: </strong>CTs with inquiry for bowel ischemia were retrospectively identified. Cases involving colon ischemia or colon wall changes of other etiologies were included. Three regions of interest (ROIs) were placed in representative areas of colon wall with and without evident changes, in arterial and venous phase images, respectively. Means of the ROIs were used to calculate the relative bowel enhancement between affected and unaffected wall in each contrast phase, as well as the contrast uptake of the wall between phases. Relative enhancement and contrast uptake were compared between patients with and without ischemia. A subset of cases was reanalyzed for intra- and inter-reader comparison.</p><p><strong>Results: </strong>31 patients with and 56 patients without ischemia were included. Relative enhancement of affected to unaffected colon wall was  -51.2% in arterial phase and  -44.6% in venous phase images for patients with ischemia. Relative enhancement was significantly higher in patients without ischemia at -0.5% in both phases (p < 0.001). There was no significant difference in contrast uptake of affected bowel (40.6% vs. 39.8% respectively, p = 0.67). ROC curves revealed a maximum Youden-Index at a cut-off of -37.4% (AUC: 0.818) in arterial phase to differentiate between groups. Intra-reader comparison showed a good, and inter-reader a moderate, correlation in a subset.</p><p><strong>Conclusions: </strong>Objective measurement of relative colon wall attenuation between affected and unaffected areas can be an additional imaging feature to aid in identifying ischemic colon.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787871","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 risk factors in persistent subsolid pulmonary nodules based on CT dynamic follow-up.","authors":"Fei Li, Jinlong Liu, Xiangyang Ren, Zihao Chen, Shenyu Yang, Yuanbo Ma, Yaman Li, Yufang Du, Xiaopeng Yang","doi":"10.1186/s12880-026-02694-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02694-5","url":null,"abstract":"<p><strong>Background: </strong>This study was designed to employ CT-based dynamic follow-up and semi-automated segmentation to investigate the growth rate of SSNs and identify risk factors for their progression.</p><p><strong>Methods: </strong>We retrospectively analyzed 665 SSN patients from the NLST and 176 from the First Affiliated Hospital of Zhengzhou University. Semi-automated segmentation software measured the mean diameter, volume, and overall mean CT attenuation (m-CTA) of all nodules. Nodule mass, diameter, volume, and mass doubling time were further calculated. Based on follow-up results, nodules were divided into cancer and non-cancer groups to compare growth rates and identify the optimal method for detecting malignancy-related growth. All SSNs were further divided into growth and non-growth groups based on selected measurements for comparison of clinical and radiological features. Independent predictors of SSN growth were evaluated through Kaplan-Meier analysis and Multivariate Cox regression modeling.</p><p><strong>Results: </strong>In both datasets, significant differences (p < 0.05) were identified in all quantitative parameters including diameter, volume and mass between the cancer and non-cancer groups. Volume and its doubling time demonstrated the smallest P-values (p < 0.001), indicating that volumetric assessment provides the most sensitive measurement for detecting tumor growth. Multivariable Cox regression analysis showed that age, pleural indentation, and initial m-CTA were independent risk factors for the growth of SSNs in both datasets (all p < 0.05), while nodule type was significantly associated with growth only in the NLST dataset (p < 0.001).</p><p><strong>Conclusions: </strong>Advanced age, pleural indentation sign, and a higher initial m-CTA attenuation are independent risk factors for the growth of SSNs.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787913","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":"Toward accessible quantification of bone mineral density: feasibility of radiographic bone aluminum equivalence (RBAE) determined by digital radiography against DEXA and CT in translational sheep model.","authors":"Muhammad M Kamal, Ahmed I Abdelgalil, Elham A Hassan","doi":"10.1186/s12880-026-02675-8","DOIUrl":"https://doi.org/10.1186/s12880-026-02675-8","url":null,"abstract":"<p><strong>Background: </strong>The study aimed to explore an accessible quantification of bone mineral density (BMD) through Radiographic Bone Aluminium Equivalence (RBAE) as determined by Digital Radiography (DR), compared to dual-energy X-ray absorptiometry (DEXA) and computed tomography (CT) in ex vivo animal model.</p><p><strong>Methods: </strong>Metacarpal and metatarsal bones (n = 24) from skeletally mature sheep were imaged using standard DR, DEXA, and CT protocols. DR-derived BMD values were calculated from calibrated greyscale intensities using an aluminum step-wedge and image-analysis workflow. Correlation and regression analyses were performed to evaluate the relationship between the different modalities, whereas the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used to assess agreement between the measurement methods.</p><p><strong>Results: </strong>DR-derived BMD as expressed as BRAE demonstrated strong linear correlation with DEXA (r = 0.779, p = 0.001) and moderate linear correlation with CT (r = 0.536, p = 0.04). Across all imaging modalities, metatarsal bones exhibited significantly higher BMD than metacarpal bones, with clear bilateral symmetry observed within each bone type. The consistency of results across modalities strengthens the validity of the findings and supports the reliability of each technique for detecting biologically meaningful differences in BMD. The agreement between DEXA, CT and digital radiography underscores the feasibility of using radiographic BMD to quantify BMD.</p><p><strong>Conclusion: </strong>Digital radiography, supported by standardized calibration and analysis, yields a reproducible BMD estimates that align closely with DEXA and CT measurements. These findings highlight DR as a cost-effective and accessible option for BMD assessment, while providing valuable baseline insight into normal mineral distribution. This information supports future research on pathological bone loss, imaging validation, and therapeutic strategies to improve bone quality.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787884","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":"Morphometric characteristics of the prefemoral fat pad of the knee on MRI: a study in a healthy population.","authors":"Christoph Hellmund, Pierre Hepp, Hanno Steinke, Leon Weigelt, Timm Denecke, Jeanette Henkelmann","doi":"10.1186/s12880-026-02663-y","DOIUrl":"10.1186/s12880-026-02663-y","url":null,"abstract":"<p><strong>Background: </strong>The prefemoral fat pad (PFP) is located anterior to the distal femur and adjacent to the patellofemoral joint. While the infrapatellar fat pad (IFP) has been extensively studied, morphometric data on the PFP are scarce. The purpose of this study was to characterize the morphology of the PFP on MRI and to evaluate its relationship to adjacent osseous and soft tissue structures.</p><p><strong>Materials and methods: </strong>Twenty healthy volunteers were prospectively enrolled, and MRI examinations of 38 knees from 19 participants were analysed. Volumes of the PFP, IFP, and suprapatellar fat pad (SFP) were measured using manual segmentation. Additional morphometric parameters included body height, weight, BMI, age, numerous femoral and tibial osseous dimensions, the Insall-Salvati ratio, and the suprapatellar axial PFP area (SAPA). Correlation and regression analyses were performed to identify predictors and surrogate measures of PFP volume.</p><p><strong>Results: </strong>Mean PFP and IFP volume were comparable. PFP volume showed significant correlations with selected osseous parameters of the distal femur and proximal tibia. IFP volume and SAPA demonstrated a strong correlation with PFP volume and emerged as surrogate parameters. No significant relationship was found between PFP volume and patellar height as assessed by the Insall-Salvati ratio.</p><p><strong>Conclusions: </strong>In healthy adults, the PFP is similar in size to the IFP and shows consistent morphometric relationships with adjacent structures. SAPA may represent a simplified, time-efficient candidate surrogate measure that showed a strong association with total PFP volume in this healthy cohort; further validation in larger and symptomatic populations is required before it can substitute for complete volumetric segmentation in clinical practice. These findings provide baseline morphometric data for future studies investigating pathological alterations of the PFP.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13471311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148720073","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":"AI-based multimodal fusion for preoperative prediction of breast microcalcifications: combining mammography and tomosynthesis.","authors":"Junting Wei, Jie Li, Ruixin Pan, Mingna Cao, Chengcheng Ma, Zongyu Xie, Yichuan Ma, Zhizhen Gao","doi":"10.1186/s12880-026-02673-w","DOIUrl":"10.1186/s12880-026-02673-w","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop artificial intelligence (AI) models based on mammography (MG) and digital breast tomosynthesis (DBT) for non-invasive preoperative differentiation of benign and malignant breast microcalcifications.</p><p><strong>Methods: </strong>We retrospectively analyzed 190 patients with pathologically confirmed breast microcalcifications, who were divided into training and test sets at a ratio of 7:3. Radiomics features and deep learning scores extracted using ResNet-50 were derived from MG and DBT images. After feature selection procedures, single-modality models and an integrated model (MD model) were constructed.</p><p><strong>Results: </strong>The area under the curves (AUCs) of the BI-RADS model, DBT model, MG model, and MD model in the training set were 0.805, 0.879, 0.896, and 0.939. In the test set, the corresponding AUCs were 0.809, 0.768, 0.793, and 0.835, respectively. The MD model demonstrated significantly better diagnostic performance compared with the other models (DeLong test, P < 0.05). SHapley Additive exPlanations (SHAP) analysis revealed that the deep learning scores (DL scores) from the MG craniocaudal (CC) view and radiomics features reflecting textural complexity and spatial heterogeneity were the most influential features in model prediction.</p><p><strong>Conclusions: </strong>The MD model integrating radiomics and deep learning scores from MG and DBT improved the diagnostic accuracy for differentiating benign and malignant breast microcalcifications, outperforming traditional methods. It provided a valuable non-invasive preoperative tool with the potential to optimize clinical decision-making and reduce unnecessary invasive procedures.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811937","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":"Optimal timing of post-therapeutic [¹³¹I]NaI whole-body scintigraphy in predominantly low- and intermediate-risk differentiated thyroid cancer: a secondary analysis of prospectively acquired data and a structured literature review.","authors":"Andreas Pfestroff, Hannah Wolfram, Kathrin Pfestroff, Wadim Bowl, Ali Ebrahimifard, Shamim Bagheri, Pietro di Fazio, Behrooz H Yousefi, Elisabeth Maurer, Katharina Holzer, Glenn Flux, Jan Taprogge, Markus Luster, Friederike Eilsberger","doi":"10.1186/s12880-026-02647-y","DOIUrl":"10.1186/s12880-026-02647-y","url":null,"abstract":"<p><strong>Background: </strong>Post-therapeutic [¹³¹I]NaI whole-body scintigraphy (ptWBS) is mandatory after radioiodine therapy (RAIT), but the most suitable acquisition time remains uncertain. We compared image interpretability at early and delayed time points in patients with differentiated thyroid cancer (DTC).</p><p><strong>Methods: </strong>This retrospective secondary analysis included prospectively acquired ptWBS data from 35 patients. Scans obtained at 6, 24, 48, 72, and 168 h after RAIT were independently ranked by three nuclear medicine physicians and evaluated with a 10-item criteria-based image-quality score. Rankings were analyzed using a Plackett-Luce model, and criteria-based scores were analyzed using generalized estimating equations.</p><p><strong>Results: </strong>The analysis comprised 167 scans, 501 criteria-based ratings, and 500 intuitive rank observations. The 24-h acquisition had the highest estimated preference, followed by 48, 6, 72, and 168 h. After Holm adjustment, 24 h was favored over 6, 72, and 168 h, whereas the difference between 24 and 48 h was not significant (P = 0.091). The criteria-based score was highest at 24 h (39.52; 95% CI, 38.62-40.43) and next-highest at 48 h (34.79); all pairwise differences in scores were significant. Each 5-point increase in the criteria-based score was associated with higher odds of a more favorable intuitive ranking (OR, 2.01; 95% CI, 1.70-2.38).</p><p><strong>Conclusions: </strong>PtWBS acquired at 24 h provided the best overall interpretability of the images. A 48 h scan may be considered when practical or radiation-protection considerations favor later imaging, but equivalence to 24 h has not been established. Additional delayed imaging may still be useful when distant metastatic disease is suspected.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148700775","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 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":"Preoperative tumor cerebral blood flow measured by arterial spin labeling predicts enhancing tumor volume progression in gliomas: a single-center retrospective study.","authors":"Thomas Lindner, Jens Fiehler","doi":"10.1186/s12880-026-02612-9","DOIUrl":"10.1186/s12880-026-02612-9","url":null,"abstract":"<p><strong>Background: </strong>Arterial spin labeling (ASL) enables non-invasive quantification of cerebral blood flow (CBF). The preoperative prognostic value of tumor-specific CBF for subsequent enhancing tumor (ET) volume progression in gliomas remains poorly characterized.</p><p><strong>Methods: </strong>In this study 60 glioma patients (predominantly glioblastoma, WHO grade 4) with preoperative 3T ASL MRI and paired follow-up structural MRI were analyzed. Tumor CBF, surrounding CBF, and whole-brain CBF were quantified from the segmented regions of interest. ET and surrounding non-enhancing FLAIR-hyperintense (SNFH) volume changes were calculated and correlated with preoperative perfusion data. Spearman correlations, Kruskal-Wallis tests, multivariable linear and logistic regression, and ROC analyses were performed.</p><p><strong>Results: </strong>Higher preoperative tumor CBF strongly predicted net ET volume increase (Spearman r = 0.531, p < 0.0001) and ET progression category (Kruskal-Wallis p = 0.0005). Mean tumor CBF was 147.7 ml/100 g/min in ET-increased versus 124.4 ml/100 g/min in unchanged and 115.8 ml/100 g/min in decreased cases. Tumor CBF remained an independent predictor after adjusting for age, scan interval, and MGMT status (linear regression p = 0.002; logistic regression OR = 2.02 per ml/100 g/min, p = 0.001). Associations with SNFH volume changes were weak and non-significant. Surrounding CBF closely matched whole-brain CBF (Pearson r ≈ 1.00). Tumor CBF alone yielded 5-fold CV AUC = 0.805 for predicting ET progression; the combined model (tumor CBF + ET volume) improved to AUC = 0.858.</p><p><strong>Conclusion: </strong>Preoperative ASL-derived tumor CBF appears to be an independent non-contrast imaging biomarker for early enhancing tumor progression risk in gliomas.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618008","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":"Diagnosis of tympanic effusions using hyperspectral imaging to prevent unnecessary paracentesis procedures.","authors":"Maximilian Gänzle, Sabine Löffler, Denis Gholami Bajestani, Michael Unger, Andreas Dietz, Andreas Melzer, Markus Pirlich, Hannes Köhler","doi":"10.1186/s12880-026-02611-w","DOIUrl":"10.1186/s12880-026-02611-w","url":null,"abstract":"<p><strong>Background: </strong>Otitis Media with Effusion (OME) is a prevalent childhood condition, affecting up to 90% of children with 10% progressing to a chronic state. It is commonly treated by tympanostomy tube placement. Diagnosis relies on clinical history, otoscopy, audiometry, and tympanometry, but these methods have a high false-positive rate of 15-28%. This highlights the need for an objective, reliable, and non-invasive diagnostic tool to reduce unnecessary surgeries.</p><p><strong>Methods: </strong>To evaluate the rate of false-positive OME diagnoses in our tertiary hospital, a retrospective database analysis was conducted. To assess the feasibility of advanced imaging, experimental studies were performed ex vivo on human body donors. Sodium chloride solution was intratympanically injected under endoscopic guidance. Hyperspectral imaging (HSI) datasets were acquired using a customized endoscopic system to quantify tissue water content.</p><p><strong>Results: </strong>An analysis of hospital records identified a false positive rate of 29% (n = 4,564) for OME diagnosis, comparing the number of paracenteses to paracenteses plus tympanostomy tubes. In the ex vivo analysis of HSI, visual evaluation of the Tissue Water Index (TWI) demonstrated sensitivity, specificity, and accuracy of 89%, 78%, and 83%, respectively. The mean TWI of all evaluated tympanic membranes was 50.2 (± 7.1) before injection (control) and increased to 62.8 (± 14.8) after injection.</p><p><strong>Conclusion: </strong>HSI presents a highly sensitive and user-friendly potential future diagnostic adjunct for diagnosing OME, which has shown its potential in an ex vivo approach. Now it needs in-vivo confirmation of the results as its implementation in clinical routine could contribute to avoiding unnecessary surgical interventions, minimizing patient burden and healthcare costs.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"26 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13422311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618030","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}