BMC Medical Imaging最新文献

{"title":"Diffusion weighted imaging-based differentiation of arteritic and non-arteritic anterior ischemic optic neuropathy.","authors":"Charlotte Pietrock, Theresia Knoche, Sophia Meidinger, Victor Wenzel, Konrad Neumann, Eberhard Siebert, Leon Alexander Danyel","doi":"10.1186/s12880-025-01780-4","DOIUrl":"10.1186/s12880-025-01780-4","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the utility of DWI-MRI to differentiate arteritic (A-AION) from non-arteritic (NA-AION) ischemic optic neuropathy.</p><p><strong>Methods: </strong>This bicentric cohort-study evaluated 3T DWI-MRI scans performed within 10 days after onset of AION in patients treated between 2014 and 2024 at two tertiary care centers. DWI was first assessed for the presence of restricted diffusion within the optic nerve. Quantitative apparent diffusion coefficient (ADC) evaluation was performed by placing a region of interest (ROI) within the affected optic nerve. Qualitative and quantitative DWI assessments were compared between A-AION and NA-AION patients.</p><p><strong>Results: </strong>Twenty A-AION patients (75.7 ± 6.8 years; 16 [80.0%] female) and 59 NA-AION patients (64.6 ± 10.7 years; 22 [37.3%] female) with a total of 82 (A-AION: 23; NA-AION: 59) DWI-MRI scans were included in the study. Restricted diffusion on ADC was significantly more frequent in A-AION, when compared to NA-AION (82.6% vs. 42.4%; p = 0.001). Corresponding sensitivity, specificity, positive and negative predictive value of qualitative ADC assessment for the identification of A-AION were 0.83, 0.58, 0.43 and 0.89. Quantitative ADC analysis revealed significantly lower values in optic nerves affected by A-AION (ADC: 448.0 ± 256.2 × 10^- 6 mm²/s vs. 671.5 ± 174.9 × 10^- 6 mm²/s, p = 0.002).</p><p><strong>Conclusion: </strong>Restricted diffusion of the optic nerve is more frequent in A-AION and associated with lower optic nerve ADC values, when compared to NA-AION. Prospective studies are required to further explore the potential of DWI in discerning arteritic from non-arteritic AION.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"208"},"PeriodicalIF":2.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332423","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":"Association between age and lung cancer risk: evidence from lung lobar radiomics.","authors":"Yuwei Li, Chengting Lin, Lei Cui, Chao Huang, Liting Shi, Shiyang Huang, Yue Yu, Xianglan Zhou, Qian Zhou, Kun Chen, Lei Shi","doi":"10.1186/s12880-025-01747-5","DOIUrl":"10.1186/s12880-025-01747-5","url":null,"abstract":"<p><strong>Background: </strong>Previous studies have highlighted the prominent role of age in lung cancer risk, with signs of lung aging visible in computed tomography (CT) imaging. This study aims to characterize lung aging using quantitative radiomic features extracted from five delineated lung lobes and explore how age contributes to lung cancer development through these features.</p><p><strong>Methods: </strong>We analyzed baseline CT scans from the Wenling lung cancer screening cohort, consisting of 29,810 participants. Deep learning-based segmentation method was used to delineate lung lobes. A total of 1,470 features were extracted from each lobe. The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. Multiple regression analyses were used to adjust for confounders in the association of age, lung lobar radiomic features, and lung cancer. Linear, Cox proportional hazards, and parametric accelerated failure time models were applied as appropriate. Mediation analyses were conducted to evaluate whether lobar radiomic features mediate the relationship between age and lung cancer risk.</p><p><strong>Results: </strong>Age was significantly associated with an increased lung cancer risk, particularly among current smokers (hazard ratio = 1.07, P = 2.81 × 10^- 13). Age-related radiomic features exhibited distinct effects across lung lobes. Specifically, the first order mean (mean attenuation value) filtered by wavelet in the right upper lobe increased with age (β = 0.019, P = 2.41 × 10^- 276), whereas it decreased in the right lower lobe (β = -0.028, P = 7.83 × 10^- 277). Three features, namely wavelet_HL_firstorder_Mean of the right upper lobe, wavelet_LH_firstorder_Mean of the right lower lobe, and original_shape_MinorAxisLength of the left upper lobe, were independently associated with lung cancer risk at Bonferroni-adjusted P value. Mediation analyses revealed that density and shape features partially mediated the relationship between age and lung cancer risk while a suppression effect was observed in the wavelet first order mean of right upper lobe.</p><p><strong>Conclusions: </strong>The study reveals lobe-specific heterogeneity in lung aging patterns through radiomics and their associations with lung cancer risk. These findings may contribute to identify new approaches for early intervention in lung cancer related to aging.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"204"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233088","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":"Investigation of the correlation between radiomorphometric indices in cone-beam computed tomography images and dual X-ray absorptiometry bone density test results in postmenopausal women.","authors":"Sara Rafieizadeh, Sima Lari, Mohammad Mahdi Maleki, Abbas Shokri, Leili Tapak","doi":"10.1186/s12880-025-01739-5","DOIUrl":"10.1186/s12880-025-01739-5","url":null,"abstract":"<p><strong>Objective: </strong>Osteoporosis is a prevalent skeletal disorder characterized by reduced bone mineral density (BMD) and structural deterioration, resulting in increased fracture risk. Early diagnosis is crucial to prevent fractures and improve patient outcomes. This study investigates the diagnostic utility of morphometric and cortical indices derived from cone-beam computed tomography (CBCT) for identifying osteoporotic postmenopausal women who were candidates for dental implant therapy, with dual-energy X-ray absorptiometry (DXA) used as the reference standard.</p><p><strong>Materials and methods: </strong>This cross-sectional study included 71 postmenopausal women, aged 50-79 years, who underwent CBCT imaging at the Oral and Maxillofacial Radiology Department of Hamadan University of Medical Sciences between 2022 and 2024. Participants with systemic conditions affecting bone metabolism were excluded. The morphometric indices-Computed Tomography Mandibular Index (CTMI), Computed Tomography Index Superior (CTI(S)), Computed Tomography Index Inferior (CTI(I)), and Computed Tomography Cortical Index (CTCI)-were measured at the mental foramen and antegonial regions using OnDemand3D Dental software. Bone mineral density (BMD) was assessed by DXA scans of the lumbar spine and femoral neck. In addition to traditional statistical analyses (Pearson's correlation and one-way ANOVA with LSD test), a multilayer perceptron (MLP) neural network model was employed to evaluate the diagnostic power of CBCT indices.</p><p><strong>Results: </strong>DXA results based on the femoral neck T-scores categorized 38 patients as normal, 32 as osteopenic, and one as osteoporotic, while lumbar spine T-scores identified 38 normal, 22 osteopenic, and 11 osteoporotic patients. Significant differences (p < 0.05) were observed in most CBCT-derived indices, with the CTMI index demonstrating the most marked variation, especially between normal and osteoporotic groups (p < 0.001). Moreover, significant positive correlations were found between the CBCT indices and DXA T-scores across the lumbar spine, femoral neck, and total hip regions. The neural network model achieved an overall diagnostic accuracy of 75%, with the highest predictive importance attributed to antegonial CTCI and CTMI indices.</p><p><strong>Conclusion: </strong>This study highlights the significant correlation between CBCT-derived radiomorphometric indices such as CTMI, CTI(S), CTI(I), and CTCI at the mental foramen and antegonial regions and bone mineral density (BMD) in postmenopausal women. CBCT, particularly the CTMI index in the antegonial region, offers a cost-effective, non-invasive method for early osteoporosis detection, providing a valuable alternative to traditional screening methods.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"203"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233090","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":"StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.","authors":"Evren Ekingen, Ferhat Yildirim, Ozgur Bayar, Erhan Akbal, Ilknur Sercek, Abdul Hafeez-Baig, Sengul Dogan, Turker Tuncer","doi":"10.1186/s12880-025-01721-1","DOIUrl":"10.1186/s12880-025-01721-1","url":null,"abstract":"<p><strong>Background and objective: </strong>Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightweight convolutional neural network (CNN) for computed tomography (CT) and magnetic resonance (MR) scans, and couples it with deep feature engineering (DFE) to improve accuracy and facilitate clinical deployment.</p><p><strong>Materials and methods: </strong>We assembled a multimodal dataset of CT and MR images, each labeled as stroke or control. StrokeNeXt employs a ConvNeXt‑inspired block and a squeeze‑and‑excitation (SE) unit across four stages: stem, StrokeNeXt block, downsampling, and output. In the DFE pipeline, StrokeNeXt extracts features from fixed‑size patches, iterative neighborhood component analysis (INCA) selects the top features, and a t algorithm-based k-nearest neighbors (tkNN) classifier has been utilized for classification.</p><p><strong>Results: </strong>StrokeNeXt achieved 93.67% test accuracy on the assembled dataset. Integrating DFE raised accuracy to 97.06%. This combined approach outperformed StrokeNeXt alone and reduced classification time.</p><p><strong>Conclusion: </strong>StrokeNeXt paired with DFE offers an effective solution for stroke detection on CT and MR images. Its high accuracy and fewer learnable parameters make it lightweight and it is suitable for integration into clinical workflows. This research lays a foundation for real‑time decision support in emergency and radiology settings.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"205"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12142900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233100","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":"Development and validation of a CT algorithm based on intratumoral necrosis and tumor morphology to predict the nuclear grade of small (2-4 cm) solid clear cell renal cell carcinoma.","authors":"Jianyi Qu, Pingyi Zhu, Xianli Zhu, Xinyan Li, Wenjie Zhang, Xinhong Song, Xiaofei Wang, Chenchen Dai, Qianqian Zhang, Jianjun Zhou","doi":"10.1186/s12880-025-01741-x","DOIUrl":"10.1186/s12880-025-01741-x","url":null,"abstract":"<p><strong>Background: </strong>Preoperative non-invasive prediction of the World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grade of small clear cell renal cell carcinoma (ccRCC) can aid in decision making for active surveillance. The study aimed to develop and validate a CT algorithm for the prediction of the WHO/ISUP nuclear grade of small (2-4 cm) solid ccRCC.</p><p><strong>Methods: </strong>A total of 233 patients with 233 ccRCCs (50 high-grade [WHO/ISUP grades 3-4] and 183 low-grade [WHO/ISUP grades 1-2]) in the initial cohort were enrolled in this study. The tumor necrosis (presence of necrosis, proportion of necrosis, and tumor necrosis score [TNS]) and tumor morphology (five grades) were retrospectively evaluated using contrast-enhanced CT. A four-tiered CT score based on TNS and shape irregularity score (SIS) was constructed using logistic regression and receiver operating characteristic (ROC) curve analyses. The effectiveness of the four-tiered CT score was confirmed through an external validation cohort (218 ccRCCs from 218 patients, including 42 high-grade and 176 low-grade).</p><p><strong>Results: </strong>The TNS and tumor morphologies significantly differed between high-grade and low-grade ccRCCs (both P < 0.001). For diagnosis of high-grade ccRCC, the TNS and SIS achieved the area under the ROC curve (AUC) values of 0.697 and 0.731, respectively. The four-tiered CT score had an interobserver agreement of 0.677 (Cohen kappa), and achieved the AUC values of 0.793 and 0.781 in the initial and validation cohorts, respectively. The CT score of ≥ 3 exhibited a sensitivity of 54.00% and 54.76% in the initial and validation cohorts, respectively, with corresponding specificity of 90.16% and 88.07%, accuracy of 82.40% and 81.65%, positive predictive value of 60.00% and 52.27%, and negative predictive value (NPV) of 87.77% and 89.08%.</p><p><strong>Conclusions: </strong>The TNS based on the number and size of necrotic foci could help diagnose high-grade ccRCC. The developed CT score algorithm achieved moderate AUC and high NPV for the diagnosis of high-grade ccRCC, which might facilitate active surveillance for ccRCC with a diameter of 2-4 cm.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"207"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233089","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":"Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.","authors":"Zhi-Li Li, Hong-Yu Yang, Xiao-Xiao Lv, Ya-Kun Zhang, Xin-Yu Zhu, Yu-Rou Zhang, Li Guo","doi":"10.1186/s12880-025-01697-y","DOIUrl":"10.1186/s12880-025-01697-y","url":null,"abstract":"<p><strong>Background: </strong>The study explores the value of a model constructed by integrating CTA-based carotid plaque radiomic features, clinical risk factors, and plaque imaging characteristics for prognosticating the risk of ischemic stroke.</p><p><strong>Methods: </strong>Data from 123 patients with carotid atherosclerosis were analyzed and divided into stroke and asymptomatic groups based on DWI findings. Clinical information was collected, and plaque imaging characteristics were assessed to construct a traditional model. Radiomic features of carotid plaques were extracted using 3D-Slicer software to build a radiomics model. Logistic regression was applied in the training set to establish the traditional model, the radiomics model, and a combined model, which were then tested in the validation set. The prognostic ability of the three models for ischemic stroke was evaluated using ROC curves, while calibration curves, decision curve analysis, and clinical impact curves were used to assess the clinical utility of the models. Differences in AUC values between models were compared using the DeLong test.</p><p><strong>Results: </strong>Hypertension, diabetes, elevated homocysteine (Hcy) concentrations, and plaque burden are independent risk factors for ischemic stroke and were used to establish the traditional model. Through Lasso regression, nine optimal features were selected to construct the radiomics model. ROC curve analysis showed that the AUC values of the three Logistic regression models were 0.766, 0.766, and 0.878 in the training set, and 0.798, 0.801, and 0.847 in the validation set. Calibration curves and decision curve analysis showed that the radiomics model and the combined model had higher accuracy and better fit in prognosticating the risk of ischemic stroke.</p><p><strong>Conclusions: </strong>The radiomics model is slightly better than the traditional model in evaluating the risk of ischemic stroke, while the combined model has the best prognostic performance.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"206"},"PeriodicalIF":2.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12142908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233099","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":"Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer.","authors":"Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang","doi":"10.1186/s12880-025-01686-1","DOIUrl":"https://doi.org/10.1186/s12880-025-01686-1","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clinical decision-making.</p><p><strong>Methods: </strong>We included a total of 356 NSCLC patients at pN0-pN2 stage, divided into training (n = 207), internal test (n = 90), and independent test (n = 59) sets. Station 4 mediastinal lymph nodes (LNs) regions of interest (ROIs) were semi-automatically segmented on venous-phase computed tomography (CT) images for radiomics feature extraction. Using least absolute shrinkage and selection operator (LASSO) regression to select features with non-zero coefficients. Four machine learning algorithms-decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)-were employed to construct radiomics models. Clinical predictors were identified through univariate and multivariate logistic regression, which were subsequently integrated with radiomics features to develop combined models. Models performance were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and DeLong's test.</p><p><strong>Results: </strong>Out of 1721 radiomics features, eight radiomics features were selected using LASSO regression. The RF-based combined model exhibited the strongest discriminative power, with an area under the curve (AUC) of 0.934 for the training set and 0.889 for the internal test set. The calibration curve and DCA further indicated the superior performance of the combined model based on RF. The independent test set further verified the model's robustness.</p><p><strong>Conclusions: </strong>The combined model based on RF, integrating radiomics and clinical features, effectively and non-invasively identifies metastasis to the station 4 mediastinal LNs in NSCLC patients at pN0-pN2 stage. This model serves as an effective auxiliary tool for clinical decision-making and has the potential to optimize treatment strategies and improve prognostic assessment for pN0-pN2 patients.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"202"},"PeriodicalIF":2.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224152","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":"Multimodality comparison of aorta morphology in patients with aortopathy: 4D flow CMR, CTA, mDIXON.","authors":"Bastiaan J C Te Kiefte, Faeze Gholamiankhah, Joe F Juffermans, Pieter Van Den Boogaard, Arthur J H A Scholte, Hildo J Lamb, Jos J M Westenberg","doi":"10.1186/s12880-025-01734-w","DOIUrl":"10.1186/s12880-025-01734-w","url":null,"abstract":"<p><strong>Background: </strong>Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) enables analysing of aortic blood flow dynamics. In order to examine the relationship between morphology and hemodynamics, additional anatomical imaging is required. This study aims to assess if 4D flow CMR segmentations can be used to determine morphological parameters by comparing with segmentations from Computed Tomography Angiography (CTA) and mDIXON CMR.</p><p><strong>Methods: </strong>This study included 18 patients with various aortic pathologies who underwent CTA and CMR (including mDIXON and 4D flow CMR sequences) of the thoracic aorta. The aortic lumen was segmented from aortic valve to the descending aorta and divided into four anatomical segments: aortic root [AoR], ascending aorta [AAo], aortic arch [AA], and descending aorta [DA]. We compared morphological parameters (maximum diameter, volume, and centreline length) using these different scanning techniques. Segmentations were performed at different cardiac phases: peak systole for CTA and 4D flow CMR, and end-diastole for mDIXON.</p><p><strong>Results: </strong>Intraclass Correlation Coefficients (ICCs) and Bland-Altman plots were determined for all modalities and all segments. Agreement between 4D flow CMR and CTA was good to very good for maximum diameter (ICC 0.70-0.85) and centreline length (ICC 0.74-0.90), and very good to excellent for volume (ICC 0.89-0.97). Between mDIXON and CTA very good for maximum diameter (0.89-0.94), good to very good for centreline length (0.78-0.88), and very good to excellent for volume (0.87-0.96). Similar results were found when comparing 4D flow CMR with mDIXON with ICCs for maximum diameter (0.68-0.84), volume (0.91-0.97), and centreline length (0.78-0.90). Statistically significant differences were observed only for maximum diameter in AAo between CTA and mDIXON (p < 0.001), and for volume in AA between CTA and 4D flow CMR (p < 0.001). No significant differences were observed for other segments and parameters.</p><p><strong>Conclusions: </strong>Morphologic parameters derived from 4D flow CMR segmentations of the thoracic aorta demonstrate high levels of agreement when compared to segmentations based on CTA and mDIXON, in this relatively small cohort of patients with diverse aortic pathologies. This finding could be of interest for future 4D flow CMR research, as it possibly allows for the evaluation of both morphology and hemodynamics in a single imaging acquisition. Further research in larger cohorts is needed to robustly validate 4D flow CMR as a single-modality imaging technique.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"201"},"PeriodicalIF":2.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214833","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":"Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.","authors":"Jinhua Wang, Zhenchen Zhu, Zhengsong Pan, Weixiong Tan, Wei Han, Zhen Zhou, Ge Hu, Zhuangfei Ma, Yinghao Xu, Zhoumeng Ying, Xin Sui, Zhengyu Jin, Lan Song, Wei Song","doi":"10.1186/s12880-025-01746-6","DOIUrl":"10.1186/s12880-025-01746-6","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT).</p><p><strong>Materials and methods: </strong>Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning-based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images.</p><p><strong>Results: </strong>Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0% and 83.4%, respectively (P < 0.001). The absolute percentage error in nodule volume from that of SDCT was 19.5% in ULDCT HIR-Str versus 17.9% in ULDCT DLR-Str (P < 0.001).</p><p><strong>Conclusion: </strong>Compared with HIR, DLR reduced image noise, increased nodule detection rate, and improved measurement accuracy of nodule volume at chest ULDCT.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"200"},"PeriodicalIF":2.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186451","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":"Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets.","authors":"Julian Marcon, Philipp Weinhold, Mona Rzany, Matthias P Fabritius, Michael Winkelmann, Alexander Buchner, Lennert Eismann, Jan-Friedrich Jokisch, Jozefina Casuscelli, Gerald B Schulz, Thomas Knösel, Michael Ingrisch, Jens Ricke, Christian G Stief, Severin Rodler, Philipp M Kazmierczak","doi":"10.1186/s12880-025-01727-9","DOIUrl":"10.1186/s12880-025-01727-9","url":null,"abstract":"<p><strong>Background: </strong>To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention.</p><p><strong>Methods: </strong>Preoperative computed tomography venous-phase datasets from patients that underwent procedures for histopathologically confirmed UTUC or RCC were retrospectively analyzed. Tumor segmentation was performed manually, and radiomic features were extracted according to the International Image Biomarker Standardization Initiative. Features were normalized using z-scores, and a predictive model was developed using the least absolute shrinkage and selection operator (LASSO). The dataset was split into a training cohort (70%) and a test cohort (30%).</p><p><strong>Results: </strong>A total of 236 patients [30.5% female, median age 70.5 years (IQR: 59.5-77), median tumor size 5.8 cm (range: 4.1-8.2 cm)] were included. For differentiating UTUC from RCC, the model achieved a sensitivity of 88.4% and specificity of 81% (AUC: 0.93, radiomics score cutoff: 0.467) in the training cohort. In the validation cohort, the sensitivity was 80.6% and specificity 80% (AUC: 0.87, radiomics score cutoff: 0.601). Subgroup analysis of the validation cohort demonstrated robust performance, particularly in distinguishing clear cell RCC from high-grade UTUC (sensitivity: 84%, specificity: 73.1%, AUC: 0.84) and high-grade from low-grade UTUC (sensitivity: 57.7%, specificity: 88.9%, AUC: 0.68). Limitations include the need for independent validation in future randomized controlled trials (RCTs).</p><p><strong>Conclusions: </strong>Machine learning-based radiomics models can reliably differentiate between RCC and UTUC in preoperative CT imaging. With a suggested performance benefit compared to conventional imaging, this technology might be added to the current preoperative diagnostic workflow.</p><p><strong>Clinical trial number: </strong>Local ethics committee no. 20-179.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"196"},"PeriodicalIF":2.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186455","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}