BMC Medical Imaging最新文献

{"title":"Assessment of right ventricle-to-pulmonary artery coupling by three-dimensional echocardiography in pre-capillary pulmonary hypertension: comparison with tricuspid annular plane systolic excursion /systolic pulmonary artery pressure ratio.","authors":"Guangjie Lv, Aili Li, Yanan Zhai, Lei Li, Mei Deng, Jieping Lei, Xincao Tao, Qian Gao, Wanmu Xie, Zhenguo Zhai","doi":"10.1186/s12880-025-01650-z","DOIUrl":"10.1186/s12880-025-01650-z","url":null,"abstract":"<p><strong>Background: </strong>The tricuspid annular plane systolic excursion/systolic pulmonary artery pressure ratio (TAPSE/sPAP) has limitations in evaluating right ventricle-to-pulmonary artery (RV-PA) coupling, particularly when pulmonary artery pressure cannot be accurately estimated by tricuspid regurgitation or when TAPSE cannot accurately reflect right ventricular systolic function in certain scenarios. Therefore, this study aimed to explore the value of three-dimensional echocardiography (3DE) coupling parameters in assessing RV-PA coupling in patients with pre-capillary pulmonary hypertension (PH).</p><p><strong>Methods: </strong>Fifty-nine patients with pre-capillary PH were retrospectively recruited. The surrogate \"gold standard\" of RV-PA coupling was derived from right heart catheterization (RHC) and cardiac magnetic resonance imaging (CMR). The relationships between echocardiographic RV-PA coupling parameters and RHC-CMR coupling standard were analyzed by Pearson's test and Bland‒Altman test. Additionally, 24 chronic thromboembolic pulmonary hypertension (CTEPH) patients were enrolled to explore the changes in echocardiographic RV-PA coupling parameters before and after PEA. Multivariate ordinal regression analysis was performed to identify echocardiographic parameters associated with prognostic risk stratification in pre-capillary PH patients.</p><p><strong>Results: </strong>3DE coupling parameters demonstrated strong correlation and good agreement with the RHC-CMR coupling standard. In contrast, TAPSE/sPAP was moderately correlated to the RHC-CMR coupling standard, but showed poor consistency, with a significant bias of 0.44 (95% CI: 0.374, 0.511). Before and after PEA, stroke volume/end-systolic volume (SV/ESV) derived by 3DE remained moderately correlated with pulmonary vascular resistance (PVR) and mean pulmonary artery pressure (mPAP) (r =-0.614, -0.655, P < 0.001), whereas TAPSE/sPAP was only associated with PVR and mPAP in CTEPH patients before PEA (r=-0.605, -0.758, P < 0.001). Multivariate regression analysis revealed TAPSE/sPAP as the strongest predictor of prognostic risk.</p><p><strong>Conclusions: </strong>3DE-derived coupling parameters offer a noninvasive and reliable approach for assessing RV-PA coupling in patients with pre-capillary PH, especially for patients who cannot accurately estimate pulmonary artery pressure or have undergone cardiac surgery. 3DE SV/ESV is superior to TAPSE/sPAP for assessing postoperative RV-PA coupling in CTEPH patients, TAPSE/sPAP remains a valuable parameter for prognostic risk stratification in pre-capillary PH patients. Echocardiography can provide valuable information for assessing RV-PA coupling and prognosis in patients with pre-capillary PH. However, the application of echocardiographic coupling parameters should be determined based on the specific clinical context.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"108"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778797","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":"Incidence and features of pulmonary track nodules after CT-guided lung biopsy with track sealing using gelatin sponge slurry.","authors":"Flavien Grandjean, Nadia Withofs, Nancy Detrembleur, Laurent Gérard, Pierre Lamborelle, Christophe Valkenborgh, Nadia Dardenne, François Cousin","doi":"10.1186/s12880-025-01644-x","DOIUrl":"10.1186/s12880-025-01644-x","url":null,"abstract":"<p><strong>Background: </strong>Track sealing (TS) with gelatin sponge slurry (GSS) is efficient in reducing pneumothorax after CT-guided lung biopsy. Nodule appearance along the pulmonary track after TS with GSS is a potential issue that has not been previously evaluated.</p><p><strong>Methods: </strong>A secondary analysis of two studies evaluating the efficacy of lung TS in 710 patients in reducing post-biopsy pneumothorax was performed. Among these patients, 377 had a follow-up CT within 2 months post-biopsy and were retrospectively included in this study (187 had TS with GSS, 83 with saline, and 107 no TS). Imaging findings of the pulmonary track were described. Binary logistic regression was used to determine factors associated with lung track nodules.</p><p><strong>Results: </strong>Median time between biopsy and follow-up CT was 29 days (range, 1-61). A pulmonary track nodule was detected on follow-up CT in 65/377 (17.2%) patients. Sixty three out of these 65 nodules (97%) were observed in the GSS group. Factors significantly associated with nodules on multivariate analysis were GSS use (odds ratio: 47.4, 95%CI:11.8-189.5; p < .0001) and track length (odds ratio: 1.03, 95%CI:1.01-1.05; p = .009). Nodules were solid in 100%, ovoid in 83.1%, well-defined in 87.7%, and had smooth borders in 96.9%. Thirty-three nodules were still visible on imaging > 6 weeks after the biopsy.</p><p><strong>Conclusion: </strong>A pulmonary nodule along the biopsy track was detected on follow-up CT in 34% of cases when TS with GSS was performed. Recognition of these nodules on chest imaging is essential to avoid misinterpretation.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"107"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771212","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":"Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.","authors":"Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu","doi":"10.1186/s12880-025-01646-9","DOIUrl":"10.1186/s12880-025-01646-9","url":null,"abstract":"<p><strong>Background: </strong>Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) images, we developed a novel radiomics model. It combined bi-regional features and two machine learning algorithms. The aim of this study was to validate its potential value for preoperative prediction of MVI.</p><p><strong>Methods: </strong>This retrospective study included 304 HCC patients (training cohort, 216 patients; testing cohort, 88 patients) from three hospitals. Intratumoral and peritumoral volumes of interest were delineated in arterial phase, portal venous phase, and hepatobiliary phase images. Conventional radiomics (CR) and deep learning radiomics (DLR) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.</p><p><strong>Results: </strong>The bi-regional CR-DLR model showed more gains and gave better predictive performance than the single-regional models or single-machine learning models. Its AUC, accuracy, sensitivity, and specificity were 0.844, 76.9%, 87.8%, and 69.1% in the training cohort and 0.740, 73.9%, 50%, and 84.5% in the testing cohort. Alpha-fetoprotein (odds ratio was 0.32) and maximum tumor diameter (odds ratio was 1.270) were independent predictors. The AUCs of the clinical model and the Clin-R model were 0.655 and 0.672, respectively. There was no significant difference in the AUCs between all the models (P > 0.005).</p><p><strong>Conclusion: </strong>Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. It will provide valuable information for clinical personalized treatment, thereby improving patient prognosis.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"105"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750913","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":"Predictive impact of T2-MRI radiomics model on initial diagnosis of bone metastasis in prostate cancer patients.","authors":"Si Nie, Bing Fan, Shaogao Gui, Huachun Zou, Min Lan","doi":"10.1186/s12880-025-01642-z","DOIUrl":"10.1186/s12880-025-01642-z","url":null,"abstract":"<p><strong>Objective: </strong>The purpose of this study was to examine the potential predictive impact of the T2-MRI radiomics model on the initial diagnosis of bone metastasis in patients with prostate cancer (PCa).</p><p><strong>Methods: </strong>We retrospectively analyzed a total of 141 patients with confirmed PCa from clinical pathology records. Among them, 52 cases had bone metastasis and 89 cases did not. By employing a computer, the patients were randomly assigned to either a training group or a test group. Using ITK-SNAP software, we manually outlined T2WI images for all patients and performed radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. In the training group, a single-variable t-test was conducted to identify features strongly associated with PCa bone metastasis. Statistical significance was defined as P < 0.05. After dimensionality reduction, the Lasso model was employed to select the best subset, and a random forest model was established. To evaluate the performance of the radiomics model in predicting PCa bone metastasis in the test group, receiver operating characteristic (ROC) curves and confusion matrices were utilized.</p><p><strong>Results: </strong>The selected imaging features exhibited a significant correlation with the differential diagnosis of prostate cancer presence or absence of metastasis. The radiomic model demonstrated high predictive efficiency for PCa bone metastasis, achieving accuracy rates of 0.81% and 0.85% in the training and test groups, respectively. The sensitivities were 92% and 93%, and the specificities were 85% and 81%. The area under the curve values were 0.88 and 0.80 for the training and test groups, respectively.</p><p><strong>Conclusion: </strong>The MRI radiomics method based onT2WI images shows promise in accurately predicting PCa bone metastasis and can serve as a valuable tool for developing clinical treatment plans.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"106"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750918","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":"Multimodal diagnostic tools and advanced data models for detection of prodromal Parkinson's disease: a scoping review.","authors":"Ibrahim Serag, Ahmed Y Azzam, Amr K Hassan, Rehab Adel Diab, Mohamed Diab, Mahmoud Tarek Hefnawy, Mohamed Ahmed Ali, Ahmed Negida","doi":"10.1186/s12880-025-01620-5","DOIUrl":"https://doi.org/10.1186/s12880-025-01620-5","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. PD is diagnosed by a combination of motor symptoms including bradykinesia, resting tremors, rigidity and postural instability. Prodromal PD is the stage preceding the onset of classic motor symptoms of PD. The diagnosis of prodromal PD remains challenging despite many available diagnostic modalities.</p><p><strong>Aim: </strong>This scoping review aims to investigate and explore the current diagnostic modalities used to detect prodromal PD, focusing particularly on multimodal imaging analysis and AI-based approaches.</p><p><strong>Methods: </strong>We adhered to the PRISMA-SR guidelines for scoping reviews. We conducted a comprehensive literature search at multiple databases such as PubMed, Scopus, Web of Science, and the Cochrane Library from inception to July 2024, using keywords related to prodromal PD and diagnostic modalities. We included studies based on predefined inclusion and exclusion criteria and performed data extraction using a standardized form.</p><p><strong>Results: </strong>The search included 9 studies involving 567 patients with prodromal PD and 35,643 control. Studies utilized various diagnostic approaches including neuroimaging techniques and AI-driven models. sensitivity ranging from 43 to 84% and specificity up to 96%. Neuroimaging and AI technologies showed promising results in identifying early pathological changes and predicting PD onset. The highest specificity was achieved by neuromelanin-sensitive imaging model, while highest sensitivity was achieved by standard 10-s electrocardiogram (ECG) + Machine learning model.</p><p><strong>Conclusion: </strong>Advanced diagnostic modalities such as AI-driven models and multimodal neuroimaging revealed promising results in early detection of prodromal PD. However, their clinical application as screening tool for prodromal PD is limited because of the lack of validation. Future research should be directed towards using Multimodal imaging in diagnosing and screening for prodromal PD.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"103"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742221","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":"Hepatobiliary phase manifestations of breast cancer liver metastasis: differentiating molecular types through Gd-EOB-DTPA-enhanced MRI.","authors":"Hui Jiang, Jin-Rong Qu, Li-Feng Wang, Peng-Rui Gao, Bing-Jie Zheng, Hong-Kai Zhang, Li-Na Jiang","doi":"10.1186/s12880-025-01648-7","DOIUrl":"https://doi.org/10.1186/s12880-025-01648-7","url":null,"abstract":"<p><strong>Objective: </strong>The primary objective of this study is to evaluate the diagnostic efficacy of gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) -enhanced magnetic resonance imaging (MRI) in distinguishing breast cancer liver metastasis (BCLM) across different molecular types.</p><p><strong>Methods: </strong>Between August 2014 and July 2021, a cohort of 270 patients histologically diagnosed with BCLM underwent examination through dynamic contrast-enhanced MRI (DCE-MRI). The data collection encompassed clinical information of patients, as well as information on the quantity, shape, boundary, and fusion state of liver metastases. Additionally, MR sequences including T2-weighted imaging with fat suppression (FS), diffusion-weighted imaging (DWI), MR arterial phase, and hepatobiliary phase (HBP) were collected. The chi-squared test was employed to study the correlations between different molecular types of BCLM and imaging features observed in MRI.</p><p><strong>Results: </strong>Significant differences were observed in the HBP image features among various subtypes of breast cancer (P = 0.022). The morphology (oval, irregular) and fusion state (converging, separated lesions) of BCLM exhibited statistically significant differences based on breast cancer subtypes (P = 0.022, 0.004). No statistical differences were found in the quantity of BCLM, the boundary of metastasis (clear or vague), and imaging features of the T2WI-FS and DWI concerning the molecular subtypes of BCLM (P = 0.693, 0.161, 0.629, 0.629).</p><p><strong>Conclusion: </strong>The findings suggest that MRI, particularly Gd-EOB-DTPA-enhanced MRI, they displayed varied enhancement patterns, including the low signal, \"target sign\", \"rim enhancement\", and \"doughnut-like enhancement\". Most basal-like metastases demonstrated a low signal, the other molecular types primarily showing the \"target sign\". This is invaluable in the imaging diagnosis of BCLM across different molecular type.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"104"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742220","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-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.","authors":"Yeseul Kang, Sang-Young Kim, Jun Hwee Kim, Nak-Hoon Son, Chae Jung Park","doi":"10.1186/s12880-025-01647-8","DOIUrl":"10.1186/s12880-025-01647-8","url":null,"abstract":"<p><strong>Background: </strong>To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.</p><p><strong>Methods: </strong>Ten healthy volunteers and 22 consecutive patients were prospectively enrolled. Volunteers underwent 3D brain MRI including T1 without CS factor (9:16 min, reference standard); with CS factor of 2 without DLR (CS2, 4:6 min); with CS factor of 2 with DLR (DLR-CS2); with CS factor of 4 without DLR (CS4, 2:6 min); and with CS factor of 4 with DLR (DLR-CS4). The patients' MRI included the CS2 and DLR-CS4. The volumes of lateral ventricles, hippocampus, choroid plexus, and white matter hypointensity were calculated and compared among the sequences. Three radiologists independently assessed anatomical conspicuity, overall image quality, artifacts, signal-to-noise ratio (SNR), and sharpness using a 5-point scale for each sequence.</p><p><strong>Results: </strong>Applying acceleration factors of 2 and 4 reduced the scan time to 65.4% and 33.5%, respectively, of that of the reference standard. Volumes of all the measured subregions showed no significant differences among different sequences in all participants. In qualitative analysis, the interrater agreement was excellent (κ = 0.844-0.926). In volunteers, quality of DLR-CS4 were comparable to those of CS2 for all metrics except for the overall image quality and SNR despite a 51.2% scan time reduction. In patients, DLR-CS4 showed quality comparable to that of CS2 for all metrics.</p><p><strong>Conclusions: </strong>DLR allowed the scan time reduction by at least half without sacrificing image quality and volumetric quantification accuracy, supporting its reliability and efficiency.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"102"},"PeriodicalIF":2.9,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143728376","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":"Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.","authors":"Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn","doi":"10.1186/s12880-025-01643-y","DOIUrl":"10.1186/s12880-025-01643-y","url":null,"abstract":"<p><strong>Background and purpose: </strong>Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI.</p><p><strong>Materials and methods: </strong>Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models.</p><p><strong>Results: </strong>All models obtained a good DSC (DSC > = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models.</p><p><strong>Conclusion: </strong>The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"101"},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11948752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717909","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":"Accuracy of ultrasonographic transcerebellar diameter for dating in third trimester of pregnancy in Nigerian women: a cross-sectional study.","authors":"Okechukwu Uche Ofoegbu, Nicholas Irurhe, Tersur Terry Saalu, Oluwaseun Emmanuel Familusi, Charity Opeoluwapo Maduagu, Lucky Enajite Tietie, Olaniyi Araotan Kusamotu, Ochuwa Adiketu Babah","doi":"10.1186/s12880-025-01634-z","DOIUrl":"10.1186/s12880-025-01634-z","url":null,"abstract":"<p><strong>Background: </strong>Accurate prediction of foetal gestational age is of critical importance as it can positively affect the outcome of pregnancy. Routine sonographic estimation of gestational age using biparietal diameter, head circumference, abdominal circumference and femur length is popular but has limitations especially when used as a singly or in late pregnancy. Often pregnant women in low-middle-income countries like Nigeria register for antenatal care late in pregnancy, necessitating the need for a single, cost-effective parameter that requires minimal skills to measure gestational age accurately in late pregnancies. This study examined the accuracy of ultrasonographic transcerebellar diameter compared to other foetal biometric parameters for dating in third trimester of pregnancy.</p><p><strong>Methodology: </strong>An analytic cross-sectional study conducted at Lagos University Teaching Hospital, Idi-Araba, Lagos, on 110 pregnant women in their third trimester. Data was collected using an interviewer administered questionnaire. Transabdominal ultrasound scan was done to determine the gestational age by measuring the biparietal diameter, head circumference, abdominal circumference, femur length and transcerebellar diameter. Spearman's correlation coefficient was used to determine the correlation between the biometric measurements; Accuracy was determine using gestational age from menstrual date as gold standard and comparisons made using Chi square test.</p><p><strong>Results: </strong>Mean age of participants was 31.5 ± 5.8 years; mean gestational age 236 ± 25 days. Compared to biparietal diameter, head circumference, abdominal circumference, and femur length, transcerebellar diameter correlates best with gestational age (r = 0.8837, p < 0.001). At an error margin of ± 2weeks, transcerebellar diameter had a high predictive accuracy of 84.6%, though significantly less than that for abdominal circumference alone, 86.4% (p = 0.003), and also less than that for all four well known foetal biometric parameters (biparietal diameter, head circumference, abdominal circumference, and femur length) combined, 85.5% (p < 0.001).</p><p><strong>Conclusion: </strong>Transcerebellar diameter has a better correlation with gestational age than other routine foetal biometric parameters and has high predictive accuracy for dating in third trimester of pregnancy. It may thus play a relevant role in low resource settings where there is shortage of staff and limited skills in obstetric ultrasonography.</p><p><strong>Clinical trial number: </strong>Not applicable for this study.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"100"},"PeriodicalIF":2.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708337","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":"Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.","authors":"Tongxin Li, Mingyi Liao, Yong Fu, Fanghong Zhang, Luya Shen, Junliang Che, Shulei Wu, Jie Liu, Wei Wu, Ping He, Qingyuan Xu, Yi Wu","doi":"10.1186/s12880-025-01641-0","DOIUrl":"10.1186/s12880-025-01641-0","url":null,"abstract":"<p><strong>Background: </strong>Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.</p><p><strong>Methods: </strong>Chest CT images from 383 patients with rib fractures were retrospectively analyzed. The dataset was divided into a training set (n = 306) and an internal testing set (n = 77). An external testing set of 50 patients from the public RibFrac dataset was included. Fractures were classified into severe and non-severe categories. A modified YOLO-based deep learning model was developed for detection and grading. Performance was compared with thoracic surgeons using precision, recall, mAP50, and F1 score.</p><p><strong>Results: </strong>The deep learning model showed excellent performance in diagnosing fresh rib fractures. For all fractures types in internal test set, the precision, recall, mAP50, and F1 score were 0.963, 0.934, 0.972, and 0.948, respectively. The model outperformed thoracic surgeons of varying experience levels (all p < 0.01).</p><p><strong>Conclusion: </strong>The proposed deep learning model can automatically detect and grade fresh rib fractures with accuracy comparable to that of physicians. This model helps improve diagnostic accuracy, reduce physician workload, save medical resources, and strengthen health care in resource-limited areas.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"98"},"PeriodicalIF":2.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699582","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}