Insights into Imaging最新文献

{"title":"CT-based radiomics models decode fibrosis content and molecular differences in pancreatic ductal adenocarcinoma: a multi-institutional study.","authors":"Fangqing Wang, Yang Sun, Jianwei Xu, Yufan Chen, Hui Zhang, Guotao Yin, Dexin Yu","doi":"10.1186/s13244-025-02036-z","DOIUrl":"https://doi.org/10.1186/s13244-025-02036-z","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a CT radiomics model for predicting fibrosis grade in pancreatic ductal adenocarcinoma (PDAC) and to investigate the underlying prognosis value and biological basis.</p><p><strong>Methods: </strong>Patients with resected PDAC were retrospectively included from three institutions. Evaluating tumor fibrosis content using fibrotic pixels proportion through Masson staining of postoperative pathological sections. Radiomics features from preoperative contrast-enhanced CT (CECT) were extracted and used to develop models in the training cohort. The diagnosis performance was further validated in the two test cohorts. The outcome cohort, including patients with advanced PDAC undergoing neoadjuvant chemotherapy, was used to evaluate the predictive value of the model for overall survival (OS) and disease-free survival (DFS), which were investigated using the Kaplan-Meier method and log-rank test. RNA sequencing data from a prospective biological basis cohort were conducted to explore the biological processes underlying the radiomics model.</p><p><strong>Results: </strong>Among 215 patients (median age 60.89 years, 142 men) used for radiomics modeling, 132 (61.40%) were confirmed as high fibrosis content. The combined phase (CP) radiomics model, which included all CECT radiomics features, showed the best performance for predicting fibrosis grade, with AUCs of 0.831, 0.785, and 0.746 in training, internal test, and external test cohorts. OS (p = 0.011) and DFS (p = 0.022) can be categorized using the CP radiomics model in the outcome cohort. RNA-seq indicated that different CP models were associated with fibrotic production and remodeling processes.</p><p><strong>Conclusion: </strong>The CP radiomics model showed the best performance in predicting fibrosis grades in PDAC.</p><p><strong>Critical relevance statement: </strong>Fibrosis grading is of prognostic and neoadjuvant chemotherapy efficacy evaluation significance, and the CT-based combined phase radiomics model established in our study will facilitate risk stratification and selection of personalized treatment strategies for patients. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights into their interpretability and clinical translation.</p><p><strong>Key points: </strong>Fibrosis grading is of prognostic significance in pancreatic ductal adenocarcinoma (PDAC), but lacks a reliable preoperative assessment. The CT-based combined phase (CP) radiomics model predicts fibrosis grading effectively in PDAC. The CP radiomics model demonstrated prognostic and neoadjuvant chemotherapy efficacy evaluation value and underlying biological processes, which related fibrotic production and remodeling processes.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"190"},"PeriodicalIF":4.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145052873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An MRI-based model for preoperative prediction of tertiary lymphoid structures in patients with gallbladder cancer.","authors":"Ying Xu, Zhuo Li, Weihua Zhi, Yi Yang, Jingzhong Ouyang, Yanzhao Zhou, Zeliang Ma, Sicong Wang, Lizhi Xie, Jianming Ying, Jinxue Zhou, Xinming Zhao, Feng Ye","doi":"10.1186/s13244-025-02007-4","DOIUrl":"https://doi.org/10.1186/s13244-025-02007-4","url":null,"abstract":"<p><strong>Objectives: </strong>To predict tertiary lymphoid structures (TLSs) in gallbladder cancer (GBC) using preoperative magnetic resonance imaging (MRI)-based radiomics.</p><p><strong>Methods: </strong>Patients with GBC from two centres served as training (n = 129) and external validation (n = 44) cohorts. Radiomics features were extracted from six imaging sequences for inclusion in a radiomics model (Rad-score). Univariate and multivariate logistic regression were used to identify independent clinico-radiological predictors of TLS status. The clinical and radiomics models were integrated into a combined model. Areas under receiver operating characteristic curves (AUC) were used to assess model performance. The combined model was divided into low- and high-risk according to the cut-off value determined by the maximum Youden index of the ROC.</p><p><strong>Results: </strong>Intratumoural TLSs independently predicted RFS (p = 0.046). Eight features were included in the Rad-score. The clinical model included three independent predictors of TLS status (tumour height, liver invasion, and arterial-phase hypo-enhancement). In the training cohort, the combined model outperformed the separate clinical and radiomics models (AUC, 0.891 vs 0.870 and 0.775, respectively) and was externally valid. In both training and external cohorts, RFS in the low-risk group was substantially higher compared to the high-risk group. The low-risk group in the immunotherapy cohort had a significantly higher median overall survival than the high-risk group.</p><p><strong>Conclusions: </strong>The MRI-based combined model developed in this study can preoperatively predict intratumoural TLS status. It accurately stratified the RFS of patients after surgery and the OS of patients with immunotherapy.</p><p><strong>Critical relevance statement: </strong>This combined model is useful for predicting response and prognosis, not only for the recurrence-free survival of patients with GBC who have undergone surgery, but also for the overall survival of patients who have received immunotherapy KEY POINTS: Intratumoural TLSs independently predict recurrence-free survival of GBC. Our MRI-based combined model is a preoperative TLS marker. The combined model accurately stratifies postoperative/post-immunotherapy recurrence-free and overall survival of GBC.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"189"},"PeriodicalIF":4.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Left ventricular remodeling index to predict ventricular tachyarrhythmia in dilated cardiomyopathy with ejection fraction < 35.","authors":"Xi Jia, Weipeng Yan, Xuan Ma, Zhixiang Dong, Jiaxin Wang, Shujuan Yang, Kankan Zhao, Zhuxin Wei, Yun Tang, Pengyu Zhou, Xingrui Chen, Yujie Liu, Xiuyu Chen, Shihua Zhao","doi":"10.1186/s13244-025-02059-6","DOIUrl":"https://doi.org/10.1186/s13244-025-02059-6","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the left ventricular remodeling index (LVRI) for predicting ventricular tachyarrhythmia (VTA) in patients with dilated cardiomyopathy (DCM) with left ventricular ejection fraction (LVEF) < 35%.</p><p><strong>Materials and methods: </strong>In this retrospective single-center study, consecutive DCM patients with LVEF < 35% (n = 271) who underwent cardiac magnetic resonance (CMR) imaging were followed up. The study endpoint was VTA, including sudden cardiac death and major ventricular arrhythmias. The CMR-derived LVRI was defined as the cubic root of the LV end-diastolic volume divided by the maximal LV wall thickness. Competing risk regression analysis and Kaplan-Meier analysis were used to evaluate the association of LVRI with VTA.</p><p><strong>Results: </strong>Over 71-month median follow-up (interquartile range: 17-134 months), 35 (12.9%, mean age 46.7 years, 27 males) participants reached VTA events. The presence (62.9% vs. 60.2%, p = 0.761) and extent (6.9 ± 6.6 vs. 6.5 ± 8.3, p = 0.747) of late gadolinium enhancement (LGE) and LVEF (23.3 ± 6 vs. 21.9 ± 10.3, p = 0.197) were not significantly different between the patients with and without endpoint. Kaplan-Meier curve analysis showed that participants with LVRI ≥ 7.5 were more likely to experience VTA (p < 0.0001). In the multiple competing risk analysis, LVRI ≥ 7.5 (HR, 2.496; 95% CI: 1.213-5.138; p = 0.013) was observed as an independent predictor of VTA after adjusting for age, sex and left bundle branch block.</p><p><strong>Conclusions: </strong>For nonischemic DCM patients with LVEF < 35%, LVRI ≥ 7.5 was associated with lethal VTA events and provided incremental value over conventional CMR parameters.</p><p><strong>Critical relevance statement: </strong>The left ventricular remodeling index (LVRI) was independently associated with ventricular tachyarrhythmias in dilated cardiomyopathy patients with LVEF < 35%, and warrants future multicenter validation to assess incremental value over established predictors for implantable cardioverter-defibrillator decision-making.</p><p><strong>Key points: </strong>Left ventricular ejection fraction did not exhibit significant prognostic value for end-stage dilated cardiomyopathy patients. Cardiac MRI (CMR)-assessed left ventricular remodeling index (LVRI) ≥ 7.5 was an independent predictor of ventricular tachyarrhythmia. LVRI provided incremental prognostic value over conventional CMR parameters.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"188"},"PeriodicalIF":4.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myocardial characterization using late enhancement photon-counting detector CT in ventricular arrhythmia: comparison with electroanatomical mapping.","authors":"Victor Mergen, Martin F Reiner, Konstantin Klambauer, Lukas J Moser, Fu Guan, Corinna Brunckhorst, Firat Duru, Ernst Klotz, Thomas Flohr, Frank Ruschitzka, Robert Manka, Matthias Eberhard, Hatem Alkadhi, Ardan M Saguner","doi":"10.1186/s13244-025-02069-4","DOIUrl":"https://doi.org/10.1186/s13244-025-02069-4","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to assess the feasibility of left ventricular myocardial characterization in patients with ventricular arrhythmias using late enhancement (LE) photon-counting detector computed tomography (PCD-CT) scans, in comparison with invasive endocardial electroanatomical mapping (EAM).</p><p><strong>Materials and methods: </strong>This single-center retrospective observational study included 20 patients (mean age 64 ± 8 years, 4 female) who underwent PCD-CT prior to 3D endocardial uni- and bipolar EAM and radiofrequency catheter ablation (RFCA) between May 2022 and February 2024. Sixteen patients (80%) had cardiac implantable electronic devices. Twelve (60%) had ischemic and 8 (40%) had non-ischemic cardiomyopathy. Pathologic myocardial segments were defined by low-voltage electrograms < 5 mV in unipolar and < 0.5 mV in bipolar maps. Cardiac scans included LE acquisitions 5 min after contrast injection in the ECG-triggered sequential mode. Myocardial extracellular volume was computed from cardiac LE scans and visualized as polar and atlas maps (the latter depicting wall thickness) to identify pathologic segments with fibrosis and/or scar. LE scans were compared with EAM.</p><p><strong>Results: </strong>In patients with ischemic cardiomyopathy, agreement of pathologic segments on CT was good with unipolar EAM (κ = 0.655 ± 0.249), and moderate with bipolar EAM (κ = 0.547 ± 0.267). In patients with non-ischemic cardiomyopathy, agreement of pathologic segments on CT was moderate compared with unipolar (κ = 0.455 ± 0.356) and fair with bipolar EAM (κ = 0.255 ± 0.260).</p><p><strong>Conclusions: </strong>Preliminary evidence suggests that characterization of pathologic myocardial segments using LE PCD-CT scans is feasible and yields good agreement with endocardial EAM, particularly when compared with unipolar EAM and in patients with ischemic cardiomyopathy.</p><p><strong>Critical relevance statement: </strong>Characterization of pathologic left ventricular segments using myocardial extracellular volume and thickness representations from spectral late enhancement photon-counting detector CT scans indicates good agreement with unipolar endocardial electroanatomical mapping, particularly in patients with ischemic cardiomyopathy.</p><p><strong>Key points: </strong>Cardiac late enhancement imaging with photon-counting detector CT may enable characterization of pathologic myocardial segments in ventricular arrhythmia. Myocardial extracellular volume and thickness representations yield good agreement with unipolar endocardial electroanatomical mapping, particularly in patients with ischemic cardiomyopathy. Left ventricular myocardial characterization is feasible with late enhancement photon-counting detector CT and may complement invasive radiofrequency catheter ablations.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"187"},"PeriodicalIF":4.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Macrotrabecular-massive subtype in hepatocellular carcinoma based on contrast-enhanced CT: deep learning outperforms machine learning.","authors":"Lulu Jia, Zeyan Li, Gang Huang, Hanchen Jiang, Hao Xu, Jianxin Zhao, Jinkui Li, Junqiang Lei","doi":"10.1186/s13244-025-02063-w","DOIUrl":"https://doi.org/10.1186/s13244-025-02063-w","url":null,"abstract":"<p><strong>Objective: </strong>To develop a CT-based deep learning model for predicting the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) and to compare its diagnostic performance with machine learning models.</p><p><strong>Materials and methods: </strong>We retrospectively collected contrast-enhanced CT data from patients diagnosed with HCC via histopathological examination between January 2019 and August 2023. These patients were recruited from two medical centers. All analyses were performed using two-dimensional regions of interest. We developed a novel deep learning network based on ResNet-50, named ResNet-ViT Contrastive Learning (RVCL). The RVCL model was compared against baseline deep learning models and machine learning models. Additionally, we developed a multimodal prediction model by integrating deep learning models with clinical parameters. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>A total of 368 patients (mean age, 56 ± 10; 285 [77%] male) from two institutions were retrospectively enrolled. Our RVCL model demonstrated superior diagnostic performance in predicting MTM (AUC = 0.93) on the external test set compared to the five baseline deep learning models (AUCs range 0.46-0.72, all p < 0.05) and the three machine learning models (AUCs range 0.49-0.60, all p < 0.05). However, integrating the clinical biomarker Alpha-Fetoprotein (AFP) into the RVCL model did not significant improvement in diagnostic performance (internal test data set: AUC 0.99 vs 0.95 [p = 0.08]; external test data set: AUC 0.98 vs 0.93 [p = 0.05]).</p><p><strong>Conclusion: </strong>The deep learning model based on contrast-enhanced CT can accurately predict the MTM subtype in HCC patients, offering a smart tool for clinical decision-making.</p><p><strong>Critical relevance statement: </strong>The RVCL model introduces a transformative approach to the non-invasive diagnosis MTM subtype of HCC by harmonizing convolutional neural networks and vision transformers within a unified architecture.</p><p><strong>Key points: </strong>The RVCL model can accurately predict the MTM subtype. Deep learning outperforms machine learning for predicting MTM subtype. RVCL boosts accuracy and guides personalized therapy.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"186"},"PeriodicalIF":4.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12394088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating head CT referral quality and appropriateness in an Italian emergency department: a monocentric retrospective study.","authors":"Marco Parillo, Milena Calabrese, Anna Maria Careddu, Antonio Alessandro Pallottino, Carlo Cosimo Quattrocchi","doi":"10.1186/s13244-025-02042-1","DOIUrl":"https://doi.org/10.1186/s13244-025-02042-1","url":null,"abstract":"<p><strong>Objectives: </strong>To analyze the quality and appropriateness of head CT referrals from the emergency department (ED) of a single hospital in Italy.</p><p><strong>Materials and methods: </strong>A quality care study was designed to retrospectively identify consecutive head CT referrals generated from the ED of a tertiary hospital between January 1 and April 30, 2022. Referral quality was assessed using the Reason for Exam Imaging Reporting and Data System (RI-RADS), while referral appropriateness was evaluated according to the American College of Radiology (ACR) criteria.</p><p><strong>Results: </strong>We included 2908 imaging requests, of which 620 (21%) were adequate (RI-RADS A or B) and 2288 (79%) were inadequate (RI-RADS C or D) in terms of quality. In 410 cases, it was not possible to evaluate the appropriateness of the requests according to the ACR guidelines due to the lack of clinical data. Among the 2498 evaluable requests, 25% were classified as usually not appropriate. Of the requests with RI-RADS A or B, 84% were appropriate. Conversely, among the evaluable requests with RI-RADS C or D, the percentage of appropriate requests dropped to 70%. Of all patients with inappropriate requests, 98% did not suffer from acute cerebral diseases according to imaging, with headache and syncope being the primary clinical indications. Analysis of positivity rates revealed a significant difference between appropriate and inappropriate CT scans (11% vs 1%; p-value < 0.001).</p><p><strong>Conclusion: </strong>The recent increase in head CT scan requests in the ED is not completely justified and could be mitigated by improving the quality and appropriateness of referrals.</p><p><strong>Critical relevance statement: </strong>Excessive head CT requests in the ED cause needless radiation, pollution, and costs. Integrating guidelines and prospective justification with clear documentation in patient records, along with improved staff training and a no-blame culture, are key to reducing unnecessary imaging.</p><p><strong>Key points: </strong>RI-RADS scores the quality, while the ACR criteria assess the appropriateness of imaging referrals. Most ED head CT requests lacked quality (especially lacking a specific diagnostic question) and were often clinically inappropriate. Appropriate head CT indication strongly predicted finding acute cerebral pathology on imaging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"185"},"PeriodicalIF":4.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12381342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shear wave elastography for assessing kidneys in pediatric patients with primary nephrotic syndrome.","authors":"Jianhuan Yang, Fangfang Yu, Dexuan Wang, Maosheng Xu, Hongxia Luo","doi":"10.1186/s13244-025-02070-x","DOIUrl":"https://doi.org/10.1186/s13244-025-02070-x","url":null,"abstract":"<p><strong>Objective: </strong>Primary nephrotic syndrome (PNS) is a prevalent kidney disorder in pediatric patients, characterized by significant proteinuria, hypoalbuminemia, and edema, which poses serious health risks and economic burdens due to frequent relapses and hospitalizations. This study aims to explore the utility of shear wave elastography (SWE) as a novel, non-invasive biomarker for assessing renal health in this population.</p><p><strong>Materials and methods: </strong>We used a cross-sectional design involving 76 pediatric patients with PNS and a control group, and measured renal stiffness through SWE The clinical characteristics of the nephrotic group were collected, including age, sex, disease duration, clinical type, 24-h urine protein, plasma albumin, and the relationship with 2D-SWE value was analyzed.</p><p><strong>Results: </strong>Our results demonstrated a significant difference in renal elasticity, with the nephrotic syndrome group exhibiting a mean shear wave velocity (YM) of 22.36 ± 8.53 kPa compared to 17.51 ± 4.09 kPa in controls (p < 0.05). Furthermore, the area under the ROC curve for SWE was 0.67, indicating moderate predictive capability for renal damage. Notably, there were no significant differences in YM values across various clinical classifications of nephrotic syndrome, suggesting a uniform renal damage assessment irrespective of clinical type. Additionally, renal elasticity did not significantly vary regardless of whether the patient's proteinuria had improved. (p = 0.464), indicating SWE's potential as an independent biomarker.</p><p><strong>Conclusions: </strong>Our findings highlight the promise of SWE in enhancing diagnostic accuracy and prognostic evaluation in pediatric nephrotic syndrome.</p><p><strong>Critical relevance statement: </strong>Shear wave elastography is a valuable noninvasive method for assessing renal elasticity in children with primary nephrotic syndrome.</p><p><strong>Key points: </strong>Shear wave elastography (SWE) can be used to evaluate the elasticity of renal tissue. SWE values were higher in children with PNS than in the control group. SWE is a valuable non-invasive method for assessing renal elasticity in children with PNS.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"183"},"PeriodicalIF":4.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting pediatric age from chest X-rays using deep learning: a novel approach.","authors":"Maolin Li, Jiang Zhao, Huanhuan Liu, Biao Jin, Xuee Cui, Dengbin Wang","doi":"10.1186/s13244-025-02068-5","DOIUrl":"https://doi.org/10.1186/s13244-025-02068-5","url":null,"abstract":"<p><strong>Objectives: </strong>Accurate age estimation is essential for assessing pediatric developmental stages and for forensics. Conventionally, pediatric age is clinically estimated by bone age through wrist X-rays. However, recent advances in deep learning enable other radiological modalities to serve as a promising complement. This study aims to explore the effectiveness of deep learning for pediatric age estimation using chest X-rays.</p><p><strong>Materials and methods: </strong>We developed a ResNet-based deep neural network model enhanced with Coordinate Attention mechanism to predict pediatric age from chest X-rays. A dataset comprising 128,008 images was retrospectively collected from two large tertiary hospitals in Shanghai. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed as main evaluation metrics across age groups. Further analysis was conducted using Spearman correlation and heatmap visualizations.</p><p><strong>Results: </strong>The model achieved an MAE of 5.86 months for males and 5.80 months for females on the internal validation set. On the external test set, the MAE was 7.40 months for males and 7.29 months for females. The Spearman correlation coefficient was above 0.98, indicating a strong positive correlation between the predicted and true age. Heatmap analysis revealed the deep learning model mainly focused on the spine, mediastinum, heart and great vessels, with additional attention given to surrounding bones.</p><p><strong>Conclusions: </strong>We successfully constructed a large dataset of pediatric chest X-rays and developed a neural network model integrated with Coordinate Attention for age prediction. Experiments demonstrated the model's robustness and proved that chest X-rays can be effectively utilized for accurate pediatric age estimation.</p><p><strong>Critical relevance statement: </strong>By integrating pediatric chest X-rays with age data using deep learning, we can provide more support for predicting children's age, thereby aiding in the screening of abnormal growth and development in children.</p><p><strong>Key points: </strong>This study explores whether deep learning could leverage chest X-rays for pediatric age prediction. Trained on over 120,000 images, the model shows high accuracy on internal and external validation sets. This method provides a potential complement for traditional bone age assessment and could reduce radiation exposure.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"184"},"PeriodicalIF":4.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a lung perfusion automated quantitative model based on dual-energy CT pulmonary angiography in patients with chronic pulmonary thromboembolism.","authors":"Linfeng Xi, Jianping Wang, Anqi Liu, Yifei Ni, Jie Du, Qiang Huang, Yishan Li, Jing Wen, Hongyi Wang, Shuai Zhang, Yunxia Zhang, Zhu Zhang, Dingyi Wang, Wanmu Xie, Qian Gao, Yong Cheng, Zhenguo Zhai, Min Liu","doi":"10.1186/s13244-025-02067-6","DOIUrl":"10.1186/s13244-025-02067-6","url":null,"abstract":"<p><strong>Objective: </strong>To develop PerAIDE, an AI-driven system for automated analysis of pulmonary perfusion blood volume (PBV) using dual-energy computed tomography pulmonary angiography (DE-CTPA) in patients with chronic pulmonary thromboembolism (CPE).</p><p><strong>Materials and methods: </strong>In this prospective observational study, 32 patients with chronic thromboembolic pulmonary disease (CTEPD) and 151 patients with chronic thromboembolic pulmonary hypertension (CTEPH) were enrolled between January 2022 and July 2024. PerAIDE was developed to automatically quantify three distinct perfusion patterns-normal, reduced, and defective-on DE-CTPA images. Two radiologists independently assessed PBV scores. Follow-up imaging was conducted 3 months after balloon pulmonary angioplasty (BPA).</p><p><strong>Results: </strong>PerAIDE demonstrated high agreement with the radiologists (intraclass correlation coefficient = 0.778) and reduced analysis time significantly (31 ± 3 s vs. 15 ± 4 min, p < 0.001). CTEPH patients had greater perfusion defects than CTEPD (0.35 vs. 0.29, p < 0.001), while reduced perfusion was more prevalent in CTEPD (0.36 vs. 0.30, p < 0.001). Perfusion defects correlated positively with pulmonary vascular resistance (ρ = 0.534) and mean pulmonary artery pressure (ρ = 0.482), and negatively with oxygenation index (ρ = -0.441). PerAIDE effectively differentiated CTEPH from CTEPD (AUC = 0.809, 95% CI: 0.745-0.863). At the 3-month post-BPA, a significant reduction in perfusion defects was observed (0.36 vs. 0.33, p < 0.01).</p><p><strong>Conclusion: </strong>CTEPD and CTEPH exhibit distinct perfusion phenotypes on DE-CTPA. PerAIDE reliably quantifies perfusion abnormalities and correlates strongly with clinical and hemodynamic markers of CPE severity.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov, NCT06526468. Registered 28 August 2024- Retrospectively registered, https://clinicaltrials.gov/study/NCT06526468?cond=NCT06526468&rank=1 .</p><p><strong>Critical relevance statement: </strong>PerAIDE is a dual-energy computed tomography pulmonary angiography (DE-CTPA) AI-driven system that rapidly and accurately assesses perfusion blood volume in patients with chronic pulmonary thromboembolism, effectively distinguishing between CTEPD and CTEPH phenotypes and correlating with disease severity and therapeutic response.</p><p><strong>Key points: </strong>Right heart catheterization for definitive diagnosis of chronic pulmonary thromboembolism (CPE) is invasive. PerAIDE-based perfusion defects correlated with disease severity to aid CPE-treatment assessment. CTEPH demonstrates severe perfusion defects, while CTEPD displays predominantly reduced perfusion. PerAIDE employs a U-Net-based adaptive threshold method, which achieves alignment with and faster processing relative to manual evaluation.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"182"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controversies in contrast-enhanced ultrasound (CEUS): pregnancy, paediatric, abdominal trauma, complex renal cysts, and endovascular aortic repair follow-up.","authors":"Paul S Sidhu, Dirk Andre Clevert, Annamaria Deganello, Maciej Piskunowicz, Vito Cantisani, Thomas Fischer","doi":"10.1186/s13244-025-02055-w","DOIUrl":"10.1186/s13244-025-02055-w","url":null,"abstract":"<p><p>The use of contrast-enhanced ultrasound (CEUS) in clinical practice is theoretically limited to the licensed indications: focal liver lesions, breast, peripheral arterial system, and the heart. In reality, there has been a continuous expansion of the deployment of CEUS examinations to many other organs and body parts over the last 20 years. Many of these applications are a natural extension of the diagnostic capabilities of the CEUS examination, used to achieve a better imaging outcome. These applications have been supported by guidelines issued by scientific societies, detailing the application, accuracy, and safety of the clinical performance. Nevertheless, there are some areas in which it remains more difficult to establish the use of CEUS in the diagnostic pathway. In the pregnant patient, CEUS is an ideal examination-a natural extension of B-mode ultrasound, avoiding ionising radiation and iodinated contrast. The contrast agents used in ultrasound do not cross the placental barrier. Ultrasound in the paediatric patient is used widely, and extending this to a CEUS examination improves diagnostic capabilities, avoiding less child-friendly imaging techniques. The parent can be in the room at the time of the ultrasound examination. Other aspects of CEUS usage are hampered by the lack of physician engagement despite the proven advantages of the technique, the reduction in the morbidity associated with CT and MR imaging, particularly the contrast agents used in these modalities. Complex renal cyst classification, follow-up of blunt abdominal trauma and the surveillance following placement of an aortic stent graft are all areas of potential benefit to the diagnosis. All these are better imaged on a CEUS examination. Furthermore, cost savings can be achieved using CEUS, mostly by alleviating downstream costs of CT and MR imaging. CRITICAL RELEVANCE STATEMENT: CEUS use outside licensed uses is becoming established, driven by the unique ability to achieve diagnostic standards safely and with patient acceptability, pushing the boundaries in areas of abdominal trauma, pregnancy, paediatrics, aortic implants, and complex renal cysts. KEY POINTS: CEUS has a narrow range of licensed applications in medical imaging, but is used widely. An exclusively intravascular agent allows assessment of vascular flow at the capillary level. CEUS is extremely safe and can be used in many areas that require repeated high-resolution imaging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"179"},"PeriodicalIF":4.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}