Insights into Imaging最新文献

{"title":"CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.","authors":"Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu","doi":"10.1186/s13244-025-01952-4","DOIUrl":"10.1186/s13244-025-01952-4","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate CT-based radiomics deep learning signatures for the non-invasive prediction of metastatic potential in pheochromocytomas and paragangliomas (PPGLs).</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 249 PPGL patients from three institutions, dividing them into training (n = 138), test1 (n = 71), and test2 (n = 40) sets. Based on the grading system for adrenal pheochromocytoma and paraganglioma (GAPP), patients were classified into low-risk (GAPP < 3) and high-risk (GAPP ≥ 3) groups. Radiomic features were extracted from CT venous phase images and modeled using six machine learning algorithms. The maximum 2D sections and 3D images of each tumor were input into four ResNet models to obtain predictive probabilities. Optimal models were selected based on receiver operating characteristic analysis and integrated with radiological features to develop a combined model, which was evaluated on external datasets, and explored prognostic information.</p><p><strong>Results: </strong>The support vector machine radiomics and 2D ResNet-50 models demonstrated good performance. By integrating these two models with intratumoral necrosis features, we constructed a combined model that achieved high accuracy, with area under the curve (AUC) values of 0.90 for the training, 0.86 for the test1, and 0.88 for the test2 sets. This model effectively stratified patients based on metastasis-free survival (p = 0.003). Its predictive ability remains robust below the 6 cm threshold, with AUC values exceeding 0.87 across all datasets.</p><p><strong>Conclusions: </strong>The combined model can predict the metastatic potential of PPGL in the preoperative stage, providing a precise surgical strategy for pheochromocytoma regarding the 6 cm surgical threshold.</p><p><strong>Critical relevance statement: </strong>The combined model, established based on radiomic and deep learning signatures, shows potential for early preoperative prediction of metastatic potential in PPGL.</p><p><strong>Key points: </strong>Metastatic potential of PPGL affects surgical approaches and prognosis. CT-based radiomics deep learning signatures can predict the metastatic potential in PPGL.3. The combined model's predictive ability remains robust below the 6-cm threshold. The combined model's predictive ability remains robust below the 6-cm threshold.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"81"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788112","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":"Ischiofemoral impingement in joint preserving hip surgery: prevalence and imaging predictors.","authors":"Alexander F Heimann, Moritz Wagner, Peter Vavron, Alexander Brunner, Till D Lerch, Ehrenfried Schmaranzer, Joseph M Schwab, Simon D Steppacher, Moritz Tannast, Reto Sutter, Florian Schmaranzer","doi":"10.1186/s13244-025-01946-2","DOIUrl":"10.1186/s13244-025-01946-2","url":null,"abstract":"<p><strong>Objectives: </strong>To determine the prevalence of ischiofemoral impingement (IFI) in young patients evaluated for joint-preserving hip surgery and investigate its associations with osseous deformities and intra-articular pathologies.</p><p><strong>Methods: </strong>Retrospective study of 256 hips (224 patients, mean age 34 years) that were examined with radiographs and MR arthrography for hip pain. Quadratus femoris muscle edema was used to indicate IFI and measurements of ischiofemoral space were performed. Imaging analysis assessed cam deformity, femoral torsion, neck-shaft angle, ischial angle, acetabular coverage-/ version, and chondro-labral pathology. Prevalence of MRI findings consistent with IFI was calculated and univariate- and multivariate logistic regression identified associations between IFI and hip deformities.</p><p><strong>Results: </strong>Quadratus femoris muscle edema consistent with IFI was present in 9% (23/256 hips) with narrowing of the ischiofemoral distance (1.7 ± 0.6 cm vs 2.8 ± 0.7 cm in the control group, p < 0.001) and a higher prevalence in females (89% vs 45%, p < 0.001). Multiple regression identified female sex (OR 12.5, 95% CI: 1.6-98.2, p = 0.017), high femoral torsion (OR 3.9, 1.4-10.4, p = 0.008), and ischial angle > 127° (OR 5.9, 1.3-27.1, p = 0.023) as independent predictors of IFI. Labral tears were highly prevalent in both IFI and control groups (87% vs 89%, p = 0.732); cartilage lesions were less common in the IFI group (26% vs 52%, p = 0.027).</p><p><strong>Conclusion: </strong>IFI was present in 9% of young patients evaluated for joint-preserving surgery, associated with female sex, high femoral torsion and increased ischial angle. The comparable prevalence of labral lesions but lower prevalence of cartilage damage suggests complex relationships between extra- and intra-articular pathologies.</p><p><strong>Critical relevance statement: </strong>Recognizing IFI and its link to hip deformities and chondrolabral damage is crucial for clinicians, as it represents an important differential diagnosis, directly impacting joint-preserving treatment strategies in young adults with hip pain.</p><p><strong>Key points: </strong>The prevalence and imaging predictors of IFI in young patients remain unknown. IFI occurred in 9%, with predictors including female sex, high femoral torsion, and an increased ischial angle. IFI is an important differential diagnosis in joint-preserving hip surgery.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"78"},"PeriodicalIF":4.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788117","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":"Should all trainees \"do research\"?","authors":"Steve Halligan, Stuart Taylor","doi":"10.1186/s13244-025-01940-8","DOIUrl":"10.1186/s13244-025-01940-8","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"79"},"PeriodicalIF":4.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788280","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":"The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.","authors":"Yixin Wang, Zongtao Hu, Hongzhi Wang","doi":"10.1186/s13244-025-01950-6","DOIUrl":"10.1186/s13244-025-01950-6","url":null,"abstract":"<p><p>Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a 'black box' due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss the current challenges and prospects concerning the interpretability of brain tumor radiomics. Enhancing the interpretability of radiomics may make it more understandable for physicians, ultimately facilitating its translation into clinical practice. CRITICAL RELEVANCE STATEMENT: The interpretability of brain tumor radiomics empowers neuro-oncologists to make well-informed decisions from radiomic models. KEY POINTS: Radiomics makes a significant impact on the management of brain tumors in several key clinical areas. Transparent models, habitat analysis, and feature attribute explanations can enhance the interpretability of traditional handcrafted-feature radiomics in brain tumors. Various interpretability methods have been applied to explain deep learning-based models; however, there is a lack of biological mechanisms underlying these models.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"77"},"PeriodicalIF":4.1,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752421","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":"Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.","authors":"Junjian Shen, Qing Li, Lei Li, Tianyu Lu, Jun Han, Zongyu Xie, Peng Wang, Zirui Cao, Mengsu Zeng, Jianjun Zhou, Tianzhu Yu, Yaolin Xu, Haitao Sun","doi":"10.1186/s13244-025-01956-0","DOIUrl":"10.1186/s13244-025-01956-0","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a contrast-enhanced MRI-based intratumoral heterogeneity (ITH) model for predicting lymph node (LN) metastasis in resectable pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Methods: </strong>Lesions were encoded into different habitats based on enhancement ratios at arterial, venous, and delayed phases of contrast-enhanced MRI. Habitat models on enhanced ratio mapping and single sequences, radiomic models, and clinical models were developed for evaluating LN metastasis. The performance of the models was evaluated via different metrics. Additionally, patients were stratified into high-risk and low-risk groups based on an ensembled model to assess prognosis after adjuvant therapy.</p><p><strong>Results: </strong>We developed an ensembled radiomics-habitat-clinical (RHC) model that integrates radiomics, habitat, and clinical data for precise prediction of LN metastasis in PDAC. The RHC model showed strong predictive performance, with area under the curve (AUC) values of 0.805, 0.779, and 0.615 in the derivation, internal validation, and external validation cohorts, respectively. Using an optimal threshold of 0.46, the model effectively stratified patients, revealing significant differences in recurrence-free survival and overall survival (OS) (p = 0.004 and p < 0.001). Adjuvant therapy improved OS in the high-risk group (p = 0.004), but no significant benefit was observed in the low-risk group (p = 0.069).</p><p><strong>Conclusion: </strong>We developed an MRI-based ITH model that provides reliable estimates of LN metastasis for resectable PDAC and may offer additional value in guiding clinical decision-making.</p><p><strong>Critical relevance statement: </strong>This ensemble RHC model facilitates preoperative prediction of LN metastasis in resectable PDAC using contrast-enhanced MRI. This offers a foundation for enhanced prognostic assessment and supports the management of personalized adjuvant treatment strategies.</p><p><strong>Key points: </strong>MRI-based habitat models can predict LN metastasis in PDAC. Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC. The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"76"},"PeriodicalIF":4.1,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752418","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":"A novel deep learning radiopathomics model for predicting carcinogenesis promotor cyclooxygenase-2 expression in common bile duct in children with pancreaticobiliary maljunction: a multicenter study.","authors":"Hui-Min Mao, Jian-Jun Zhang, Bin Zhu, Wan-Liang Guo","doi":"10.1186/s13244-025-01951-5","DOIUrl":"10.1186/s13244-025-01951-5","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a deep learning radiopathomics model (DLRPM) integrating radiological and pathological imaging data to predict biliary cyclooxygenase-2 (COX-2) expression in children with pancreaticobiliary maljunction (PBM), and to compare its performance with single-modality radiomics, deep learning radiomics (DLR), and pathomics models.</p><p><strong>Methods: </strong>This retrospective study included 219 PBM patients, divided into a training set (n = 104; median age, 2.8 years, 75.0% females) and internal test set (n = 71; median age, 2.2 years, 83.1% females) from center I, and an external test set (n = 44; median age, 3.4 years, 65.9% females) from center II. Biliary COX-2 expression was detected using immunohistochemistry. Radiomics, DLR, and pathomics features were extracted from portal venous-phase CT images and H&E-stained histopathological slides, respectively, to build individual single-modality models. These were then integrated to develop the DLRPM, combining three predictive signatures. Model performance was evaluated using AUC, net reclassification index (NRI, for assessing improvement in correct classification) and integrated discrimination improvement (IDI).</p><p><strong>Results: </strong>The DLRPM demonstrated the highest performance, with AUCs of 0.851 (95% CI, 0.759-0.942) in internal test set and 0.841 (95% CI, 0.721-0.960) in external test set. In comparison, AUCs for the radiomics, DLR, and pathomics models were 0.532-0.602, 0.658-0.660, and 0.787-0.805, respectively. The DLRPM significantly outperformed three single-modality models, as demonstrated by the NRI and IDI tests (all p < 0.05).</p><p><strong>Conclusion: </strong>The multimodal DLRPM could accurately and robustly predict COX-2 expression, facilitating risk stratification and personalized postoperative management in PBM. However, prospective multicenter studies with larger cohorts are needed to further validate its generalizability.</p><p><strong>Critical relevance statement: </strong>Our proposed deep learning radiopathomics model, integrating CT and histopathological images, provides a novel and cost-effective approach to accurately predict biliary cyclooxygenase-2 expression, potentially advancing individualized risk stratification and improving long-term outcomes for pediatric patients with pancreaticobiliary maljunction.</p><p><strong>Key points: </strong>Predicting biliary COX-2 expression in pancreaticobiliary maljunction (PBM) is critical but challenging. A deep learning radiopathomics model achieved high predictive accuracy for COX-2. The model supports patient stratification and personalized postoperative management in PBM.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"74"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718672","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":"Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.","authors":"José Guilherme de Almeida, Ana Sofia Castro Verde, Carlos Bilreiro, Inês Santiago, Joana Ip, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Celso Matos, Nickolas Papanikolaou","doi":"10.1186/s13244-025-01938-2","DOIUrl":"10.1186/s13244-025-01938-2","url":null,"abstract":"<p><strong>Objectives: </strong>To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML.</p><p><strong>Methods: </strong>Retrospective prostate mpMRI studies were classified into 5 series types-T2-weighted (T2W), diffusion-weighted images (DWI), apparent diffusion coefficients (ADC), dynamic contrast-enhanced (DCE) and other series types (others). Metadata was processed for all series and two models were trained (XGBoost after custom categorical tokenization and CatBoost with raw categorical data) using 5-fold cross-validation (CV) with different data fractions for learning curve analyses. For validation, two test sets-hold-out test set and temporal split-were used. A leave-one-group-out (LOGO) CV analysis was performed with centres as groups to understand the effect of dataset-specific data.</p><p><strong>Results: </strong>4045 studies (31,053 series) and 1004 studies (7891 series) from 11 centres were used to train and test series identification models, respectively. Test F1-scores were consistently above 0.95 (CatBoost) and 0.97 (XGBoost). Learning curves demonstrate learning saturation, while temporal validation shows model remain capable of correctly identifying all T2W/DWI/ADC triplets. However, optimal performance requires centre-specific data-controlling for model and used feature sets when comparing CV with LOGOCV, F1-score dropped for T2W, DCE and others (-0.146, -0.181 and -0.179, respectively), with larger performance decreases for CatBoost (-0.265). Finally, we delineate heuristics to assist researchers in series classification for PCa mpMRI datasets.</p><p><strong>Conclusions: </strong>Automatic series-type identification is feasible and can enable automated data curation. However, dataset-specific data should be included to achieve optimal performance.</p><p><strong>Critical relevance statement: </strong>Organising large collections of data is time-consuming but necessary to train clinical machine-learning models. To address this, we outline and validate an automatic series identification method that can facilitate this process. Finally, we outline a set of metadata-based heuristics that can be used to further automate series-type identification.</p><p><strong>Key points: </strong>Multi-centric prostate MRI studies were used for sequence annotation model training. Automatic sequence annotation requires few instances and generalises temporally. Sequence annotation, necessary for clinical AI model training, can be performed automatically.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"75"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12187622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718676","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":"CT acquisition protocols for lung cancer screening-current landscape and the urgent need for consistency.","authors":"Mathis Franz Georg Konrad, Emily Nischwitz, Aad van der Lugt, Gudrun Zahlmann, Viktoria Palm, Joanna Chorostowska-Wynimko, Helmut Prosch, James L Mulshine, Hans-Ulrich Kauczor","doi":"10.1186/s13244-025-01949-z","DOIUrl":"10.1186/s13244-025-01949-z","url":null,"abstract":"<p><strong>Key points: </strong>Standardizing CT acquisition protocols reduces radiation exposure in lung cancer screening. Cross-continent collaboration will enhance understanding of diverse clinical practices. Survey results will inform future advancements in radiology sustainability efforts.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"72"},"PeriodicalIF":4.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729861","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":"On-call or not on-call, what difference does it make in paediatric radiology?","authors":"Willemijn M Klein, Amaka C Offiah, Ola Kvist, Karen Rosendahl","doi":"10.1186/s13244-025-01948-0","DOIUrl":"10.1186/s13244-025-01948-0","url":null,"abstract":"<p><strong>Objectives: </strong>There is an ever-increasing demand for out-of-hours expert opinion in paediatric radiology, which cannot be delivered in all hospitals. This study was designed to ascertain whether paediatricians, paediatric surgeons and radiologists are satisfied with the current situation; and to investigate the extent to which diagnostic errors are made while on-call with either residents, general or paediatric radiologists reporting on paediatric examinations.</p><p><strong>Methods: </strong>Two surveys were compiled and dispatched. The first, is to paediatricians, paediatric surgeons and paediatric radiologists questioning their satisfaction with the current on-call paediatric radiology services in their hospitals. The second, is to paediatric radiologists inviting them to retrospectively score the accuracy of the reporting on consecutive paediatric radiology examinations performed during on-call hours in their hospitals.</p><p><strong>Results: </strong>The first survey revealed that 40/49 (82%) paediatric physicians were satisfied with the paediatric radiology service during office hours, decreasing to 33% during on-call hours. In the second survey, a total of 464 on-call paediatric radiology examinations were analysed, demonstrating 20.2% misdiagnoses. General radiologists had more misdiagnoses and were slower in providing a report than residents.</p><p><strong>Conclusion: </strong>The current service with a lack of on-call paediatric radiologists, is associated with increased misdiagnoses and dissatisfaction among physicians and requires improvement.</p><p><strong>Critical relevance statement: </strong>This study shows that it may be a struggle to organise the 24-h availability of an expert paediatric radiologist, yet this might avoid 20% of misdiagnoses, half of which have direct clinical consequences.</p><p><strong>Key points: </strong>The current organisation of paediatric radiology on-call rotas is unsatisfactory for many clinicians. A substantial amount of on-call paediatric radiology reports contain misdiagnoses, and these may have significant clinical consequences. Hospitals should reconfigure out-of-hours paediatric radiology covers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"73"},"PeriodicalIF":4.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718708","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":"Bronchial wall T2w MRI signal as a new imaging biomarker of severe asthma.","authors":"Ilyes Benlala, Gaël Dournes, Pierre-Olivier Girodet, François Laurent, Wadie Ben Hassen, Fabien Baldacci, Baudouin Denis De Senneville, Patrick Berger","doi":"10.1186/s13244-025-01939-1","DOIUrl":"10.1186/s13244-025-01939-1","url":null,"abstract":"<p><strong>Objectives: </strong>Severe asthma patients are prone to severe exacerbations with a need of hospital admission increasing the economic burden on healthcare systems. T2w lung MRI was found to be useful in the assessment of bronchial inflammation. The main goal of this study is to compare quantitative MRI T2 signal bronchial intensity between patients with severe and non-severe asthma.</p><p><strong>Methods: </strong>This is an ancillary study of a prospective single-center study (NCT03089346). We assessed the mean T2 intensity MRI signal of the bronchial wall area (BrWall_T2-MIS) in 15 severe and 15 age and sex-matched non-severe asthmatic patients. They also have had pulmonary function tests (PFTs), fractional exhaled nitric oxide (FeNO) and blood eosinophils count (Eos). Comparisons between the two groups were performed using Student's t-test. Correlations were assessed using Pearson coefficients. Reproducibility was assessed using intraclass correlation coefficient and Bland-Altman analysis.</p><p><strong>Results: </strong>BrWall_T2-MIS was higher in severe than in non-severe asthma patients (74 ± 12 vs 49 ± 14; respectively p < 0.001). BrWall_T2-MIS showed a moderate inverse correlation with PFTs in the whole cohort (r = -0.54, r = -0.44 for FEV1(%pred) and FEV1/FVC respectively, p ≤ 0.01) and in the severe asthma group (r = -0.53, r = -0.44 for FEV1(%pred) and FEV1/FVC respectively, p ≤ 0.01). Eos was moderately correlated with BrWall_T2-MIS in severe asthma group (r = 0.52, p = 0.047). Reproducibility was almost perfect with ICC = 0.99 and mean difference in Bland-Altman analysis of -0.15 [95% CI = -0.48-0.16].</p><p><strong>Conclusion: </strong>Quantification of bronchial wall T2w signal intensity appears to be able to differentiate severe from non-severe asthma and correlates with obstructive PFTs' parameters and inflammatory markers in severe asthma.</p><p><strong>Critical relevance statement: </strong>The development of non-ionizing imaging biomarkers could play an essential role in the management of patients with severe asthma in the current era of biological therapies.</p><p><strong>Key points: </strong>Severe asthma exhibits severe exacerbations with a high burden on healthcare systems. T2w bronchial wall signal intensity is related to inflammatory biomarker in severe asthma. T2w MRI may represent a non-invasive tool to follow up severe asthma patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"71"},"PeriodicalIF":4.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709778","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}