Insights into Imaging最新文献

{"title":"Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study.","authors":"Yafang Zhang, Shilin Lu, Chuan Peng, Shichong Zhou, Irene Campo, Michele Bertolotto, Qian Li, Zhiyuan Wang, Dong Xu, Yun Wang, Jinshun Xu, Qinfu Wu, Xiaoying Hu, Wei Zheng, Jianhua Zhou","doi":"10.1186/s13244-025-02045-y","DOIUrl":"10.1186/s13244-025-02045-y","url":null,"abstract":"<p><strong>Objectives: </strong>Subvariants of testicular germ cell tumor (TGCT) significantly affect therapeutic strategies and patient prognosis. However, preoperatively distinguishing seminoma (SE) from non-seminoma (n-SE) remains a challenge. This study aimed to evaluate the performance of a deep learning-based super-resolution (SR) US radiomics model for SE/n-SE differentiation.</p><p><strong>Materials and methods: </strong>This international multicenter retrospective study recruited patients with confirmed TGCT between 2015 and 2023. A pre-trained SR reconstruction algorithm was applied to enhance native resolution (NR) images. NR and SR radiomics models were constructed, and the superior model was then integrated with clinical features to construct clinical-radiomics models. Diagnostic performance was evaluated by ROC analysis (AUC) and compared with radiologists' assessments using the DeLong test.</p><p><strong>Results: </strong>A total of 486 male patients were enrolled for training (n = 338), domestic (n = 92), and international (n = 59) validation sets. The SR radiomics model achieved AUCs of 0.90, 0.82, and 0.91, respectively, in the training, domestic, and international validation sets, significantly surpassing the NR model (p < 0.001, p = 0.031, and p = 0.001, respectively). The clinical-radiomics model exhibited a significantly higher across both domestic and international validation sets compared to the SR radiomics model alone (0.95 vs 0.82, p = 0.004; 0.97 vs 0.91, p = 0.031). Moreover, the clinical-radiomics model surpassed the performance of experienced radiologists in both domestic (AUC, 0.95 vs 0.85, p = 0.012) and international (AUC, 0.97 vs 0.77, p < 0.001) validation cohorts.</p><p><strong>Conclusions: </strong>The SR-based clinical-radiomics model can effectively differentiate between SE and n-SE.</p><p><strong>Critical relevance statement: </strong>This international multicenter study demonstrated that a radiomics model of deep learning-based SR reconstructed US images enabled effective differentiation between SE and n-SE.</p><p><strong>Key points: </strong>Clinical parameters and radiologists' assessments exhibit limited diagnostic accuracy for SE/n-SE differentiation in TGCT. Based on scrotal US images of TGCT, the SR radiomics models performed better than the NR radiomics models. The SR-based clinical-radiomics model outperforms both the radiomics model and radiologists' assessment, enabling accurate, non-invasive preoperative differentiation between SE and n-SE.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"165"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764888","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":"An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer.","authors":"Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen","doi":"10.1186/s13244-025-02009-2","DOIUrl":"10.1186/s13244-025-02009-2","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop an interpretable 3-year disease-free survival risk prediction tool to stratify patients with stage II colorectal cancer (CRC) by integrating CT images and clinicopathological factors.</p><p><strong>Methods: </strong>A total of 769 patients with pathologically confirmed stage II CRC and disease-free survival (DFS) follow-up information were recruited from three medical centers and divided into training (n = 442), test (n = 190), and validation cohorts (n = 137). CT-based tumor radiomics features were extracted, selected, and used to calculate a Radscore. A combined model was developed using artificial neural network (ANN) algorithm, by integrating the Radscore with significant clinicoradiological factors to classify patients into high- and low-risk groups. Model performance was assessed using the area under the curve (AUC), and feature contributions were qualified using the Shapley additive explanation (SHAP) algorithm. Kaplan-Meier survival analysis revealed the prognostic stratification value of the risk groups.</p><p><strong>Results: </strong>Fourteen radiomics features and five clinicoradiological factors were selected to construct the radiomics and clinicoradiological models, respectively. The combined model demonstrated optimal performance, with AUCs of 0.811 and 0.846 in the test and validation cohorts, respectively. Kaplan-Meier curves confirmed effective patient stratification (p < 0.001) in both test and validation cohorts. A high Radscore, rough intestinal outer edge, and advanced age were identified as key prognostic risk factors using the SHAP.</p><p><strong>Conclusion: </strong>The combined model effectively stratified patients with stage II CRC into different prognostic risk groups, aiding clinical decision-making.</p><p><strong>Critical relevance statement: </strong>Integrating CT images with clinicopathological information can facilitate the identification of patients with stage II CRC who are most likely to benefit from adjuvant chemotherapy.</p><p><strong>Key points: </strong>The effectiveness of adjuvant chemotherapy for stage II colorectal cancer remains debated. A combined model successfully identified high-risk stage II colorectal cancer patients. Shapley additive explanations enhance the interpretability of the model's predictions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"162"},"PeriodicalIF":4.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144760013","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":"Individualized prediction of post-acute pancreatitis diabetes mellitus by combining lipid metabolism and anatomical features.","authors":"Ling Ling Tang, Qi Zhang, Shuang Yi Song, Nian Liu, Qing Lin Du, Shu Ting Zhong, Xiao Hua Huang","doi":"10.1186/s13244-025-02039-w","DOIUrl":"10.1186/s13244-025-02039-w","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the lipid metabolism and anatomical risk factors of post-acute pancreatitis diabetes mellitus (PPDM) and their value in individualized prediction.</p><p><strong>Materials and methods: </strong>A continuous retrospective analysis was conducted on 241 patients with acute pancreatitis (AP) treated in our hospital from January 2017 to December 2021. The type and angle of the pancreaticobiliary junction were measured on magnetic resonance cholangiopancreatography (MRCP) images, and baseline lipid metabolism indicators were collected. We evaluated the risk factors of PPDM using univariate and multivariate Cox proportional hazard analysis, established quantitative prediction models for PPDM, and evaluated the predictive value of lipid metabolism and features of the pancreaticobiliary junction.</p><p><strong>Results: </strong>Overall, 85 of 241 eligible patients (35.27%) ultimately developed PPDM. Univariate and multivariate analyses showed B-P type in pancreaticobiliary junction (p = 0.017), the angle of junction (p = 0.041), non-high-density lipoprotein (p = 0.029), alcohol index (p < 0.001), body mass index (p = 0.042), inflammatory frequency (p = 0.016), fasting blood glucose (p = 0.002), concomitant hypertension (p < 0.001) were important predictive factors for the occurrence of PPDM. The model that integrated imaging features of the pancreaticobiliary junction has a higher predictive performance than models without imaging features, with an AUC of 0.882 (95% CI, 0.836-0.930). The AUC of the combined model was 0.886 (95% CI, 0.841-0.932), and there was no statistical difference in AUC between the combined model and the pancreaticobiliary junction model (p = 0.340).</p><p><strong>Conclusion: </strong>The lipid metabolism and morphological characteristics of the pancreaticobiliary junction are additional risk factors for PPDM, and the quantitative prediction model shows moderate predictive performance.</p><p><strong>Critical relevance statement: </strong>The type and angle of the pancreaticobiliary junction based on MRCP are independent predictors of PPDM, which can quantitatively predict risk in the early stage.</p><p><strong>Key points: </strong>PPDM has an increasing incidence and poor prognosis, which requires early monitoring. Larger angles and B-P type in the pancreaticobiliary junction are risk factors for PPDM. Quantitative prediction of PPDM risk allows for early personalized prevention and treatment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"161"},"PeriodicalIF":4.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144760015","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 colonography: revisited after 30 years.","authors":"Mehdi Cadi, Charles André Cuenod, Philippe A Grenier, Aurélien Saltel, Mohamed Abdulbaki, Gilles Manceau, Mehdi Karoui","doi":"10.1186/s13244-025-02038-x","DOIUrl":"10.1186/s13244-025-02038-x","url":null,"abstract":"<p><p>Computed tomography colonography (CTC), also known as virtual colonoscopy, is a well-tolerated, minimally invasive and effective procedure. Used for over two decades and supported by extensive studies and meta-analyses, CTC has demonstrated performance comparable to that of optical colonoscopy (OC). However, CTC remains generally underutilized in many countries, including the United States of America; in contrast, in some countries, such as the United Kingdom, it is widely used. CTC requires bowel preparation with laxative and fecal contrast-agent tagging, followed by colonic distension with low-pressure, automated, CO₂ insufflation. It enables detailed image analysis with postprocessing software and is highly sensitive and specific for detecting cancers and significant benign precursors ≥ 10 mm (adenomatous and sessile-serrated polyps) years before potential malignant transformation. After reviewing the state of the art of CTC acquisition, analysis and reporting, we wrote this article to update the new, potential and emerging CTC indications. CTC is increasingly used after incomplete OC, for undetermined colonic anomalies, in elderly and/or fragile patients or when OC is refused. Recent routine clinical use has broadened CTC's applications, proving its usefulness in local colon-cancer staging, preoperative laparoscopic surgery planning, and selecting patients with severe diverticular disease for elective sigmoidectomy. CRITICAL RELEVANCE STATEMENT: Beyond its excellent performance in detecting advanced adenomas and cancers, CTC provides precise staging of locally advanced tumors, guiding decisions on neoadjuvant therapy, and coupled with contrast-enhanced thoracic-abdominal-pelvic acquisition, enables comprehensive, preoperative evaluation for laparoscopic colectomy. KEY POINTS: CT colonography (CTC) and optical colonoscopy (OC) are similarly able to detect advanced adenomas (≥ 10 mm) and early-stage colorectal cancer. CTC enables a \"one-stop shop\" examination for laparoscopic surgery planning, with precise localization and detailed vascular mesenteric mapping. With the rise of neoadjuvant treatments for advanced colorectal cancer, CTC may become pivotal in radiological staging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"160"},"PeriodicalIF":4.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144760014","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 value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists.","authors":"Junjie Jin, Xijia Deng, Ling Long, Meiling Liu, Meimei Cao, Hao Gong, Huan Liu, Xiaosong Lan, Lili Liu, Jiuquan Zhang","doi":"10.1186/s13244-025-02047-w","DOIUrl":"10.1186/s13244-025-02047-w","url":null,"abstract":"<p><strong>Objectives: </strong>To develop an MRI-based radiomics model for ovarian masses categorization and to compare the model performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and radiologists' assessments.</p><p><strong>Materials and methods: </strong>This retrospective multicenter study included 497 patients (249 benign, 248 malignant) allocated to training, internal, and external validation sets (293/124/80 masses, respectively). Radiomics features were extracted from preoperative MRI. Features were selected using minimum redundancy, maximum relevance, and the least absolute shrinkage and selection operator algorithm. Diagnostic performance of the radiomics model, O-RADS, and independent assessments by junior and senior radiologists was evaluated via the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.</p><p><strong>Results: </strong>In external validation, the radiomics model (AUC = 0.939) outperformed O-RADS (AUC = 0.862; p = 0.047) and the junior radiologist (AUC = 0.802; p = 0.003) and was similar to the senior radiologist (AUC = 0.886; p = 0.231). Subgroup analysis of O-RADS score 4 showed the model (AUC = 0.879) outperformed both radiologists (junior: p = 0.001; senior: p = 0.005). For solid, cystic-solids, and cystic masses, the AUCs of the model were 0.921, 0.975, and 0.848, respectively.</p><p><strong>Conclusions: </strong>The performance of the radiomics model to categorize ovarian masses was superior to O-RADS and junior radiologists and similar to senior radiologists. As a complementary tool to O-RADS, it allows for refined risk stratification for ovarian masses with an O-RADS score of 4 and different morphological characteristics, providing clinicians with quantitative decision support to improve preoperative diagnosis and guide treatment planning.</p><p><strong>Critical relevance statement: </strong>Radiomics model provides improved risk stratification and supports precise clinical decision-making for ovarian masses with an O-RADS score of 4 and solid, cystic-solid ovarian masses, thereby improving the management of patients with ovarian masses.</p><p><strong>Key points: </strong>MRI-based radiomics allows for the characterization of ovarian masses with high accuracy. Radiomics helps differentiate between benign and malignant ovarian masses with an O-RADS score of 4. For solid, cystic-solid, and cystic masses, the radiomics model exhibited higher or similar performance to that of the O-RADS and radiologists.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"163"},"PeriodicalIF":4.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12314133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144760016","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":"Paediatric cranial ultrasound: assessment of the preterm brain.","authors":"Caoilfhionn Ní Leidhin, Michael Paddock, Paul M Parizel, Richard R Warne, Peter Shipman, Rahul Lakshmanan","doi":"10.1186/s13244-025-02030-5","DOIUrl":"10.1186/s13244-025-02030-5","url":null,"abstract":"<p><p>Cranial ultrasound is an invaluable tool in assessing neonatal brain anatomy and pathology. It is accessible, relatively quick, inexpensive, safe, portable and generally well-tolerated. This pictorial review focuses on the use of cranial ultrasound in evaluating the premature brain. We illustrate the different grades of intraventricular haemorrhage, the most common sequela of prematurity, its evolution and potential complications, as well as periventricular leukomalacia. Anatomical variants and benign findings that mimic preterm brain injury are also discussed. CRITICAL RELEVANCE STATEMENT: Cranial US is an invaluable tool for assessing neonatal brain anatomy and pathology and can be used in preterm infants to diagnose, monitor and assess for complications of intraventricular haemorrhage and periventricular leukomalacia. KEY POINTS: Cranial US (CUS) is an invaluable tool for assessing the neonatal brain and has many advantages over MRI. CUS can detect intraventricular haemorrhage and periventricular leukomalacia, the most important sequelae of prematurity. Knowledge of optimal CUS technique, normal anatomy, and variants/benign sonographic findings that mimic pathology is crucial to avoid misdiagnosis and unnecessary concern.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"158"},"PeriodicalIF":4.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690103","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":"Paediatric cranial ultrasound: abnormalities of the brain in term neonates and young infants.","authors":"Caoilfhionn Ní Leidhin, Michael Paddock, Paul M Parizel, Richard R Warne, Peter Shipman, Rahul Lakshmanan","doi":"10.1186/s13244-025-02031-4","DOIUrl":"10.1186/s13244-025-02031-4","url":null,"abstract":"<p><p>Cranial ultrasound is a critical screening tool in the detection of cerebral abnormalities in term neonates and infants, and is complementary to other imaging modalities. This pictorial review illustrates the diverse central nervous system pathologies which can affect the term neonatal and infantile brain, including vascular abnormalities (hypoxic ischaemic injury, perinatal arterial ischaemic stroke, cerebral sinovenous thrombosis, vein of Galen aneurysmal malformations, subpial haemorrhage, and dural sinus malformations); infections (congenital (cytomegalovirus and toxoplasmosis) and bacterial meningoencephalitis); genetic disorders and malformations (callosal agenesis, tuberous sclerosis, developmental megalencephaly, lissencephaly-pachygyria, and grey matter heterotopia); tumours (choroid plexus papilloma, atypical teratoid/rhabdoid tumour, and desmoplastic infantile glioma) and trauma (birth-related, inflicted injury). Each condition is explored with a focus on its sonographic characteristics-some have rarely, if ever, been described on ultrasound. CRITICAL RELEVANCE STATEMENT: Through this case review, we illustrate various pathologies affecting the term neonatal and infantile brain, including vascular lesions, infection, genetic disorders/malformations, tumours and trauma: some of these pathologies have rarely, if ever, been described on CUS. KEY POINTS: Cranial ultrasound (CUS) is a critical screening tool for the term brain. Many term neonatal and infantile pathologies can be detected on CUS. Some of the pathologies illustrated in this paper have rarely been described on US.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"159"},"PeriodicalIF":4.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690102","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":"Exploring the key clinical and CT characteristics of granulomas mimicking peripheral lung cancers: a case-control study.","authors":"Hong-Bo Xu, Can Ding, Min Zhao, Fa-Jin Lv, Zhi-Gang Chu","doi":"10.1186/s13244-025-02043-0","DOIUrl":"10.1186/s13244-025-02043-0","url":null,"abstract":"<p><strong>Objectives: </strong>Some granulomas exhibit CT manifestations similar to those of peripheral lung cancers (PLCs), often resulting in misdiagnosis. This study aimed to identify the key clinical and CT indicators for differentiating them.</p><p><strong>Materials and methods: </strong>From October 2019 to July 2024, 204 atypical granulomas (no calcification, satellite lesions, and/or halo sign) and 204 size-matched PLCs manifested as solid nodules (SNs) were retrospectively enrolled. Patients' clinical, as well as non-enhanced and contrast-enhanced CT data, were evaluated and compared. The enhancement patterns of lesions included no significant enhancement (▵CT value < 15 HU), rim enhancement, enhancement with well-defined necrosis, heterogeneous enhancement, and homogeneous enhancement. The latter two patterns were further divided into mild (15-29 HU), moderate (30-59 HU), and severe (≥ 60 HU) enhancement.</p><p><strong>Results: </strong>Multivariate analysis revealed that younger age (≤ 63 years) (odds ratio [OR], 5.237; 95% confidence interval [CI], 2.609-10.509; p < 0.001), history of diabetes (OR, 9.097; 95% CI: 3.056-27.077; p < 0.001), irregular shape (OR, 3.603; 95% CI: 1.594-8.142; p = 0.002), lower non-enhanced CT value (≤ 21 HU) (OR, 7.576; 95% CI: 3.720-15.431; p < 0.001), and non-moderate enhancement patterns (OR, 50.065; 95% CI: 20.293-123.517; p < 0.001) were independent predictors of granulomas. The sensitivity, specificity, and area under the curve of this model were 88.7%, 83.8%, and 0.941 (95% CI: 0.919-0.962) (p < 0.001), respectively.</p><p><strong>Conclusions: </strong>In younger (≤ 63 years) patients with diabetes, an irregular SN displaying lower density (≤ 21 HU) in non-enhanced CT and a non-moderate enhancement pattern should first be considered as a granuloma.</p><p><strong>Clinical relevance statement: </strong>Distinguishing atypical granulomas from PLCs can be effectively achieved by evaluating the patient's age, underlying diseases, and the lesion's shape, non-enhanced CT value, and enhancement pattern. This integrated clinical-CT diagnostic approach could provide crucial insights for guiding subsequent clinical management.</p><p><strong>Key points: </strong>Atypical granulomas and PLCs exhibit high morphological similarity. Enhancement patterns of lesions are crucial for differentiating atypical granulomas and PLCs. Atypical granulomas typically display irregular shape, lower non-enhanced CT value, and non-moderate enhancement pattern. Younger age and a history of diabetes are key clinical indicators of granulomas.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"157"},"PeriodicalIF":4.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12276186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144667545","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":"Current practices and perceptions on diagnostic reference levels: a EuroSafe Imaging Survey Analysis.","authors":"John Damilakis, Boris Brkljacic, Guy Frija, Timo De Bondt, Graciano Paulo, Virginia Tsapaki, Eliseo Vano","doi":"10.1186/s13244-025-02028-z","DOIUrl":"10.1186/s13244-025-02028-z","url":null,"abstract":"<p><p>Despite progress in implementing diagnostic reference levels (DRLs) across Europe, clinical practices remain variable. This prompts the EuroSafe Imaging campaign to conduct a survey assessing current practices, perceptions, and challenges related to DRLs. A total of 146 responses were collected from radiology departments in 38 countries, predominantly in the EU/EEA region. While 52.4% reported established local DRLs, significant gaps were identified, with 34.5% lacking local DRLs and 13.1% unaware of their existence. DRLs were primarily established for computed tomography (CT) (88.7%) and conventional radiography (77.5%), with lower implementation in interventional radiology (36.6%). Key challenges included time constraints, data collection difficulties, and limited standardization across institutions. Education gaps were notable, with less than half of the respondents reporting DRL-related training for radiology residents. Respondents emphasized the need for dose management systems, personalized DRLs based on clinical indications, and enhanced education and policy support. Addressing barriers through targeted training, policy enhancements, and technological innovations can improve DRL implementation. Future efforts should focus on promoting standardized clinical protocols, increasing awareness, and fostering European and international collaboration to ensure the consistent use and optimization of DRLs in clinical practice. CRITICAL RELEVANCE STATEMENT: The article critically examines the variability and challenges in implementing diagnostic reference levels (DRLs) across European radiology departments, providing actionable recommendations on policy, education, and technological advancements to optimize radiation protection and improve clinical radiology practices. Diagnostic reference levels (DRLs) help healthcare providers ensure that radiation doses from medical imaging, like CT scans and X-rays, are not higher than necessary. This study looked at how DRLs are used across Europe. It found that while many hospitals have established and follow DRLs, others do not, which may affect patient safety. Challenges like time constraints and lack of training prevent better use of DRLs. Improving education for medical staff and updating protocols can help protect patients by reducing unnecessary radiation exposure while still ensuring accurate diagnoses. KEY POINTS: Variability persists in diagnostic reference level (DRL) practices across Europe. Over half of radiology departments have established local DRLs. Less than half of radiology residents receive structured DRL training. Improved DRL adoption can optimize radiation protection and patient safety. Collaboration, training, and standardized protocols are essential for better DRL practices.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"156"},"PeriodicalIF":4.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659132","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":"ECR 2025 Book of Abstracts.","authors":"","doi":"10.1186/s13244-025-02003-8","DOIUrl":"10.1186/s13244-025-02003-8","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 Suppl 1","pages":"147"},"PeriodicalIF":4.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659133","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}