Insights into Imaging最新文献

{"title":"Charting a sustainable future in radiology: evaluating radiologists' knowledge, attitudes, and practices toward environmental responsibility.","authors":"Mohamed M Abuzaid, Nora Almuqbil","doi":"10.1186/s13244-025-01917-7","DOIUrl":"10.1186/s13244-025-01917-7","url":null,"abstract":"<p><strong>Objectives: </strong>This study assesses radiologists' knowledge, attitudes, and practices regarding healthcare sustainability. With radiology's substantial environmental impact, sustainable practices are crucial to reduce energy use, waste, and resource depletion. The study evaluates radiologists' awareness, engagement, and perceived barriers to sustainable practices in the UAE, identifying areas for improvement and intervention.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted among UAE radiologists in hospitals, clinics, and medical centers from August to October 2024. Developed and piloted by the research team, the survey addressed demographic details, sustainability knowledge, attitudes, current practices, and implementation barriers. Convenience sampling yielded 111 responses, analyzed using descriptive and inferential statistics to identify trends and associations.</p><p><strong>Results: </strong>The findings indicate moderate knowledge levels, with 31.8% of radiologists agreeing to understand sustainability concepts. While 36.4% strongly agreed on its importance, only 20% felt it was prioritized at their workplace. Key barriers included lack of training (40.5% agreed, 29.7% strongly agreed) and insufficient financial support (37.8% agreed, 25.2% strongly agreed). Digital documentation and waste-reduction practices were observed but varied in consistency.</p><p><strong>Conclusion: </strong>Radiologists display a positive attitude toward sustainability but face significant implementation barriers, primarily in institutional support and resources. Addressing training gaps and increasing leadership commitment are essential to advancing sustainable practices. Future initiatives should emphasize policy support, education, and resource allocation to foster a sustainable radiology sector.</p><p><strong>Critical relevance statement: </strong>This article critically examines radiologists' knowledge, attitudes, and barriers to sustainable practices, highlighting the need for institutional support and targeted training to advance environmental responsibility and sustainable practices within clinical radiology.</p><p><strong>Key points: </strong>Radiologists support sustainability but lack knowledge of specific practices. Key challenges include limited training, support, and funding. Commitment, training, and resources are essential for sustainable radiology.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"39"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440703","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":"Exploratory high b value diffusion-weighted MR for quantitative differentiation of ileocecal inflammatory conditions and tumors.","authors":"Hao Yu, Yucheng Hai, Jingyu Lu","doi":"10.1186/s13244-025-01916-8","DOIUrl":"10.1186/s13244-025-01916-8","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the quantitative analysis of high b value (2000 s/mm²) diffusion-weighted imaging (DWI) for the differentiation of ileocecal inflammatory conditions and tumors, compared with conventional b value (800 s/mm²) DWI.</p><p><strong>Methods: </strong>Sixty-six patients with 30 tumors and 36 inflammatory conditions underwent MR enterography with conventional and high b values DWI. Quantitative apparent diffusion coefficient (ADC) values and signal intensity ratios (SIRs) of lesions of the psoas muscle were measured from the two b value DWIs. The receiver operating characteristic (ROC) curve was applied to determine the diagnostic value of ADC and SIR for differentiating tumors from inflammatory conditions.</p><p><strong>Results: </strong>The ADC values of tumors were significantly lower than those of inflammatory conditions in 800 s/mm² (p = 0.001) and 2000 s/mm² (p < 0.001) DWI. In addition, tumors exhibited significantly higher SIR values compared to inflammatory conditions (p < 0.001 in 800 s/mm² and 2000 s/mm² DWI). Areas under the curve (AUC) of ADC and SIR derived from high b value (0.828 for ADC, 0.947 for SIR) were superior to those from conventional b value DWI (0.731 and 0.849, respectively). Compared to ADC, SIR values achieved better AUCs in both two b values DWI.</p><p><strong>Conclusions: </strong>Quantitative ADC values and SIR could be used as non-invasive tools to distinguish ileocecal tumors from inflammatory conditions. The use of high b value DWI would improve this ability. Furthermore, SIR obtained from high b value DWI was the most promising quantitative parameter.</p><p><strong>Critical relevance statement: </strong>This study indicated that quantitative DWI parameters might be applied as non-invasive imaging biomarkers for distinguishing bowel tumors from inflammatory conditions. The SIR from high b value DWI could improve the differentiation, providing invaluable information for establishing appropriate therapeutic strategies.</p><p><strong>Key points: </strong>Differentiation between bowel inflammatory conditions and tumors is still a dilemma. Quantitative DWI contributed to distinguishing ileocecal tumors from inflammatory conditions. SIR from DWI is a promising parameter for differentiating these pathologies.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"41"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440720","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":"Tumour surface regularity predicts survival and benefit from gross total resection in IDH-wildtype glioblastoma patients.","authors":"Peng Lin, Jin-Shu Pang, Ya-Dan Lin, Qiong Qin, Jia-Yi Lv, Gui-Qian Zhou, Tian-Ming Tan, Wei-Jia Mo, Gang Chen","doi":"10.1186/s13244-025-01900-2","DOIUrl":"10.1186/s13244-025-01900-2","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the ability of sphericity in glioblastomas (GBMs) for predicting overall survival (OS) and the survival benefit from gross tumour resection (GTR).</p><p><strong>Methods: </strong>Preoperative MRI scans were retrospectively analysed in IDH-wildtype GBM patients from two datasets. After MRI preprocessing and tumour segmentation, tumour sphericity was calculated based on the tumour core region. The prognostic value of tumour surface regularity was evaluated via Kaplan-Meier (K-M) plots, univariate and multivariate Cox proportional hazards analyses. In different surface regularity subgroups, the OS benefit from GTR was evaluated via K-M plots and the restricted mean survival time (RMST).</p><p><strong>Results: </strong>This study included 367 patients (median age, 62.0 years [IQR, 54.5-70.5 years]) in the discovery cohort and 475 patients (median age, 63.6 years [IQR, 56.2-71.3 years]) in the validation cohort. Sphericity was an independent predictor of OS in the discovery (p = 0.022, hazard ratio (HR) = 1.45, 95% confidence interval (CI) 1.06-1.99) and validation groups (p = 0.007, HR = 1.38, 95% CI: 1.09-1.74) according to multivariate analysis. Age, extent of resection, and surface regularity composed a prognostic model that separated patients into subgroups with distinct prognoses. Patients in the surface-irregular subgroup benefited from GTR, but patients in the surface-regular subgroup did not in the discovery (p < 0.001 vs. p = 0.056) and validation datasets (p < 0.001 vs. p = 0.11).</p><p><strong>Conclusions: </strong>The high surface regularity of IDH-wildtype GBM is significantly correlated with better OS and does not benefit substantially from GTR.</p><p><strong>Critical relevance statement: </strong>The proposed imaging marker has the potential to increase the survival prediction efficacy for IDH-wildtype glioblastomas (GBMs), offering a valuable indicator for clinical decision-making.</p><p><strong>Key points: </strong>Sphericity is an independent prognostic factor in IDH-wildtype glioblastomas (GBMs). High sphericity in IDH-wildtype GBM is significantly correlated with better survival. GBM patients with low sphericity could receive survival benefits from gross tumour resection.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"42"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440709","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 machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival and recurrence risk of pancreatic ductal adenocarcinoma.","authors":"Chao Qu, Piaoe Zeng, Changlei Li, Weiyu Hu, Dongxia Yang, Hangyan Wang, Huishu Yuan, Jingyu Cao, Dianrong Xiu","doi":"10.1186/s13244-025-01915-9","DOIUrl":"10.1186/s13244-025-01915-9","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a machine learning (ML) model combining preoperative multiparametric diffusion-weighted imaging (DWI) and clinical features to better predict overall survival (OS) and recurrence-free survival (RFS) following radical surgery for pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on 234 PDAC patients who underwent radical resection at two centers. Among 101 ML models tested for predicting postoperative OS and RFS, the best-performing model was identified based on comprehensive evaluation metrics, including C-index, Brier scores, AUC curves, clinical decision curves, and calibration curves. This model's risk stratification capability was further validated using Kaplan-Meier survival analysis.</p><p><strong>Results: </strong>The random survival forest model achieved the highest C-index (0.828/0.723 for OS and 0.781/0.747 for RFS in training/validation cohorts). Incorporating nine key factors-D value, T-stage, ADC-value, postoperative 7th day CA19-9 level, AJCC stage, tumor differentiation, type of operation, tumor location, and age-optimized the model's predictive accuracy. The model had integrated Brier score below 0.13 and C/D AUC values above 0.85 for both OS and RFS predictions. It also outperformed traditional models in predictive ability and clinical benefit, as shown by clinical decision curves. Calibration curves confirmed good predictive consistency. Using cut-off scores of 16.73/29.05 for OS/RFS, Kaplan-Meier analysis revealed significant prognostic differences between risk groups (p < 0.0001), highlighting the model's robust risk prediction and stratification capabilities.</p><p><strong>Conclusion: </strong>The random survival forest model, combining DWI and clinical features, accurately predicts survival and recurrence risk after radical resection of PDAC and effectively stratifies risk to guide clinical treatment.</p><p><strong>Critical relevance statement: </strong>The construction of 101 ML models based on multiparametric quantitative DWI combined with clinical variables has enhanced the prediction performance for survival and recurrence risks in patients undergoing radical resection for PDAC.</p><p><strong>Key points: </strong>This study first develops DWI-based radiological-clinical ML models predicting PDAC prognosis. Among 101 models, RFS is the best and outperforms other traditional models. Multiparametric DWI is the key prognostic predictor, with model interpretations through SurvSHAP.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"38"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440697","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 role of imaging in defining cardiovascular risk to help cancer patient management: a scoping review.","authors":"Roberto Farì, Giulia Besutti, Pierpaolo Pattacini, Guido Ligabue, Francesco Piroli, Francesca Mantovani, Alessandro Navazio, Mario Larocca, Carmine Pinto, Paolo Giorgi Rossi, Luigi Tarantini","doi":"10.1186/s13244-025-01907-9","DOIUrl":"10.1186/s13244-025-01907-9","url":null,"abstract":"<p><strong>Objective: </strong>This scoping review explores the potential role of cancer-staging chest CT scans in assessing cardiovascular (CV) risk in cancer patients. It aims to evaluate: (1) the correlation between non-gated chest CT and the conventional Agatston score from cardiac CT; (2) the association between coronary calcium scores from non-gated chest CT and CV risk in non-oncological patients; (3) the link between coronary calcium assessed by non-gated chest CT and CV events or endothelial damage in cancer patients.</p><p><strong>Methods: </strong>Three different searches were performed on PubMed, according to the three steps described above. Both original articles and systematic reviews were included.</p><p><strong>Results: </strong>Many studies in the literature have found a strong correlation between coronary calcium scores from non-gated chest CTs and the conventional Agatston scores from gated cardiac CTs. Various methodologies, including Agatston scoring, ordinal scoring, and the \"extent\" and \"length\" methods, have been successfully adapted for use with non-gated chest CTs. Studies show that non-gated scans, even those using iodinated contrast, can accurately assess coronary calcification and predict CV risk, with correlations as high as r = 0.94 when compared to cardiac CTs. In oncological settings, studies demonstrated a significant link between coronary calcium levels on non-gated chest CTs and higher CV risk, including MACE and overall mortality.</p><p><strong>Conclusions: </strong>Radiological assessment of coronary calcium on non-gated CT scans shows potential for improving CV risk prediction.</p><p><strong>Critical relevance statement: </strong>Non-gated chest CT scans can detect endothelial damage in cancer patients, highlighting the need for standardized radiological practices to assess CV risks during routine oncological follow-up, thereby enhancing radiology's role in comprehensive cancer care.</p><p><strong>Key points: </strong>Cancer therapies improve outcomes but increase cardiovascular risk, requiring balanced management. Coronary calcification on non-gated CT correlates with Agatston scores, predicting cardiovascular risk. Routinely performed CTs predict cardiovascular risk, optimizing the management of cancer patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"37"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440753","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":"Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis.","authors":"Kejun Wang, Weiyin Vivian Liu, Renjie Yang, Liang Li, Xuefang Lu, Haoran Lei, Jiawei Jiang, Yunfei Zha","doi":"10.1186/s13244-025-01911-z","DOIUrl":"10.1186/s13244-025-01911-z","url":null,"abstract":"<p><strong>Objective: </strong>To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRI_DL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.</p><p><strong>Materials and methods: </strong>This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRI_DL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2_MESE). The inter-reader agreement on qualitative evaluation of SyMRI_DL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.</p><p><strong>Results: </strong>DLR significantly narrowed the quantitative differences between T2-SyMRI_DL and T2_MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRI_DL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRI_DL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRI_DL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.</p><p><strong>Conclusion: </strong>DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.</p><p><strong>Critical relevance statement: </strong>One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.</p><p><strong>Key points: </strong>Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"44"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440750","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":"R-AI-diographers: a European survey on perceived impact of AI on professional identity, careers, and radiographers' roles.","authors":"Nikolaos Stogiannos, Gemma Walsh, Benard Ohene-Botwe, Kevin McHugh, Ben Potts, Winnie Tam, Chris O'Sullivan, Anton Sheahan Quinsten, Christopher Gibson, Rodrigo Garcia Gorga, David Sipos, Elona Dybeli, Moreno Zanardo, Cláudia Sá Dos Reis, Nejc Mekis, Carst Buissink, Andrew England, Charlotte Beardmore, Altino Cunha, Amanda Goodall, Janice St John-Matthews, Mark McEntee, Yiannis Kyratsis, Christina Malamateniou","doi":"10.1186/s13244-025-01918-6","DOIUrl":"10.1186/s13244-025-01918-6","url":null,"abstract":"<p><strong>Objectives: </strong>Radiographers use advanced medical imaging and radiotherapy (MIRT) equipment. They are also a digitally mature and digitally resilient workforce in healthcare. Artificial intelligence is already changing their clinical practice and roles in data acquisition, post-processing, and workflow management. It is therefore vital to understand the impact of AI on the careers, roles and professional identity of radiographers, as key stakeholders of the digital transformation of healthcare within the medical imaging ecosystem.</p><p><strong>Methods: </strong>A European radiographer survey, endorsed by the European Federation of Radiographer Societies (EFRS), was distributed online. It was piloted with twelve radiographers and translated into eight languages. Although this study included both qualitative and quantitative results, this paper emphasises the quantitative aspect.</p><p><strong>Results: </strong>A total of 2206 European radiographers have responded from 37 different countries. Despite some concerns around workforce deskilling, future professional identity, and job prospects, participants showed overall optimistic views about the use of AI in healthcare. This was particularly strong for those with prior AI education (mean: 2.15 vs. 1.89; p-value: < 0.001), hands-on experience with AI (correlation: 0.047; p-value: 0.038), from countries with higher digital literacy (mean: 2.00 vs.1.93; p-value: 0.027) and a higher academic level of radiography education (mean: 3.28 vs. 3.15; p-value: 0.002). Men appeared slightly more enthused about the development of technological skills and women about the honing of patient-centred care skills. Finally, interprofessional collaboration was seen as essential not only for the seamless clinical integration of AI but also for supporting patient benefit.</p><p><strong>Conclusion: </strong>While AI implementation advances, AI education needs to keep at pace to ensure acceptability, trust, and safe use of this technology by healthcare professionals, minimising their concerns around professional role changes and enabling them to see the opportunities of service transformation.</p><p><strong>Critical relevance statement: </strong>This paper aims to map out the perceived impact of AI on the professional identity and careers of European radiographers.</p><p><strong>Key points: </strong>AI is impacting radiographers' clinical practice and changing their professional identity. Despite increasing AI awareness, AI education is still lacking across Europe. AI education is key for AI acceptability and trust by radiographers, which facilitates AI implementation and service transformation.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"43"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440747","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":"Diagnosis of adult midgut malrotation in CT: sign of absent retromesenteric duodenum reliable.","authors":"Min Yang, Shaokun Zheng, Jian Shu, Zhenwei Yao","doi":"10.1186/s13244-025-01921-x","DOIUrl":"10.1186/s13244-025-01921-x","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the incidence of absent retromesenteric duodenum with other radiological signs and to assess its diagnostic significance for midgut malrotation in adults.</p><p><strong>Methods: </strong>This IRB-approved retrospective single-center study involved adult patients who underwent abdominal CT scans. Patients were screened for the presence of the absent retromesenteric duodenum sign. Signs observed included the position of the duodenal-jejunal junction (DJJ) and jejunum within the abdomen, the relationship between the superior mesenteric artery (SMA) and superior mesenteric vein (SMV), the locations of the ascending colon, cecum, and appendix, and the presence of intestinal volvulus.</p><p><strong>Results: </strong>A total of 5594 patients were included. Seven patients exhibited the sign of absent retromesenteric duodenum. Four of these patients were identified as those diagnosed with midgut malrotation in the past five years. The common features observed in all 11 patients were: the horizontal segment of the duodenum did not traverse behind the SMA but instead curved rightwards and forwards adjacent to it; the DJJ and jejunum were positioned in the right abdomen; the SMV was anterior to the SMA. In 7 patients (7/11), the ascending colon, cecum, and appendix were located in the left abdomen. 5 patients (5/11) showed a high cecum position, and 2 patients (2/11) exhibited a pelvic appendix.</p><p><strong>Conclusion: </strong>The absent retromesenteric duodenum sign in CT diagnosis of adult midgut malrotation has proven to be more reliable.</p><p><strong>Critical relevance statement: </strong>Radiologists should routinely identify the course of the duodenum horizontal segment in CT images, to prevent misdiagnosis of adult midgut malrotation.</p><p><strong>Key points: </strong>CT is suitable for the diagnosis of adult midgut malrotation. Absent retromesenteric duodenum for diagnosing adult midgut malrotation is more reliable than other signs. Diagnostic CT criteria for adult midgut malrotation need updating.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"35"},"PeriodicalIF":4.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11832868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440707","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":"Guiding AI in radiology: ESR's recommendations for effective implementation of the European AI Act.","authors":"Elmar Kotter, Tugba Akinci D'Antonoli, Renato Cuocolo, Monika Hierath, Merel Huisman, Michail E Klontzas, Luis Martí-Bonmatí, Matthias Stefan May, Emanuele Neri, Konstantin Nikolaou, Daniel Pinto Dos Santos, Maija Radzina, Susan Cheng Shelmerdine, Arianna Bellemo","doi":"10.1186/s13244-025-01905-x","DOIUrl":"10.1186/s13244-025-01905-x","url":null,"abstract":"<p><p>This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance. By proposing actionable solutions, the statement highlights ESR's readiness in supporting appropriate application of the AI Act in the field, promoting clarity and the effective integration of AI technologies to ensure their impactful and safe use for the benefit of Europe's patients. CRITICAL RELEVANCE STATEMENT: With the impending arrival of the EU AI Act, it is critical for stakeholders to provide timely input on its key areas. This statement offers expert feedback on the aspects of the EU AI Act that will affect medical imaging. KEY POINTS: The AI Act will significantly impact the field of medical imaging, shaping how AI technologies are used and regulated. The ESR is committed to develop guidelines and best practices, collaborating on the implementation process. This statement offers expert feedback on the aspects of the framework that will affect medical imaging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"33"},"PeriodicalIF":4.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414044","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":"Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer.","authors":"Yilong Huang, Hanxue Cun, Zhanglin Mou, Zhonghang Yu, Chunmei Du, Lan Luo, Yuanming Jiang, Yancui Zhu, Zhenguang Zhang, Xin Chen, Bo He, Zaiyi Liu","doi":"10.1186/s13244-025-01910-0","DOIUrl":"10.1186/s13244-025-01910-0","url":null,"abstract":"<p><strong>Objectives: </strong>This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were retrospectively enrolled in this multicenter study. Body composition metrics, including the area of skeletal muscle, intermuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, muscle radiodensity, and derivative parameters from five basic metrics mentioned before, were calculated based on preoperative non-contrast-enhanced chest CT images at L1 level. The Cox proportional hazards regression analysis was used to evaluate the association between body composition metrics and survival outcomes including overall survival (OS) and disease-free survival (DFS).</p><p><strong>Results: </strong>A total of 2712 patients (mean age, 61.53 years; 1146 females) were evaluated. A total of 635 patients (23.41%) died. 465 patients (19.51%) experienced recurrence and/or distant metastasis. After multivariable adjustment, skeletal muscle index (SMI, HR = 0.86), intermuscular adipose index (IMAI, HR = 1.49), and subcutaneous adipose index (SAI, HR = 0.96) were associated with OS. Similar results were found after stratification by gender, TNM stage, and center. There was no significant association between all body composition metrics and DFS (all p > 0.05). The body composition metrics significantly enhance the model including clinicopathological factors, resulting in an improved AUC for predicting 1-year and 3-year OS, with AUC values of 0.707 and 0.733, respectively.</p><p><strong>Conclusions: </strong>SMI, IMAI, and SAI body composition metrics have been identified as independent prognostic factors and may indicate mortality risk for resectable NSCLC patients.</p><p><strong>Critical relevance statement: </strong>Our findings emphasize the significance of muscle mass, quality, and fat energy storage in clinical decision-making for patients with non-small cell lung cancer (NSCLC). Nutritional and exercise interventions targeting muscle quality and energy storage could be considered for patients with NSCLC.</p><p><strong>Key points: </strong>Multiparameter body composition analysis is associated with the clinical outcome in NSCLC patients. Assessing muscle mass, quality, and adipose tissue helps predict overall survival in NSCLC. The quantity and distribution of body composition can contribute to unraveling the adiposity paradox.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"32"},"PeriodicalIF":4.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11803022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255549","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}