Insights into Imaging最新文献

Advancements in medical imaging, nuclear medicine, and radiotherapy have significantly improved patient care, while also requiring responsive education and training programmes and continuing professional development (CPD) for radiographers who work across medical imaging, nuclear medicine, and radiotherapy. This article presents findings from the EU-REST (European Union Radiation, Education, Staffing and Training) project with a focus on the evaluation of education and training requirements for radiographers across EU Member States. Evidence-based guidelines to harmonise radiographer education and training and improve safety and quality in medical settings are proposed. The findings highlight the need for standardised, competency-based curricula that align with evolving technologies, safety regulations, and professional responsibilities, together with the importance of integrating radiation safety, quality management, and patient-centred care into curricula. To address accessibility and workforce needs, diverse entry pathways, flexible learning models, and equitable financial support for student radiographers are recommended. Harmonisation of training content, structured clinical placements, and mandatory CPD are also proposed to ensure radiographers remain proficient in emerging technologies such as AI and automation. The findings also underscore the necessity of national accreditation, certification, and licensing systems to maintain high professional standards. Establishing a unified core curriculum at the European level would enhance education quality and ensure compliance with the basic safety standards directive (BSSD). Additionally, postgraduate training opportunities should be expanded to support specialisation and career advancement. By adopting these recommendations, the radiographer profession can cultivate a highly skilled workforce capable of delivering safe, effective, and innovative patient care, ensuring alignment with the future demands of healthcare and technological progress. CRITICAL RELEVANCE STATEMENT: The radiographer education and training recommendations developed by the EU-REST project propose a framework to ensure a highly skilled radiographer workforce capable of delivering safe, effective, and innovative patient care, ensuring alignment with the future demands of healthcare and technological progress. KEY POINTS: The EU-REST project explored the education and training requirements for radiographers across the EU with a focus on patient safety and quality. The development of standardised education and training guidelines for radiographers is essential to ensuring a highly skilled, safe, and effective workforce. These recommendations will support the development of competent, adaptable, and research-driven professionals who contribute to the advancement of patient care.

Objective: It was hypothesized that virtual touch tissue imaging and quantification (VTIQ) is more accurate in quantifying intestinal stiffness compared to conventional B-mode ultrasound for detecting Crohn's disease (CD) stenosis. We aimed to explore the diagnostic value of multimodal ultrasound in intestinal stenosis of CD.

Materials and methods: A retrospective analysis of CD patients (May 2020 to October 2024) was conducted. Risk factors associated with intestinal stenosis in CD were identified using univariate and multivariate logistic regression analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) of combined indices was compared with individual indices to assess their predictive ability for intestinal stenosis in CD.

Results: Sixty-three patients were included. Univariate and multivariate logistic regression analysis identified shear wave velocity (OR = 3.943, p = 0.008), Young's modulus value (OR = 1.079, p = 0.046), and intestinal bowel ultrasound stenosis assessment score (IBUS-SAS; OR = 1.033, p = 0.015) as significant risk factors. The AUC for IBUS-SAS was 0.671, for shear wave velocity was 0.838, and for Young's modulus value was 0.788. The combined model yielded an AUC of 0.878. Compared to the gold standard (Simplified Endoscopy for Crohn's Disease, SES-CD), the ultrasound-based approach showed 100% specificity and 71% sensitivity for stenosis detection.

Conclusion: IBUS-SAS, shear wave velocity, and Young's modulus were independent risk factors for CD intestinal stenosis, with shear wave velocity being the most accurate (AUC = 0.838), supporting our hypothesis. These findings warrant validation in large, high-quality studies.

Critical relevance statement: This study examines the potential of VTIQ ultrasound to assess intestinal stiffness in CD, offering a non-invasive, radiation-free approach that may enhance diagnostic capabilities and contribute to clinical radiology practice.

Key points: VTIQ non-invasively assesses intestinal stiffness in CD. Shear wave velocity, Young's modulus, and IBUS-SAS predict stenosis. Integrated indices improve diagnostic accuracy. VTIQ shows promise for safe, non-invasive diagnosis. Requires large-scale, multicenter studies for confirmation.

Objectives: Escape rooms provide an interactive learning experience, combining clinical knowledge with problem-solving and teamwork. A radiology-themed escape room has been organized at the European Congress of Radiology in 2019 and 2023-2025, with over 900 people participating in total. The process of developing a radiology-themed escape room is discussed, and the results of a participant survey are presented.

Materials and methods: The development of a radiology-themed escape room was based on five steps. Initially, an overarching concept was chosen, then multiple puzzle ideas were brainstormed. These were linked together to form a story, and then fully developed with relevant images and materials. Finally, the room was tied together, and a fitting atmosphere was created. Participants in 2025 were asked to complete a survey with questions on their training status, the challenges that they found most difficult, and their thoughts on the activity as a learning tool and for improving teamwork.

Results: Three different concepts of radiology-themed escape rooms were developed for the congresses from 2019 to 2025. The overarching concepts were a polytrauma situation, a thrombectomy for fulminant pulmonary embolism, and a tumor board, respectively. Two hundred ninety people participated in 2025, and 149 completed the exit survey; 66.7% of participants were able to complete the room in time. Enjoyment, learning, and team building were all rated highly by participants.

Conclusion: A development process for designing a radiology-themed escape room is presented. A prior implementation shows an enjoyable and educational experience for radiologists and other medical professionals.

Critical relevance: Insights are given on the development of a radiology-themed escape room, providing a unique interactive learning opportunity for residents that incorporates image interpretation with teamwork and cognitive puzzles, resulting in an enjoyable educational experience.

Key points: A step-by-step guide on developing a radiology-themed escape room is presented. Radiological escape rooms provide an enjoyable, educational, and team-building experience. Interactive learning experiences could play a larger role in modern radiology education.

Objective: The purpose of the present study is to determine whether or not lesion characteristics on PET/CT could reduce the number of samples required to achieve a diagnosis in image-guided bone biopsies (IGBB).

Materials and methods: A retrospective review of 38 percutaneous IGBB performed at a single center. Biopsies have been performed from January 1st, 2020, to October 23rd, 2024. Inclusion criteria were patients with a PET/CT and a histopathologic report available. Specimens were collected, numbered, and independently analyzed in separate containers. PET/CT data, including SUV_max, SUV_mean, MTV, TLG, and morphological lesion characteristics, were correlated with biopsy outcomes and subjected to statistical analysis. Patients were classified by the number of samples needed for diagnosis: first (Group 1), second (Group 2), or third/subsequent (Group 3).

Results: Thirty-four/38 (89%) involved spinal and pelvic locations (34/38; 89%). Breast cancer metastases were the most common diagnosis (21/38; 55%). Group 1 included 20 IGBB (52%), group 2 included 9 IGBB (24%), and group 3 included 9 IGBB (24%). No statistically significant difference was found between groups in metabolic characteristics and the number of samples needed for diagnostic purposes (p > 0.05). Subgroup analysis, including factors such as density or lesion size, didn't find any significant differences between groups.

Conclusion: The results suggest that high metabolic activity alone does not justify reducing the number of biopsy samples without compromising diagnostic performance. This supports the recommendation to obtain at least three samples and highlights the importance of selecting the safest biopsy site, regardless of metabolic activity.

Critical relevance statement: This study critically assesses the role of FDG PET/CT metabolic parameters in predicting the diagnostic success of IGBB, providing new insights to improve target selection and biopsy planning in clinical radiology.

Key points: This study assessed whether metabolic activity on FDG PET/CT influences the diagnostic yield of IGBB. High metabolic activity did not allow for reducing the number of samples without affecting diagnostic performance. At least three biopsy samples should be obtained, prioritizing safety over metabolic activity when selecting the biopsy site.

Objectives: In this study, we developed a multi-modal CT-based machine learning model to predict the response of gastric cancer (GC) patients to first-line chemotherapy combined with PD-1 inhibitors and performed external validation and multi-model comparisons.

Materials and methods: We retrospectively analyzed the clinical data of 348 patients with GC who underwent immunotherapy. The patients were categorized into an internal validation cohort (center A, n = 272) and an external validation cohort (center B, n = 76). Pre-treatment clinical and CT radiomics features were extracted to develop three models: a clinical model, a radiomics model and a clinical-radiomics model. The classifiers included logistic regression (LR), linear support vector classification (Linear SVC), support vector machine, and random forest.

Results: A total of 19 radiomics signatures and 5 clinical feature signatures were selected. In the radiomics model, the Linear SVC algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.88 and 0.76 in internal and external validation sets, respectively. In both the clinical model and the clinical-radiomics model, the LR algorithm demonstrated high and stable predictive performance in the internal (AUC = 0.89 and 0.94) and external validation datasets (AUC = 0.76 and 0.85). Among all models in the external validation dataset, the clinical-radiomics model utilizing LR outperformed all other classifiers.

Conclusions: The clinical-radiomics model, in combination with the LR algorithm, provides a reliable and effective method for predicting the early response of advanced GC patients treated with programmed cell death-1 (PD-1) inhibitors combined with chemotherapy.

Critical relevance statement: CT radiomics and laboratory parameters were used to evaluate early prediction of response to PD-1 inhibitors combined with chemotherapy in patients with advanced gastric cancer. This clinical-radiomics model provides a novel approach to predict immunotherapy efficacy and prognosis.

Key points: Evaluating the efficacy of PD-1 inhibitors combined with chemotherapy in advanced gastric cancer using only clinical data is limited. Only some patients with advanced gastric cancer treated with the PD-1 inhibitors combined with chemotherapy achieved complete regression. This clinical-radiomics model showed good performance for predicting gastric cancer response to chemotherapy combined with PD-1 inhibitors.

Objectives: AI offers considerable potential to improve diagnostic accuracy and efficiency in radiology. However, its successful implementation depends largely on the trust and acceptance of referring physicians. This study examines physicians' attitudes toward AI in radiology, identifying key facilitators and barriers to its clinical integration.

Materials and methods: A total of 169 licensed physicians in Germany, including surgeons, internists, and general practitioners who frequently refer patients to radiology, were surveyed. Participants were recruited via a systematic review of hospital and practice websites. A structured online questionnaire assessed perceptions of AI, focusing on trust-related factors, preferred applications, and adoption barriers. Statistical analysis was conducted using R and Python.

Results: Overall, 60% of respondents evaluated AI positively for enhancing diagnostic accuracy (mean score 3.7 ± 1.2). The most influential trust factor was model transparency (56.3%), followed by legal clarity on liability (25.0%) and strong data protection (11.7%). Transparency was rated significantly higher than other factors (p < 0.001). Preferred AI applications included lesion detection, research data analysis, and workflow management. Barriers to adoption included the "black box" nature of AI, unclear accountability, and data privacy concerns. Subgroup analysis revealed no significant variation in trust factors between specialties (p = 0.21).

Conclusion: Physicians see AI as a promising tool in radiology but emphasize the need for greater transparency, clear legal responsibility, and secure data handling. Addressing these concerns through explainable AI models, legal frameworks, and robust data protection measures is essential for fostering trust and facilitating successful AI integration in clinical practice.

Critical relevance statement: Understanding physicians' concerns about AI transparency, liability, and data privacy is essential. Addressing these barriers is critical to ensuring responsible implementation, building trust, and enabling effective integration of AI into clinical radiology workflows.

Key points: AI acceptance in radiology faces transparency and liability concerns. Lesion detection and data analysis were rated most beneficial by physicians. Clear regulation and explainability are key for clinical AI trust.

Objectives: To prospectively evaluate the diagnostic accuracy of ultrasound-derived fat fraction (UDFF) in quantifying hepatic steatosis, to establish and validate a dual-threshold UDFF classification system, and to investigate its efficacy for risk stratification in body mass index (BMI)-defined subgroups.

Materials and methods: This prospective multicenter study involved 790 suspected metabolic dysfunction-associated steatotic liver disease (MASLD) participants from April 2023 to November 2024 (derivation: n = 553; validation: n = 237). Liver biopsy histopathology (n = 342), MRI proton density fat fraction (MRI-PDFF) (n = 396), or proton magnetic resonance spectroscopy (¹H-MRS) (n = 52) was used as the reference standard. UDFF was compared to noninvasive test Hepatic Steatosis Index (HSI) and Fatty Liver Index (FLI) using area under the curve (AUC). The diagnostic thresholds were optimized to maintain at least 90% sensitivity and specificity in stratifying hepatic steatosis severity. A two-step strategy of UDFF followed by HSI was used to rule in and rule out steatosis at BMI ≥ 23 kg/m² subgroup.

Results: UDFF demonstrated significant correlations with three reference standards (Spearman's ρ = 0.798-0.847). Comparing with HSI and FLI, UDFF showed higher AUC (0.933, 0.948, and 0.914, respectively) for assessing ≥ S1, ≥ S2 and S3. A clinically practical dual-threshold system effectively classified hepatic steatosis severity. A sequential UDFF/HSI strategy achieved a high positive predictive value (PPV = 95.8%) to rule in hepatic steatosis and lowered the proportion of indeterminate cases (from 18.0 to 7.6%) in patients with BMI ≥ 23 kg/m².

Conclusion: UDFF is a highly effective noninvasive tool for quantifying hepatic steatosis. A sequential use of UDFF/HSI could improve hepatic steatosis detection in patients with BMI ≥ 23 kg/m².

Critical relevance statement: The study proposed dual-threshold diagnostic criteria (sensitivity/specificity ≥ 90%) of UDFF for steatosis grading, and established a BMI-stratified risk stratification tool in multi-center cohorts, proving the efficacy of UDFF in noninvasively quantifying liver steatosis.

Key points: Early diagnosis of hepatic steatosis holds critical clinical significance. The study proposed dual-threshold ultrasound-derived fat fraction (UDFF) criteria and BMI-stratified steatosis risk prediction strategy. UDFF provided a non-invasive, accurate diagnostic alternative to liver biopsy and MRI.

Objectives: To evaluate the potential value of ¹⁸F-FDG positron emission tomography (PET) and multiparametric MRI (intravoxel incoherent motion, IVIM, and diffusion kurtosis imaging, DKI) in the prediction of lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC).

Materials and methods: A total of 73 patients with NSCLC who underwent integrated ¹⁸F-FDG PET/MRI were included. IVIM, DKI, and PET parameters with or without LVI of NSCLC were measured and compared, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of each parameter. Univariate and multivariate logistic regression models were used to study the optimal combination of PET/MRI parameters for predicting LVI.

Results: PET-derived parameters (SUVmax, MTV, TLG) and IVIM, DKI MRI-derived parameters (ADCstand, D, MK, MD) were significantly different between patients with and without LVI (p < 0.05). Multivariate logistic regression analysis showed that MTV and D were independent predictors of LVI, and the combined prediction model of the two parameters had the highest predictive value for the diagnosis of LVI (AUC = 0.841; sensitivity = 63.83%; specificity = 92.31%).

Conclusion: The present study demonstrates that IVIM, DKI, and PET can be utilized to evaluate LVI status in NSCLC, with the combined diagnostic approach of MTV and D showing the highest diagnostic performance, which may provide a novel reference for clinical management.

Critical relevance statement: The performance of metabolic parameters and diffusion parameters in the identification of lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC) is similar, but the combination of metabolic tumor volume (MTV) and true diffusion coefficient (D) may improve the diagnostic efficacy.

Key points: A multimodal PET-MRI model evaluates lymphovascular invasion (LVI) in patients with non-small cell lung cancer (NSCLC). Metabolic and diffusion parameters have similar efficacy in predicting LVI in NSCLC. The combined metabolic tumor volume and true diffusion coefficient prediction model is the most valuable.

Objectives: To evaluate gender representation among editors-in-chief and deputy editors of radiology journals indexed in the 2024 Journal Citation Reports (JCR) and to analyze associations with bibliometric indicators and global economic classification.

Materials and methods: A cross-sectional study was performed using publicly available data from radiology-related journals listed in the 2024 JCR (released June 2025). Journals were included if the editorial board composition was accessible online. Gender was identified through institutional profiles and standardized databases. Descriptive statistics summarized gender distribution. Associations between gender, editorial role, bibliometric performance, and World Bank income classification were tested using chi-square, Mann-Whitney U, Spearman's correlation, and nominal logistic regression.

Results: Of 204 eligible journals, 135 met the inclusion criteria, comprising 387 editorial members. Women represented 20.2% of all editors, 21.4% of deputy editors, and 18.4% of editors-in-chief. Female representation was highest in Q1 journals (26.0%) and lowest in Q2 (15.1%). A significant association was observed between Eigenfactor Score and female representation (p = 0.0494), whereas no association was found with journal impact factor or income classification. Geographic disparities were evident, with some countries achieving parity while others had no female representation.

Conclusions: Gender inequities remain pronounced in radiology editorial leadership, particularly at the editor-in-chief level. Higher Eigenfactor Scores may modestly correlate with improved inclusion. Transparent policies and targeted interventions are required to address structural inequities and advance diversity in academic publishing.

Critical relevance statement: Gender disparities exist in radiology editorial leadership, and the Eigenfactor Score was found to be associated with female representation. By providing a comprehensive overview, the findings underscore the structural barriers that limit diversity and the importance of transparent, equity-focused editorial policies.

Key points: Gender disparities persist in radiology editorial boards, with women underrepresented at both deputy editor and editor-in-chief levels. Eigenfactor Score, but not impact factor or national income classification, was significantly associated with increased female representation. Gender disparities persist across editorial leadership roles in radiology, underscoring the need for transparent policies and structural reforms to promote greater equity.

Objectives: To compare understandability, readability, and communication characteristics of radiology report explanations generated by three freely accessible large language models-ChatGPT, Gemini, and Copilot-based on a standardized prompt, as assessed by expert reviewers.

Materials and methods: In this retrospective single-center study, 100 anonymized radiology reports were randomly selected from five subspecialties. Each report was submitted to ChatGPT (GPT-3.5), Gemini, and Copilot between May 23 and May 30, 2025, using the prompt, "Can you explain my radiology report?". Responses were evaluated for medical correctness on a 3-point scale (0-2), understandability using the patient education materials assessment tool for understandability (PEMAT-U), and readability using Flesch Reading Ease (FRE), Automated Readability Index (ARI), and Gunning Fog Index (GFI). Communicative features-including uncertainty language, patient guidance, and clinical suggestions-were also assessed. Anxiety-inducing potential was rated on a 3-point Likert scale.

Results: All models demonstrated high medical correctness (mean: 1.97 ± 0.17/2). ChatGPT produced the most readable (FRE: 60.33 ± 3.65; ARI: 9.66 ± 1.01; GFI: 9.1 ± 1.04) and understandable (PEMAT-U: 89.58 ± 3.90%) responses (p < 0.01). Copilot included the most uncertainty language (1.62 ± 0.62) and clinical suggestions (1.69 ± 0.60), while Gemini provided the strongest patient guidance (1.62 ± 0.58) (all p < 0.01). Only Copilot showed subspecialty-related variation in readability (GFI; p = 0.048). Anxiety potential was low across all models (mean: 0.07 ± 0.33).

Conclusion: ChatGPT offered superior readability and understandability. Copilot delivered more clinical detail with cautious language, while Gemini emphasized patient-centered guidance. These differences support context-specific use of language models in radiology communication.

Critical relevance statement: This study shows that freely accessible large language models produce radiology report explanations with varying levels of readability, understandability, and communication quality. Expert-based findings may help inform future strategies to optimize patient-facing applications of AI in radiological communication.

Key points: This study compared how freely available AI chatbots respond to patient queries about radiology reports. Significant differences were found in understandability, readability, patient guidance, and use of uncertainty or clinical suggestions. Findings support context-specific use of AI tools to improve radiology communication and patient understanding.