Insights into Imaging最新文献

{"title":"Generative pre-trained transformer 4o (GPT-4o) in solving text-based multiple response questions for European Diploma in Radiology (EDiR): a comparative study with radiologists.","authors":"Jakub Pristoupil, Laura Oleaga, Vanesa Junquero, Cristina Merino, Ozbek Suha Sureyya, Martin Kyncl, Andrea Burgetova, Lukas Lambert","doi":"10.1186/s13244-025-01941-7","DOIUrl":"10.1186/s13244-025-01941-7","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to assess the accuracy of generative pre-trained transformer 4o (GPT-4o) in answering multiple response questions from the European Diploma in Radiology (EDiR) examination, comparing its performance to that of human candidates.</p><p><strong>Materials and methods: </strong>Results from 42 EDiR candidates across Europe were compared to those from 26 fourth-year medical students who answered exclusively using the ChatGPT-4o in a prospective study (October 2024). The challenge consisted of 52 recall or understanding-based EDiR multiple-response questions, all without visual inputs.</p><p><strong>Results: </strong>The GPT-4o achieved a mean score of 82.1 ± 3.0%, significantly outperforming the EDiR candidates with 49.4 ± 10.5% (p < 0.0001). In particular, chatGPT-4o demonstrated higher true positive rates while maintaining lower false positive rates compared to EDiR candidates, with a higher accuracy rate in all radiology subspecialties (p < 0.0001) except informatics (p = 0.20). There was near-perfect agreement between GPT-4 responses (κ = 0.872) and moderate agreement among EDiR participants (κ = 0.334). Exit surveys revealed that all participants used the copy-and-paste feature, and 73% submitted additional questions to clarify responses.</p><p><strong>Conclusions: </strong>GPT-4o significantly outperformed human candidates in low-order, text-based EDiR multiple-response questions, demonstrating higher accuracy and reliability. These results highlight GPT-4o's potential in answering text-based radiology questions. Further research is necessary to investigate its performance across different question formats and candidate populations to ensure broader applicability and reliability.</p><p><strong>Critical relevance statement: </strong>GPT-4o significantly outperforms human candidates in factual radiology text-based questions in the EDiR, excelling especially in identifying correct responses, with a higher accuracy rate compared to radiologists.</p><p><strong>Key points: </strong>In EDiR text-based questions, ChatGPT-4o scored higher (82%) than EDiR participants (49%). Compared to radiologists, GPT-4o excelled in identifying correct responses. GPT-4o responses demonstrated higher agreement (κ = 0.87) compared to EDiR candidates (κ = 0.33).</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"66"},"PeriodicalIF":4.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691712","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":"Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis.","authors":"Zhengping Zhang, Kede Mi, Zhaojun Wang, Xiaoyan Yang, Shuping Meng, Xingcang Tian, Yanzhu Han, Yuling Qu, Li Zhu, Juan Chen","doi":"10.1186/s13244-025-01933-7","DOIUrl":"10.1186/s13244-025-01933-7","url":null,"abstract":"<p><strong>Objective: </strong>To develop and externally validate an integrated model that utilizes optimized radiomics features from non-contrast-enhanced CT (NE-CT) or contrast-enhanced CT (CE-CT), along with morphological features and clinical risk factors, to predict histological classifications of thymic epithelial tumors (TETs).</p><p><strong>Methods: </strong>A total of 182 patients with TET, classified as the low-risk group and the high-risk group based on histology, were divided into a training cohort (N = 122, center 1) and an external validation cohort (N = 60, center 2). Radiomics features were extracted from different CT types, followed by feature selection, including consistency, correlation, and importance tests, to generate Rad-scores for both NE-CT and CE-CT. The integrated model was developed by combining the optimal Rad-score, morphological features, and clinical risk factors using multivariate logistic regression. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. A nomogram was used to visually present the integrated model.</p><p><strong>Results: </strong>A total of 851 radiomics features were extracted, with NE-CT and CE-CT Rad-scores consisting of four and five features, respectively. The AUCs of the CE-CT Rad-score were higher than those of the NE-CT Rad-score in both the training cohort (0.783 vs 0.749) and the external validation cohort (0.775 vs 0.723, p = 0.361). The integrated model, combining five morphological features and the CE-CT Rad-score, achieved AUCs of 0.814 and 0.802 in the training and external validation cohorts, respectively.</p><p><strong>Conclusion: </strong>The integrated model, incorporating radiomics features from CE-CT and morphological features, can help to identify the histological classifications of TETs.</p><p><strong>Critical relevance statement: </strong>This study developed an integrated model based on radiomics features from contrast-enhanced CT and morphological features, demonstrating that the integrated model has impressive predictive capability in distinguishing histological classifications of thymic epithelial tumors through external validation.</p><p><strong>Key points: </strong>Radiomics features extracted from CT more effectively represented thymic epithelial tumor (TET) heterogeneity than morphological features. The radiomics model using contrast-enhanced CT outperformed that using non-contrast-enhanced CT in identifying histological classifications of TET. The integrated model, combining radiomics and morphological features, exhibited the highest performance in predicting TET histological classifications.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"67"},"PeriodicalIF":4.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691879","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":"Intestinal fibrosis assessment in Crohn's disease patient using unenhanced spectral CT combined with 3D-printing technique.","authors":"Qiapeng Huang, Zhihui Chen, Ruonan Zhang, Huasong Cai, Xufeng Yang, Xiaodi Shen, Lili Huang, Xinyue Wang, Qingzhu Zheng, Mingzhe Li, Ziyin Ye, Xubin Liu, Ren Mao, Yangdi Wang, Jinjiang Lin, Zhoulei Li","doi":"10.1186/s13244-025-01914-w","DOIUrl":"10.1186/s13244-025-01914-w","url":null,"abstract":"<p><strong>Objectives: </strong>To integrate multiple parameters derived from unenhanced spectral CT with 3D-printing technique to accurately evaluate intestinal lesions in patients with Crohn's disease (CD).</p><p><strong>Methods: </strong>Patients with proven CD who underwent preoperative spectral CT and surgery were included. The spectral CT images and histopathological specimens were achieved by employing 3D-printing technique. Diagnostic models were developed utilizing Z-Effective, Electron Density (ED), and Hounsfield unit (HU) values derived from spectral CT, along with spectral curve slopes λ₁ and λ₂, as well as ΔHU_MonoE. The area under the receiver operating characteristic curve (AUC) and the influence of inflammation on the efficacy of the models were analyzed.</p><p><strong>Results: </strong>The ED and HU at MonoE 50 keV of the spectral CT were determined to exhibit the highest correlation with the fibrosis degree of the diseased intestine. The training dataset yielded an AUC of 0.828 (95% CI: 0.705-0.951). The sensitivity and specificity were calculated to be 77.3% and 82.6%, respectively. The AUC of the validation set was 0.812 (95% CI: 0.676-0.948) with a sensitivity of 63.6% and specificity of 89.7%. Moreover, our model demonstrated enhanced diagnostic accuracy for detecting fibrosis with an AUC value of 0.933 (95% CI: 0.856-1.000), sensitivity of 90.9%, and specificity of 87.0%, after regulating the influence of inflammation.</p><p><strong>Conclusion: </strong>The integration of unenhanced multi-parametric spectral CT and 3D-printing technique seems to be able to assess the intestinal fibrosis. Our diagnostic model remains effective in assessing the severity of fibrosis under presence of inflammation.</p><p><strong>Critical relevance statement: </strong>Our diagnostic model accurately assessed the degree of intestinal wall fibrosis in Crohn's disease patients by using unenhanced spectral CT and 3D-printing technique, which could facilitate individualized treatment.</p><p><strong>Key points: </strong>Evaluating the extent of Crohn's disease-related fibrosis is important. The combination of 3D-printing technique and multi-parametric spectral CT enhances diagnostic accuracy. The developed model using spectral CT allows for the assessment of intestinal fibrosis using multi-parameters.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"62"},"PeriodicalIF":4.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669818","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":"MRI assessment of body composition for prediction of therapeutic response to biologic agents in patients with Crohn's disease.","authors":"Naomi S Sakai, Andrew A Plumb, Norin Ahmed, Kashfia Chowdhury, Yakup Kilic, Maira Hameed, Anisha Patel, Anisha Bhagwanani, Emma Helbren, Rachel Hyland, Gauraang Bhatnagar, Harbir Sidhu, Hannah Lambie, James M Franklin, Maryam Mohsin, Elen Thomson, Darren Boone, Damian Tolan, Safi Rahman, Nik Ding, Gordon W Moran, Stuart Bloom, Ailsa Hart, Alex Menys, Simon Travis, Steve Halligan, Stuart A Taylor","doi":"10.1186/s13244-025-01930-w","DOIUrl":"10.1186/s13244-025-01930-w","url":null,"abstract":"<p><strong>Objectives: </strong>Altered body fat and muscle mass in Crohn's disease (CD) have been linked to adverse disease course and outcomes. Prediction of treatment response or remission (RoR) of small bowel CD (SBCD) to biologic therapy remains challenging. We aimed to establish the prognostic value of body composition parameters measured using MR enterography (MRE) for RoR at 1 year in patients with SBCD commencing biologic therapy.</p><p><strong>Methods: </strong>Participants were identified from those recruited to a prospective, multicentre study investigating the predictive ability of motility MRI for 1 year RoR in patients starting biologic therapy for active SBCD (MOTILITY trial). Myopenia, skeletal muscle:fat and visceral:subcutaneous fat were measured from baseline MRE. RoR at 1 year was judged using a composite of clinical and morphological MRE parameters. We compared the likelihood of RoR in patients with and without myopenia or low skeletal muscle:fat using logistic regression models.</p><p><strong>Results: </strong>Ninety-six participants were included (mean age 38.2 years; 40 (42%) female). There were 34 (35%) responders. There was no significant difference in RoR at 1 year between those patients with and without skeletal muscle myopenia (OR: 0.85, 95% CI: 0.27, 2.66, p-value: 0.78), or those with or without low skeletal muscle:fat (OR: 0.71, 95% CI: 0.19, 2.71, p-value: 0.62).</p><p><strong>Conclusions: </strong>Body composition parameters demonstrated no value for predicting therapeutic RoR in patients commencing biologic therapy for SBCD.</p><p><strong>Critical relevance statement: </strong>Prediction of response to biologic therapy in small bowel Crohn's disease (SBCD) remains challenging. Body composition parameters cannot predict biologic therapeutic response or remission for SBCD reliably.</p><p><strong>Key points: </strong>Altered body fat and muscle mass in Crohn's disease have been linked to adverse outcomes. Prediction of response to biologic therapy in small bowel CD (SBCD) would be useful for treatment optimisation. Body composition parameters measured using MRI cannot reliably predict biological therapeutic response or remission for SBCD.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"61"},"PeriodicalIF":4.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663340","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":"Annotation-efficient, patch-based, explainable deep learning using curriculum method for breast cancer detection in screening mammography.","authors":"Ozden Camurdan, Toygar Tanyel, Esma Aktufan Cerekci, Deniz Alis, Emine Meltem, Nurper Denizoglu, Mustafa Ege Seker, Ilkay Oksuz, Ercan Karaarslan","doi":"10.1186/s13244-025-01922-w","DOIUrl":"10.1186/s13244-025-01922-w","url":null,"abstract":"<p><strong>Objectives: </strong>To develop an efficient deep learning (DL) model for breast cancer detection in mammograms, utilizing both weak (image-level) and strong (bounding boxes) annotations and providing explainable artificial intelligence (XAI) with gradient-weighted class activation mapping (Grad-CAM), assessed by the ground truth overlap ratio.</p><p><strong>Methods: </strong>Three radiologists annotated a balanced dataset of 1976 mammograms (cancer-positive and -negative) from three centers. We developed a patch-based DL model using curriculum learning, progressively increasing patch sizes during training. The model was trained under varying levels of strong supervision (0%, 20%, 40%, and 100% of the dataset), resulting in baseline, curriculum 20, curriculum 40, and curriculum 100 models. Training for each model was repeated ten times, with results presented as mean ± standard deviation. Model performance was also tested on an external dataset of 4276 mammograms to assess generalizability.</p><p><strong>Results: </strong>F1 scores for the baseline, curriculum 20, curriculum 40, and curriculum 100 models were 80.55 ± 0.88, 82.41 ± 0.47, 83.03 ± 0.31, and 83.95 ± 0.55, respectively, with ground truth overlap ratios of 60.26 ± 1.91, 62.13 ± 1.2, 62.26 ± 1.52, and 64.18 ± 1.37. In the external dataset, F1 scores were 74.65 ± 1.35, 77.77 ± 0.73, 78.23 ± 1.78, and 78.73 ± 1.25, respectively, maintaining a similar performance trend.</p><p><strong>Conclusion: </strong>Training DL models with a curriculum method and a patch-based approach yields satisfactory performance and XAI, even with a limited set of densely annotated data, offering a promising avenue for deploying DL in large-scale mammography datasets.</p><p><strong>Critical relevance: </strong>This study introduces a DL model for mammography-based breast cancer detection, utilizing curriculum learning with limited, strongly labeled data. It showcases performance gains and better explainability, addressing challenges of extensive dataset needs and DL's \"black-box\" nature.</p><p><strong>Key points: </strong>Increasing numbers of mammograms for radiologists to interpret pose a logistical challenge. We trained a DL model leveraging curriculum learning with mixed annotations for mammography. The DL model outperformed the baseline model with image-level annotations using only 20% of the strong labels. The study addresses the challenge of requiring extensive datasets and strong supervision for DL efficacy. The model demonstrated improved explainability through Grad-CAM, verified by a higher ground truth overlap ratio. He proposed approach also yielded robust performance on external testing data.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"60"},"PeriodicalIF":4.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663336","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 status of radiologist staffing, education and training in the 27 EU Member States.","authors":"Adrian P Brady, Graciano Paulo, Boris Brkljacic, Christian Loewe, Martina Szucsich, Monika Hierath","doi":"10.1186/s13244-025-01925-7","DOIUrl":"10.1186/s13244-025-01925-7","url":null,"abstract":"<p><p>This second article of a series of three publications summarises the radiologist situation regarding staffing as well as education and training as analysed by The European Union Radiation, Education, Staffing & Training (EU-REST) study. Despite certain limitations posed by the dependence on survey responses, the results demonstrate that, for both workforce and education/training, considerable heterogeneity exists between Member States, which will impact healthcare delivery and the level of knowledge, skills, and competencies available. The number of radiologists per million inhabitants varies from 51 to 270. 16 out of 27 Member States have Radiologist numbers below the EU average of 127, and 45% of Radiologists in Europe are over 51 years old (in 2022). Clear guidance and metrics about workforce availability for the professions involved in the use of ionising radiation are needed to secure and improve the quality of healthcare delivery in Europe. Although the main scope of the EU-REST study was education, training and workforce availability, an attempt was made to characterise the numbers of pieces of medical imaging and radiotherapy equipment. CRITICAL RELEVANCE STATEMENT: Clear guidance and metrics on radiologist staffing and education/training are needed to address workforce shortages and harmonise education and training standards across the EU-27. KEY POINTS: The article describes the radiologist situation regarding staffing and radiation protection education in the EU Member States. Radiologist staffing and training vary considerably across the EU-27. The fact that more than half of the EU Member States have radiologist numbers below the EU average, and the large proportion of radiologists over 51 years of age, show that clear guidance and metrics are needed to ensure future quality of radiological care.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"59"},"PeriodicalIF":4.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11910488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634004","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":"RadLex and SNOMED CT integration: a pilot study for standardising radiology classification.","authors":"Merit Marquis, Igor Bossenko, Peeter Ross","doi":"10.1186/s13244-025-01935-5","DOIUrl":"10.1186/s13244-025-01935-5","url":null,"abstract":"<p><strong>Background: </strong>Effective communication and information exchange across diverse platforms are critical in healthcare data systems. However, the presence of multiple coding systems and varying standards creates discrepancies and misalignments, highlighting the need for innovative solutions to address these challenges.</p><p><strong>Objective: </strong>The study aimed to develop a technical and semantic interoperability method specifically for radiology procedures, utilising the terminology management tool TermX to facilitate efficient data exchange and utilisation in healthcare.</p><p><strong>Results: </strong>The study resulted in a revised RadLex data model using SNOMED CT, accompanied by a mapping guide and a classification system for X-ray and angiography procedures. This classification system consists of nineteen distinct properties, each defined by specific value sets derived from SNOMED CT terminology. A total of 380 concepts were utilised to describe the 622 procedures examined comprehensively.</p><p><strong>Conclusion: </strong>Through twelve design cycles involving in-depth analysis and iterative refinement, the mapping of angiography and X-ray procedures was successfully achieved, culminating in the creation and validation of a universal model that enhances both primary and secondary data collection. The efficacy and innovation of this system pave the way for further advancements in healthcare interoperability.</p><p><strong>Critical relevance statement: </strong>The innovative integration achieved in this study for standardising radiology classification promises to improve data management practices and enhance patient care outcomes through increased interoperability within the healthcare sector.</p><p><strong>Key points: </strong>A universal radiology procedure model to enhance capture would be valuable. A tool to facilitate technical and semantic interoperability for efficient data exchange in healthcare was created. This system could pave the way for futher advancements in healthcare interoperability.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"58"},"PeriodicalIF":4.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624437","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":"Guidelines and recommendations for radiologist staffing, education and training.","authors":"Adrian P Brady, Christian Loewe, Boris Brkljacic, Graciano Paulo, Martina Szucsich, Monika Hierath","doi":"10.1186/s13244-025-01926-6","DOIUrl":"10.1186/s13244-025-01926-6","url":null,"abstract":"<p><p>This article outlines the radiology-related staffing and education/training guidelines and recommendations developed by the European Commission-funded EU-REST (European Union Radiation, Education, Staffing & Training) project. The radiologist consortium partners propose the use of hour of machine/system/activity as the basic unit to calculate radiologist staffing needs. Education and training recommendations for radiologists include establishing 5 years as the standard duration of specialty training in radiology and establishing the ESR European Training Curriculum for Radiology as the European-wide standard. General recommendations for all professional groups include the maintenance of a central registry for each professional group and for relevant equipment, by each EU Member State, mandated CPD including techniques and knowledge relevant to each professional group, adoption vs adaptation of the project's recommendations. CRITICAL RELEVANCE STATEMENT: The radiology-related staffing and education/training guidelines and recommendations developed by the EU-REST project propose a novel approach to calculate radiologist staffing numbers and provide recommendations regarding radiology education and training as well as general recommendations for all professional groups covered by the project. KEY POINTS: The pros and cons of taking population, workload, equipment or bed availability numbers as parameters to calculate radiology workforce are described. The reasons why these parameters are not suitable to calculate radiologist staffing needs are explained. The proposed use of hour of machine/system/activity as the basic unit to calculate radiologist staffing needs allows for establishing an adaptable and scalable guideline. Education and training recommendations for radiologists and non-profession-specific recommendations are summarised.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"57"},"PeriodicalIF":4.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614863","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 project to define standards for radiology staffing, education and training across the European Union.","authors":"Adrian P Brady, Boris Brkljačić, Jonathan McNulty, Christian Loewe, Mary Coffey, Dimitris Visvikis, Yavuz Anacak, Cristina Garibaldi, Monika Hierath, François Jamar, Núria Jornet, Pedro C Lara, Michelle Leech, Graciano Paulo, Csilla Pesznyak, Irene Polycarpou, Roberto M Sánchez, Martina Szucsich, Francis Zarb","doi":"10.1186/s13244-025-01924-8","DOIUrl":"10.1186/s13244-025-01924-8","url":null,"abstract":"<p><p>The European Union Radiation, Education, Staffing & Training (EU-REST) study was a European Commission-funded, 24-month project that analysed workforce availability, education and training needs to ensure quality and safety aspects of medical applications involving ionising radiation in the EU and developed staffing and education/training guidelines for key professional groups involved in ensuring radiation safety and quality of medical radiation applications in the EU Member States. This article outlines the origin, development, goals and overall structure of the project. CRITICAL RELEVANCE STATEMENT: This article provides a concise overview of the EU-REST project, which analysed the workforce availability of health professionals involved in the use of ionising radiation for diagnostic and therapeutic procedures and the corresponding education and training in radiation protection. KEY POINTS: The aims, professional groups, components, and findings of The European Union Radiation, Education, Staffing & Training (EU-REST) study are described. The limited amount of data and literature on staffing recommendations constituted an important finding of the project. One of the study's recommendations is for each EU Member State to maintain a central registry of professionals involved in ionising radiation as well as on related equipment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"56"},"PeriodicalIF":4.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614862","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":"Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study.","authors":"Siwei Luo, Xiaobo Chen, Mengxia Yao, Yuanlin Ying, Zena Huang, Xiaoya Zhou, Zuwei Liao, Lijie Zhang, Na Hu, Chunwang Huang","doi":"10.1186/s13244-025-01934-6","DOIUrl":"10.1186/s13244-025-01934-6","url":null,"abstract":"<p><strong>Objectives: </strong>Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively.</p><p><strong>Methods: </strong>Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction.</p><p><strong>Results: </strong>The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test cohorts, the patient-level combined model showed the best discrimination ability with the macro-AUCs of 0.988 (95% CI: 0.983-0.992), 0.915 (95% CI: 0.851-0.965), and 0.862 (95% CI: 0.820-0.899), respectively.</p><p><strong>Conclusion: </strong>This study built a robust and interpretable clinical-radiomics model to evaluate three classes of HER2 status based on ultrasound images.</p><p><strong>Critical relevance statement: </strong>Ultrasound-based radiomics method can noninvasively identify three different states of HER2, which may guide treatment decisions and the implementation of personalized HER2-targeted treatment for invasive breast cancer patients.</p><p><strong>Key points: </strong>Determination of HER2 status can affect treatment options for breast cancer. The ultrasound-based clinical-radiomics model can discriminate the three different HER2 statuses. Our developed model can assist in providing personalized recommendations for novel HER2-targeted therapies.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"53"},"PeriodicalIF":4.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572828","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}