European Radiology最新文献

{"title":"ESR Essentials: staging and restaging with FDG-PET/CT in oncology-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging.","authors":"Ricarda Ebner, Gabriel T Sheikh, Matthias Brendel, Jens Ricke, Clemens C Cyran","doi":"10.1007/s00330-024-11094-8","DOIUrl":"10.1007/s00330-024-11094-8","url":null,"abstract":"<p><p>Positron emission tomography (PET) stands as the paramount clinical molecular imaging modality, especially in oncology. Unlike conventional anatomical-morphological imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI), PET provides detailed visualizations of internal activity at the molecular and cellular levels. 18-fluorine-fluorodeoxyglucose ([¹⁸F]FDG)-PET combined with contrast-enhanced CT (ceCT) significantly improves the detection of various cancers. Appropriate patient selection is crucial, and physicians should carefully assess the appropriateness of [¹⁸F]FDG-PET/CT based on specific clinical criteria and evidence. Due to its high diagnostic accuracy, [¹⁸F]FDG-PET/CT is indispensable for evaluating the extent of disease, staging, and restaging known malignancies, and assessing the response to therapy. PET/CT imaging offers significant advantages in patient management, particularly by identifying occult metastases that might otherwise go undetected. This can help prevent unnecessary surgeries, allowing many patients to be redirected to systemic chemotherapy instead. However, it is important to note that the gold standard for surgical planning remains CT and/or MRI, depending on the body region. These imaging modalities, with or without associated angiography, provide superior contrast and spatial resolution, essential for detailed surgical preparation and planning. [¹⁸F]FDG-PET/CT has a central role in the precise and early diagnosis of cancer, contributing significantly to personalized treatment plans. However, it has limitations, including non-tumor-specific uptake and the potential to inaccurately capture the metabolic activity of certain tumor types due to low uptake in some well-differentiated tumor cell lines. Therefore, it should be utilized in clinical scenarios where it offers crucial diagnostic insights not readily available with other imaging modalities. KEY POINTS: Use [¹⁸F]FDG-PET/CT selectively based on clinical appropriateness criteria and existing evidence to optimize resource utilization and minimize patient exposure. Employ [¹⁸F]FDG-PET/CT in treatment planning and monitoring, particularly for assessing chemotherapy or radiotherapy response in FDG-avid lymphoma and solid tumors. When available, [¹⁸F]FDG-PET/CT can be integrated with other diagnostic tools, such as MRI, to enhance overall diagnostic accuracy.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1894-1902"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389147","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 assessed morphological features can predict higher mitotic index in gastric gastrointestinal stromal tumors.","authors":"Xiaoxuan Jia, Youping Xiao, Hui Zhang, Jiazheng Li, Shiying Lv, Yinli Zhang, Fan Chai, Caizhen Feng, Yulu Liu, Haoquan Chen, Feiyu Ma, Shengcai Wei, Jin Cheng, Sen Zhang, Zhidong Gao, Nan Hong, Lei Tang, Yi Wang","doi":"10.1007/s00330-024-11087-7","DOIUrl":"10.1007/s00330-024-11087-7","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the correlation of the mitotic index (MI) of 1-5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size.</p><p><strong>Methods: </strong>We enrolled 344 patients across four institutions, each pathologically diagnosed with 1-5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1-2 cm (n = 69), 2-3 cm (n = 96), 3-4 cm (n = 107), and 4-5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated.</p><p><strong>Results: </strong>Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01-1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77-11.73) emerged as independent high-risk features for MI > 5 of 1-5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3-4 cm and 4-5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2-3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson's ρ = 0.85, p < 0.01).</p><p><strong>Conclusions: </strong>The invasive margin stands out as the sole independent CT morphological high-risk feature for 1-5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features.</p><p><strong>Key points: </strong>Question How can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making? Findings Invasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement. Clinical relevance For gGISTs measuring up to 5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2094-2105"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142344226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to Letter to the Editor: \"Prognostic significance of MRI features in patients with solitary large hepatocellular carcinoma following surgical resection\" from Xiaoping Yu, MD; Huaping Liu, MD.","authors":"Kyowon Gu, Ji Hye Min","doi":"10.1007/s00330-024-11068-w","DOIUrl":"10.1007/s00330-024-11068-w","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2355-2356"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.","authors":"Yuchen Wang, Qinghe Han, Baohong Wen, Bingbing Yang, Chen Zhang, Yang Song, Luo Zhang, Junfang Xian","doi":"10.1007/s00330-024-11033-7","DOIUrl":"10.1007/s00330-024-11033-7","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.</p><p><strong>Methods: </strong>This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models.</p><p><strong>Results: </strong>The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006).</p><p><strong>Conclusions: </strong>This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness.</p><p><strong>Clinical relevance statement: </strong>Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors.</p><p><strong>Key points: </strong>Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2074-2083"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic structuring of radiology reports with on-premise open-source large language models.","authors":"Piotr Woźnicki, Caroline Laqua, Ina Fiku, Amar Hekalo, Daniel Truhn, Sandy Engelhardt, Jakob Kather, Sebastian Foersch, Tugba Akinci D'Antonoli, Daniel Pinto Dos Santos, Bettina Baeßler, Fabian Christopher Laqua","doi":"10.1007/s00330-024-11074-y","DOIUrl":"10.1007/s00330-024-11074-y","url":null,"abstract":"<p><strong>Objectives: </strong>Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists' reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports.</p><p><strong>Materials and methods: </strong>We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a detailed, fully structured reporting template with 48 question-answer pairs. All reports were independently structured by the LLM and two human readers. Bayesian inference (Markov chain Monte Carlo sampling) was used to estimate the distributions of Matthews correlation coefficient (MCC), with [-0.05, 0.05] as the region of practical equivalence (ROPE).</p><p><strong>Results: </strong>The LLM generated valid structured reports in all cases, achieving an average MCC of 0.75 (94% HDI: 0.70-0.80) and F1 score of 0.70 (0.70-0.80) for English, and 0.66 (0.62-0.70) and 0.68 (0.64-0.72) for German reports, respectively. The MCC differences between LLM and humans were within ROPE for both languages: 0.01 (-0.05 to 0.07), 0.01 (-0.05 to 0.07) for English, and -0.01 (-0.07 to 0.05), 0.00 (-0.06 to 0.06) for German, indicating approximately comparable performance.</p><p><strong>Conclusion: </strong>Locally hosted, open-source LLMs can automatically structure free-text radiology reports with approximately human accuracy. However, the understanding of semantics varied across languages and imaging findings.</p><p><strong>Key points: </strong>Question Why has structured reporting not been widely adopted in radiology despite clear benefits and how can we improve this? Findings A locally hosted large language model successfully structured narrative reports, showing variation between languages and findings. Critical relevance Structured reporting provides many benefits, but its integration into the clinical routine is limited. Automating the extraction of structured information from radiology reports enables the capture of structured data while allowing the radiologist to maintain their reporting workflow.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2018-2029"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399830","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":"Meta-analysis of [¹⁸F]FDG-PET/CT in pulmonary sarcoidosis.","authors":"Ryan Donnelly, Michael McDermott, Gerry McManus, Alessandro N Franciosi, Michael P Keane, Emmet E McGrath, Cormac McCarthy, David J Murphy","doi":"10.1007/s00330-024-10949-4","DOIUrl":"10.1007/s00330-024-10949-4","url":null,"abstract":"<p><strong>Background: </strong>18F-Fluorodeoxyglucose (FDG) PET/CT is emerging as a tool in the diagnosis and evaluation of pulmonary sarcoidosis, however, there is limited consensus regarding its diagnostic performance and prognostic value.</p><p><strong>Method: </strong>A meta-analysis was conducted with PubMed, Science Direct, MEDLINE, Scopus, and CENTRAL databases searched up to and including September 2023. 1355 studies were screened, with seventeen (n = 708 patients) suitable based on their assessment of the diagnostic performance or prognostic value of FDG-PET/CT. Study quality was assessed using the QUADAS-2 tool. Forest plots of pooled sensitivity and specificity were generated to assess diagnostic performance. Pooled changes in SUVmax were correlated with changes in pulmonary function tests (PFT).</p><p><strong>Results: </strong>FDG-PET/CT in diagnosing suspected pulmonary sarcoidosis (six studies, n = 400) had a pooled sensitivity of 0.971 (95%CI 0.909-1.000, p = < 0.001) and specificity of 0.873 (95%CI 0.845-0.920)(one study, n = 169). Eleven studies for prognostic analysis (n = 308) indicated a pooled reduction in pulmonary SUVmax of 4.538 (95%CI 5.653-3.453, p = < 0.001) post-treatment. PFTs displayed improvement post-treatment with a percentage increase in predicted forced vital capacity (FVC) and diffusion capacity of the lung for carbon monoxide (DLCO) of 7.346% (95%CI 2.257-12.436, p = 0.005) and 3.464% (95%CI -0.205-7.132, p = 0.064), respectively. Reduction in SUVmax correlated significantly with FVC (r = 0.644, p < 0.001) and DLCO (r = 0.582, p < 0.001) improvement.</p><p><strong>Conclusion: </strong>In cases of suspected pulmonary sarcoidosis, FDG-PET/CT demonstrated good diagnostic performance and correlated with functional health scores. FDG-PET/CT may help to guide immunosuppression in cases of complex sarcoidosis or where treatment rationalisation is needed.</p><p><strong>Clinical relevance statement: </strong>FDG-PET/CT has demonstrated a high diagnostic performance in the evaluation of suspected pulmonary sarcoidosis with radiologically assessed disease activity correlating strongly with clinically derived pulmonary function tests.</p><p><strong>Key points: </strong>In diagnosing pulmonary sarcoidosis, FDG-PET/CT had a sensitivity and specificity of 0.971 and 0.873, respectively. Disease activity, as determined by SUVmax, reduced following treatment in all the included studies. Reduction in SUVmax correlated with an improvement in functional vital capacity, Diffusion Capacity of the Lungs for Carbon Monoxide, and subjective health scoring systems.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2222-2232"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751388","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":"ESR Essentials: imaging of middle ear cholesteatoma-practice recommendations by the European Society of Head and Neck Radiology.","authors":"Philip Touska, Steve E J Connor","doi":"10.1007/s00330-024-11021-x","DOIUrl":"10.1007/s00330-024-11021-x","url":null,"abstract":"<p><p>Although non-malignant, middle ear cholesteatoma can result in significant complications due to local bone erosion and infection. The treatment of cholesteatoma is surgical, but residual disease is common and may be clinically occult, particularly when the canal wall is preserved or reconstructive techniques are employed. Imaging plays a pivotal role in the management of patients with middle ear cholesteatoma-aiding clinical diagnosis, identifying complications, planning surgery, and detecting residual disease at follow-up. Computed tomography is the primary imaging tool in the preoperative setting since it can provide both a surgical roadmap and detect erosive complications of cholesteatoma. The ability of magnetic resonance imaging with non-echoplanar diffusion-weighted sequences to accurately detect residual disease has led to a shift in the diagnostic paradigm for post-surgical follow-up of cholesteatoma, such that routine \"second-look\" surgery is no longer required. The following practice recommendations are aimed at helping the radiologist choose appropriate imaging approaches and understand the key diagnostic considerations for the evaluation of pre- and post-surgical middle ear cholesteatoma. KEY POINTS: In the preoperative setting, CT is the first-line imaging modality and MRI is reserved for rare clinical scenarios (low evidence). Non-echoplanar imaging (EPI) DWI is the optimal MRI sequence for the detection of residual cholesteatoma (moderate evidence). Non-EPI DWI plays an important role in the postoperative surveillance of cholesteatoma (moderate evidence).</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2053-2064"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055288","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":"Further insights into the use of contrast-enhanced imaging for breast cancer follow-up: the pros view.","authors":"Elisabetta Giannotti, Matteo Lambertini","doi":"10.1007/s00330-024-11096-6","DOIUrl":"10.1007/s00330-024-11096-6","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2141-2143"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor: Current role of [18 F] FDG-PET/CT in pulmonary sarcoidosis: a meta-analysis.","authors":"Yingwei Guo, Yingying Guan, Jian Liao, Lan Yue, Shengzhang Li","doi":"10.1007/s00330-024-11189-2","DOIUrl":"10.1007/s00330-024-11189-2","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2233-2234"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142766686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One solid step to general neuroradiology AI.","authors":"Yuanyuan Cui, Jie Feng","doi":"10.1007/s00330-024-11020-y","DOIUrl":"10.1007/s00330-024-11020-y","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1933-1934"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}