European Radiology最新文献

{"title":"Two independent studies, one goal, one conclusion: radiomics research quality under the microscope.","authors":"Burak Kocak, Nathaniel Barry","doi":"10.1007/s00330-025-11457-9","DOIUrl":"10.1007/s00330-025-11457-9","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4546-4548"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448672","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":"PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography.","authors":"Jennifer Alvén, Richard Petersen, David Hagerman, Mårten Sandstedt, Pieter Kitslaar, Göran Bergström, Erika Fagman, Ola Hjelmgren","doi":"10.1007/s00330-025-11410-w","DOIUrl":"10.1007/s00330-025-11410-w","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA).</p><p><strong>Materials and methods: </strong>CCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Linköping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance.</p><p><strong>Results: </strong>PlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684).</p><p><strong>Conclusion: </strong>We developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader.</p><p><strong>Key points: </strong>Question A tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination. Findings Segmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance. Clinical relevance This novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4461-4471"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255083","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":"Predicting axillary residual disease after neoadjuvant therapy in breast cancer using baseline MRI and ultrasound.","authors":"Caroline Malhaire, Ozgun Umay, Vincent Cockenpot, Fatine Selhane, Toulsie Ramtohul, Fabien Reyal, Jean-Yves Pierga, Emanuella Romano, Anne Vincent-Salomon, Youlia Kirova, Enora Laas, Hervé J Brisse, Frédérique Frouin","doi":"10.1007/s00330-025-11408-4","DOIUrl":"10.1007/s00330-025-11408-4","url":null,"abstract":"<p><strong>Objectives: </strong>To predict axillary node residual disease in women treated for node-positive breast cancer (BC) by neoadjuvant therapy (NAT), using breast BI-RADS MRI features and axillary ultrasound at baseline.</p><p><strong>Material and methods: </strong>In this single-center, retrospective study, women with node-positive BC who underwent NAT between 2016 and 2021 were included. Pre-treatment axillary US and breast MRIs were evaluated using the BI-RADS lexicon and T2 features, including Breast Edema Score. Univariate and multivariate logistic regression analyses were conducted for the prediction of axillary residual disease (ARD). A multivariable model based on logistic regression was trained and evaluated on randomly split train and test sets (7:3 ratio).</p><p><strong>Results: </strong>Out of the 141 women, 41% had post-NAT ARD. Axillary metastasis was independently associated with luminal subtype (odds ratio (OR), 25.5; p < 0.001), anterior tumor location (OR, 14.1; p = 0.008), and cortical thickening ≥ 7 mm (OR, 6.09; p = 0.002). Intratumoral T2 high signal intensity was protective (OR, 0.16; p = 0.006), while Ki67 had a marginal association (p = 0.064). In the training and test sets, the model, which is available online, achieved AUCs of 0.860 (95% CI: 0.783-0.936) and 0.843 (95% CI: 0.714-0.971), respectively. Anterior depth location and cortical thickening greater than 7 mm were also independently associated with post-NAT axillary burden.</p><p><strong>Conclusion: </strong>Adjusting for BC subtype and KI-67 index, the anterior third location of BC, a cortical thickness greater than 7 mm, and the absence of intratumoral T2 hyperintensity is predictive of ARD after NAT.</p><p><strong>Key points: </strong>Question What baseline imaging-based predictive models can identify patients at risk of persistent nodal disease after neoadjuvant therapy? Findings Baseline US cortical thickness superior to 7 mm, anterior tumor location, and absence of an intratumoral high signal on T2-weighted MRI predict residual axillary disease. Clinical relevance Our predictive model, available online at: litoic.shinyapps.io/LNPred_Apps , including breast cancer subtype, Ki-67 index level, breast cancer location, intratumoral signal intensity on T2WI, and initial lymph node thickness, could guide post-NAT axillary management.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4896-4909"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370645","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":"AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study.","authors":"Francesco Giganti, Nadia Moreira da Silva, Michael Yeung, Lucy Davies, Amy Frary, Mirjana Ferrer Rodriguez, Nikita Sushentsev, Nicholas Ashley, Adrian Andreou, Alison Bradley, Chris Wilson, Giles Maskell, Giorgio Brembilla, Iztok Caglic, Jakub Suchánek, Jobie Budd, Zobair Arya, Jonathan Aning, John Hayes, Mark De Bono, Nikhil Vasdev, Nimalan Sanmugalingam, Paul Burn, Raj Persad, Ramona Woitek, Richard Hindley, Sidath Liyanage, Sophie Squire, Tristan Barrett, Steffi Barwick, Mark Hinton, Anwar R Padhani, Antony Rix, Aarti Shah, Evis Sala","doi":"10.1007/s00330-024-11323-0","DOIUrl":"10.1007/s00330-024-11323-0","url":null,"abstract":"<p><strong>Objectives: </strong>Multi-centre, multi-vendor validation of artificial intelligence (AI) software to detect clinically significant prostate cancer (PCa) using multiparametric magnetic resonance imaging (MRI) is lacking. We compared a new AI solution, validated on a separate dataset from different UK hospitals, to the original multidisciplinary team (MDT)-supported radiologist's interpretations.</p><p><strong>Materials and methods: </strong>A Conformité Européenne (CE)-marked deep-learning (DL) computer-aided detection (CAD) medical device (Pi) was trained to detect Gleason Grade Group (GG) ≥ 2 cancer using retrospective data from the PROSTATEx dataset and five UK hospitals (793 patients). Our separate validation dataset was on six machines from two manufacturers across six sites (252 patients). Data included in the study were from MRI scans performed between August 2018 to October 2022. Patients with a negative MRI who did not undergo biopsy were assumed to be negative (90.4% had prostate-specific antigen density < 0.15 ng/mL²). ROC analysis was used to compare radiologists who used a 5-category suspicion score.</p><p><strong>Results: </strong>GG ≥ 2 prevalence in the validation set was 31%. Evaluated per patient, Pi was non-inferior to radiologists (considering a 10% performance difference as acceptable), with an area under the curve (AUC) of 0.91 vs. 0.95. At the predetermined risk threshold of 3.5, the AI software's sensitivity was 95% and specificity 67%, while radiologists at Prostate Imaging-Reporting and Data Systems/Likert ≥ 3 identified GG ≥ 2 with a sensitivity of 99% and specificity of 73%. AI performed well per-site (AUC ≥ 0.83) at the patient-level independent of scanner age and field strength.</p><p><strong>Conclusion: </strong>Real-world data testing suggests that Pi matches the performance of MDT-supported radiologists in GG ≥ 2 PCa detection and generalises to multiple sites, scanner vendors, and models.</p><p><strong>Key points: </strong>QuestionThe performance of artificial intelligence-based medical tools for prostate MRI has yet to be evaluated on multi-centre, multi-vendor data to assess generalisability. FindingsA dedicated AI medical tool matches the performance of multidisciplinary team-supported radiologists in prostate cancer detection and generalises to multiple sites and scanners. Clinical relevanceThis software has the potential to support the MRI process for biopsy decision-making and target identification, but future prospective studies, where lesions identified by artificial intelligence are biopsied separately, are needed.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4915-4924"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522992","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":"Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models.","authors":"Babak Salam, Claire Stüwe, Sebastian Nowak, Alois M Sprinkart, Maike Theis, Dmitrij Kravchenko, Narine Mesropyan, Tatjana Dell, Christoph Endler, Claus C Pieper, Daniel L Kuetting, Julian A Luetkens, Alexander Isaak","doi":"10.1007/s00330-025-11438-y","DOIUrl":"10.1007/s00330-025-11438-y","url":null,"abstract":"<p><strong>Purpose: </strong>Large language models (LLMs) like Generative Pre-trained Transformer 4 (GPT-4) can assist in detecting errors in radiology reports, but privacy concerns limit their clinical applicability. This study compares closed-source and privacy-compliant open-source LLMs for detecting common errors in radiology reports.</p><p><strong>Materials and methods: </strong>A total of 120 radiology reports were compiled (30 each from X-ray, ultrasound, CT, and MRI). Subsequently, 397 errors from five categories (typographical, numerical, findings-impression discrepancies, omission/insertion, interpretation) were inserted into 100 of these reports; 20 reports were left unchanged. Two open-source models (Llama 3-70b, Mixtral 8x22b) and two commercial closed-source (GPT-4, GPT-4o) were tasked with error detection using identical prompts. The Kruskall-Wallis test and paired t-test were used for statistical analysis.</p><p><strong>Results: </strong>Open-source LLMs required less processing time per radiology report than closed-source LLMs (6 ± 2 s vs. 13 ± 4 s; p < 0.001). Closed-source LLMs achieved higher error detection rates than open-source LLMs (GPT-4o: 88% [348/397; 95% CI: 86, 92], GPT-4: 83% [328/397; 95% CI: 80, 87], Llama 3-70b: 79% [311/397; 95% CI: 76, 83], Mixtral 8x22b: 73% [288/397; 95% CI: 68, 77]; p < 0.001). Numerical errors (88% [67/76; 95% CI: 82, 93]) were detected significantly more often than typographical errors (75% [65/86; 95% CI: 68, 82]; p = 0.02), discrepancies between findings and impression (73% [73/101; 95% CI: 67, 80]; p < 0.01), and interpretation errors (70% [50/71; 95% CI: 62, 78]; p = 0.001).</p><p><strong>Conclusion: </strong>Open-source LLMs demonstrated effective error detection, albeit with comparatively lower accuracy than commercial closed-source models, and have potential for clinical applications when deployed via privacy-compliant local hosting solutions.</p><p><strong>Key points: </strong>Question Can privacy-compliant open-source large language models (LLMs) match the error-detection performance of commercial non-privacy-compliant closed-source models in radiology reports? Findings Closed-source LLMs achieved slightly higher accuracy in detecting radiology report errors than open-source models, with Llama 3-70b yielding the best results among the open-source models. Clinical relevance Open-source LLMs offer a privacy-compliant alternative for automated error detection in radiology reports, improving clinical workflow efficiency while ensuring patient data confidentiality. Further refinement could enhance their accuracy, contributing to better diagnosis and patient care.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4549-4557"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143467391","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":"Important radiological and clinicopathological risk factors for the recurrence of intraductal papillary mucinous neoplasms after surgical resection.","authors":"Junghoan Park, Jung Hoon Kim, Rae Rim Ryu, Sungjun Hwang","doi":"10.1007/s00330-025-11431-5","DOIUrl":"10.1007/s00330-025-11431-5","url":null,"abstract":"<p><strong>Objectives: </strong>To assess significant radiological and clinicopathological risk factors for post-surgery recurrence in patients with intraductal papillary mucinous neoplasm (IPMN).</p><p><strong>Materials and methods: </strong>Patients with IPMNs who underwent surgery from 2011 to 2021 at a single center were retrospectively included. Two reviewers evaluated CT findings according to international guidelines. Clinicopathological data were collected from medical records and surgical pathology reports. Patients were monitored for recurrence with contrast-enhanced CT or MRI up to 2023. Univariable Cox regression analysis included potential risk factors: all high-risk stigmata and worrisome features in the international guidelines, age, sex, tumor location, type, carcinoembryonic antigen, surgery type, postsurgical residual cyst, adjuvant treatment, pathologic grade, type, size, margin status, lymph node metastasis, gland type, and pancreatic intraepithelial neoplasia. Variables with p < 0.2 were included in multivariate analysis.</p><p><strong>Results: </strong>Among 332 patients (mean age, 66.3 ± 9.0 years; 212 men), recurrence occurred in 39 (11.7%) over a median follow-up of 3.2 years (range: 0.1-12.3 years). Two- and five-year recurrence-free survival rates were 91.2% and 86.4%, respectively. Significant radiological risk factors included enhancing mural nodule (EMN) presence (hazard ratio [HR] 5.088, p = 0.007) and lymphadenopathy (HR 2.837, p = 0.01). Associated invasive carcinoma (HR 25.030), lymph node metastasis (HR 27.562), adjuvant treatment (HR 0.203), and history of pancreatitis (HR 2.608) were also significant. Most imaging features showed moderate to excellent interobserver agreement, except for thickened/enhancing cyst walls (κ, 0.25).</p><p><strong>Conclusion: </strong>The presence of EMNs and lymphadenopathy, along with several clinicopathologic factors, were significantly associated with IPMN recurrence.</p><p><strong>Key points: </strong>Question Understanding postoperative recurrence risk in IPMN patients is crucial for determining surveillance strategies; however, research on radiologic risk factors remains limited. Findings The presence of EMNs and lymphadenopathy were identified as significant radiologic risk factors for the postoperative recurrence of IPMN, along with clinicopathologic factors. Clinical relevance IPMN recurrence is significantly associated with imaging findings like EMNs and lymphadenopathy, as well as clinical and pathologic factors. It can guide the development of tailored postoperative surveillance strategies for IPMN patients in further studies.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"5004-5016"},"PeriodicalIF":4.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457404","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: Navigating bias in machine learning-reevaluating feature importances through robust statistical analysis.","authors":"Yoshiyasu Takefuji","doi":"10.1007/s00330-025-11797-6","DOIUrl":"https://doi.org/10.1007/s00330-025-11797-6","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559550","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":"Diagnostic value of transcranial ultrasonography and clinical features for Parkinson's disease based on XGBoost model and SHAP visualization analysis: a retrospective study.","authors":"Yanan Ge, Xuelei Zhang, Xiaoming Ding, Panpan Zhang, Liyang Su, Gang Wang","doi":"10.1007/s00330-025-11789-6","DOIUrl":"https://doi.org/10.1007/s00330-025-11789-6","url":null,"abstract":"<p><strong>Objectives: </strong>Parkinson's disease (PD) requires early diagnosis for optimal management. This study aims to evaluate whether combining transcranial ultrasonography (TCS) and clinical data using an interpretable machine learning model improves diagnostic accuracy.</p><p><strong>Materials and methods: </strong>In this retrospective study, data from patients who underwent TCS between May 2023 and December 31, 2024, were retrospectively collected. Key clinical and TCS features were identified using the Boruta algorithm. An XGBoost model (an advanced gradient boosting algorithm) was developed based on these features, and Shapley Additive Explanations (SHAP, a method for interpreting machine learning predictions) was applied to visualize their contributions to PD diagnosis.</p><p><strong>Results: </strong>The study included 599 patients (480 training, 119 validation) and achieved area under the curve (AUC) values of 0.863 and 0.811 in training and validation datasets, respectively. SHAP analysis revealed that bilateral substantia nigra hyperechoic (SNHA) and the substantia nigra/midbrain ratio (S/M) were the most influential predictors.</p><p><strong>Conclusion: </strong>Integrating TCS with clinical data via XGBoost and SHAP provides high diagnostic performance and clear interpretability, supporting early PD diagnosis.</p><p><strong>Key points: </strong>Question Can TCS features combined with machine learning provide reliable diagnostic support for PD? Findings XGBoost model integrating TCS and clinical features achieved a high diagnostic performance (AUC = 0.811) and interpretable outputs via SHAP visualization analysis. Clinical relevance This interpretable AI model supports early PD diagnosis and individualized decision making using non-invasive imaging and routine clinical parameters.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559549","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":"Reduction of radiation exposure and preserved image quality using photon-counting detector cardiac computed tomography without electrocardiographic gating in children with congenital heart disease.","authors":"Susanne Hellms, Thomas Werncke, Joachim Böttcher, Christoph M Happel, Jan Eckstein, Markus Benedikt Krueger, Christoph Panknin, Alexander Pfeil, Till F Kaireit, Philipp Beerbaum, Jens Vogel-Claussen, Frank Wacker, Diane Miriam Renz","doi":"10.1007/s00330-025-11719-6","DOIUrl":"https://doi.org/10.1007/s00330-025-11719-6","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the radiation exposure, quantitative, and qualitative image quality in pediatric cardiac CT by using photon-counting detector computed tomography (PCD CT) versus energy-integrating detector CT (EID CT) in matched children.</p><p><strong>Materials and methods: </strong>Thirty-seven contrast-enhanced, clinically indicated cardiac CTs performed on PCD CT were matched with 37 examinations acquired by EID CT. The patients were matched according to water-equivalent diameters. Quantitative evaluation of image quality comprised a region of interest (ROI)-based analysis, calculating image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratio. Differences of the attenuation variation of the paraspinal and the pectoral muscles were calculated to measure beam hardening artifacts. Volume CT dose index (CTDI_vol) and dose length product (DLP) were documented, and the effective radiation dose was calculated for each patient. Statistical analysis comprised t-tests and Wilcoxon signed rank tests.</p><p><strong>Results: </strong>The mean age of the children on PCD CT was 794 ± 1016 days, similar to the mean age of 815 ± 957 days of the children on EID CT (p = 0.76). Moreover, age, height, weight, and body mass index (BMI) were also not significantly different between the two groups (p ≥ 0.32). Radiation exposure was significantly lower on PCD CT (CTDI_vol 0.20 ± 0.12 mGy and DLP 4.06 ± 3.22 mGy*cm) versus EID CT (CTDI_vol 0.37 ± 0.17 mGy, p < 0.001 and DLP 7.21 ± 4.67 mGy*cm, p < 0.001). No significant differences in SNR, CNR, or beam hardening artifacts could be observed. Qualitative image quality was also comparable for PCD CT versus EID CT.</p><p><strong>Conclusions: </strong>With a reduction in radiation exposure exceeding 40% by using PCD CT, image quality remained stable compared to EID CT. Reducing radiation with PCD CT while preserving image quality might substantially advance cardiac imaging in children.</p><p><strong>Key points: </strong>Question Children are particularly sensitive to radiation exposure, highlighting the need for dose reduction. Findings Radiation dosage can be significantly reduced while preserving image quality when using photon-counting detector (PCD) CT in pediatric patients with congenital heart disease. Clinical relevance Since radiation exposure can be significantly reduced by PCD CT compared to energy-integrating detector (EID) CT, while image quality was comparable, PCD CT is advisable for children with congenital heart disease.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552774","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":"Cost-effective integration of dynamic myocardial CT perfusion in the assessment of symptomatic coronary artery disease.","authors":"Carlos González-Freixa, Francesc Altadill Balsells, Lidia Bos Real, Martín Descalzo Buey, Juan Fernández Martínez, Mario Salido Iniesta, Konrad Pieszko, Ruben Leta Petracca, David Viladés Medel","doi":"10.1007/s00330-025-11754-3","DOIUrl":"https://doi.org/10.1007/s00330-025-11754-3","url":null,"abstract":"<p><strong>Objectives: </strong>Coronary computed tomography angiography (CCTA) is highly effective for detecting coronary artery disease (CAD) but cannot assess its hemodynamic significance, often requiring additional tests. This study evaluates the clinical performance and cost-effectiveness of integrating dynamic myocardial CT perfusion (DynCTP) into the assessment of symptomatic patients with suspected CAD or prior chronic coronary syndrome (CCS).</p><p><strong>Materials and methods: </strong>We conducted a single center, retrospective study comparing two matched cohorts. In the first cohort patients underwent CCTA followed by non-CT-based functional tests, while in the second cohort DynCTP was included for cases of potential functionally significant CAD. The study analyzed the number of additional tests, diagnostic process duration, and the incidence of major adverse cardiovascular events. A probabilistic cost analysis was performed to evaluate the economic impact.</p><p><strong>Results: </strong>A total of 205 patients were included, 71% of whom were male, with a mean age of 72.5 ± 10 years. Over a follow-up of 30 months, the CCTA+DynCTP cohort showed a 56% reduction in additional tests and 45% in time to clinical decision-making, with a higher proportion of patients requiring only the initial study. No significant differences were observed in the number of invasive coronary angiograms or major adverse clinical events, although an increase in overall healthcare costs was documented.</p><p><strong>Conclusion: </strong>Integrating DynCTP into the evaluation of symptomatic patients with suspected CAD or prior CCS streamlines the diagnostic process compared to a strategy based on CCTA and other functional tests, reducing time and additional testing without increasing adverse outcomes, although it is associated with slightly higher healthcare costs.</p><p><strong>Key points: </strong>Question Evaluating the hemodynamic significance of coronary artery stenosis identified by coronary CT angiography is important to determine the best treatment strategy, but requires complementary tests. Findings DynCTP reduced the time to clinical decision-making and the need for additional testing without increasing major adverse cardiovascular events. Clinical relevance The integration of DynCTP safely streamlines the diagnostic process of CAD compared to a strategy based on CCTA and additional functional tests.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539703","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}