European Journal of Radiology Open最新文献

{"title":"3.0 T multi-parametric MRI combined with clinical features improve malignancy prediction of BI-RADS 4 lesions and preoperative prediction of Nottingham Prognostic Index","authors":"Han Zhou ,&nbsp;Haofan Huang ,&nbsp;Kaibin Huang ,&nbsp;XiaoYan Chen ,&nbsp;Yao Fu ,&nbsp;ZiJie Fu ,&nbsp;Xiaolei Zhang ,&nbsp;Renhua Wu ,&nbsp;Yi Gao ,&nbsp;Yan Lin","doi":"10.1016/j.ejro.2025.100665","DOIUrl":"10.1016/j.ejro.2025.100665","url":null,"abstract":"<div><h3>Purpose</h3><div>To establish an optimal model to improve the malignancy prediction of BI-RADS 4 lesions and the preoperative prediction of tumor prognosis.</div></div><div><h3>Materials and methods</h3><div>Ninety-six patients with 126 histopathology-confirmed breast lesions were included in the study. Conventional imaging features, radiomic features based on 3.0 T multi-parametric MRI and patient`s clinical characteristics were analyzed and selected as model candidate features. The least absolute shrinkage and selection operator (Lasso) and Random Forest (RF) were used to construct the combined model. Receiver operating characteristic (ROC) and Net Reclassification Improvement Index (NRI) were performed to assess the diagnostic efficiency between the model and BI-RADS category. Relative ratio (RR) was calculated to assess the ability of model to predict the invasiveness of breast cancers. Finally, the malignant probability (MP) calculated by the optimal model, MRI-based size and lymph node (LN) stage were used by logistic algorithm to construct a preoperative Nottingham Prognostic Index (NPI) model.</div></div><div><h3>Results</h3><div>The combined model incorporating multi-parametric MRI and clinical characteristics was superior to BI-RADS category in the diagnosis of breast cancer (NRI: 1.71, p &lt; 0.05), and had an accuracy of 94 % to predict the malignancy of BI-RADS 4 lesions<strong>.</strong> In addition, MP calculated by the combined model in association with MRI-based size and LN stage can accurately predict the NPI preoperatively (AUC: 92.1 %).</div></div><div><h3>Conclusions</h3><div>The combined model based on multi-parametric MRI and clinical characteristics improves the malignancy prediction of BI-RADS 4 lesions and the preoperative prediction of NPI, therefore providing comprehensive information on the characteristics and treatment plans for breast cancer.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100665"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an AI model for pneumothorax imaging: Dataset and model optimization strategies for real-world deployment","authors":"Wen-Chang Tseng ,&nbsp;Yung-Cheng Wang ,&nbsp;Wei-Chi Chen ,&nbsp;Kang-Ping Lin","doi":"10.1016/j.ejro.2025.100664","DOIUrl":"10.1016/j.ejro.2025.100664","url":null,"abstract":"<div><h3>Purpose</h3><div>This study develops an AI-assisted pneumothorax diagnosis system using deep learning and chest X-ray images to enhance diagnostic efficiency and accuracy, reduce radiologists' workload, and provide timely treatment. The system addresses limitations of traditional methods, which rely on subjective interpretation and are vulnerable to fatigue or inexperience.</div></div><div><h3>Methods</h3><div>The DenseNet121 model was employed using a chest X-ray dataset from a medical center in northern Taiwan, with a total of 6888 images’ divided into training (64 %), validation (16 %), and testing (20 %) sets. Image preprocessing involved normalization, data augmentation (rotation, translation, scaling, brightness adjustment), and standardization. The model was trained using stochastic gradient descent with an initial learning rate of 0.0016 for 150 epochs. Performance evaluation included accuracy, sensitivity, specificity, and AUROC, integrating with the hospital's PACS for real-time analysis.</div></div><div><h3>Results</h3><div>Initial testing yielded AUROC values of 94.52 % and 97.21 % for pneumothorax and mild pneumothorax groups. However, when applied to 6888 clinical images, the AUROC dropped to 62.55 %, resulting in 4294 false positives. Adjusting the dataset split and retraining with 1000 false positive images improved the AUROC from 62.55 % to 85.53 %.</div></div><div><h3>Conclusions</h3><div>The AI model shows potential in pneumothorax detection, but performance is influenced by data diversity, image quality, and clinical complexity. The model struggles to identify key areas in complex cases, indicating a need for attention mechanisms or region proposal networks (RPN). Expanding the dataset, optimizing preprocessing, and training separate models for different image locations could enhance performance further.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100664"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive model combining radiomics and risk factors for predicting massive hemorrhage in cesarean scar pregnancy during dilatation and curettage","authors":"Feng Gao ,&nbsp;Le Fu ,&nbsp;Zhuoying Zhang ,&nbsp;Yafen Li ,&nbsp;Zeyi Zhang ,&nbsp;Yong Zhang ,&nbsp;Yichen Zhang ,&nbsp;Jie Shi ,&nbsp;Jiejun Cheng","doi":"10.1016/j.ejro.2025.100661","DOIUrl":"10.1016/j.ejro.2025.100661","url":null,"abstract":"<div><h3>Background</h3><div>To develop a comprehensive model integrating MRI radiomics signatures and independent risk factors for predicting the risk of massive bleeding during dilatation and curettage(D&amp;C) in patients with cesarean scar pregnancy (CSP).</div></div><div><h3>Methods</h3><div>CSP patients who underwent D&amp;C were retrospectively reviewed. Intraoperative massive bleeding was defined as bleeding exceeding 200 ml based on surgical records. Three-dimensional MRI T2-weighted images were obtained, and radiomics signatures were extracted from the gestational sac (GS). Subjects were randomly separated into the training and testing sets in a 7:3 ratio. Radiomics features and clinical variables were analyzed to conduct both radiomics and clinical models. The nomogram was established by combining Radscore and the selected clinical variables.</div></div><div><h3>Results</h3><div>Among 109 CSP patients, 33 patients experienced massive hemorrhage while 76 patients did not. Serum β-hCG and the maximum inlet diameter of the CSD (P &lt; 0.05) were identified as significant clinical prognostic factors for massive hemorrhage. The nomogram demonstrated superior AUCs of 0.962 (95 % CI 0.928–0.989) and 0.926 (95 % CI 0.843–0.987) in the training and testing cohorts, respectively, Delong’s test was employed to compare the AUCs of the nomogram with those of the radiomics model and the clinical model. The results showed no significant differences between the nomogram and the other models in both the training (p &gt; 0.05) and testing cohorts (p &gt; 0.05). The nomogram calibration curve exhibited good agreement, with no significant differences found in the Hosmer-Lemeshow test (all p &gt; 0.05). DCA revealed a substantial overall net benefit for the nomogram.</div></div><div><h3>Conclusions</h3><div>Our study achieved accurate prediction of massive hemorrhage during D&amp;C in CSP patients by integrating MRI radiomics and clinical features, underscoring the synergistic effectiveness of radiomics combined with clinical variables. The combined nomogram offered valuable support for precise preoperative risk assessment and individualized treatment decisions.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100661"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of resting-state perfusion CMR in the evaluation of microvascular obstruction in patients with acute myocardial infarction: A clinical perspective","authors":"Yingying Hu ,&nbsp;Zidi Wang ,&nbsp;Zheng Sun ,&nbsp;Zhi Liu ,&nbsp;Jie Lu","doi":"10.1016/j.ejro.2025.100662","DOIUrl":"10.1016/j.ejro.2025.100662","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate the clinical application value of cardiac resting-state perfusion weight imaging (rs-PWI)-derived parameters in patients with acute myocardial infarction (AMI) complicated by microvascular obstruction (MVO).</div></div><div><h3>Methods</h3><div>Overall, 300 patients with AMI were prospectively enrolled, and divided into the MVO and non-MVO groups, based on the presence of MVO in the infarcted myocardium. Differences in rs-PWI imaging parameters, and the diagnostic value of rs-PWI in reperfusion myocardial ischemia at segment level and MVO were quantitatively evaluated.</div></div><div><h3>Results</h3><div>The average age was 58.60 ± 13.03 years, and 246/300 (82 %) were males. The MVO group had 176 patients (mean age: 57.90 ± 12.47), including 140 (80 %) males. The left ventricular (LV) volumes occupied by the infarcted myocardium were 19.60 ± 2.70 %LV and 15.20 ± 3.40 %LV in the MVO and non-MVO groups, respectively (<em>P</em> &lt; 0.05). There were 679 LGE positive segments in the MVO group (679/2816, 24.1 %). The area under curve (AUC), sensitivity, specificity, and Jordan index of rs-PWI for MVO diagnosis were 0.95(0.89–0.99), 94.3 %, 93.4 %, and 0.88, respectively. At the segmental level, the maximum rising slope was higher in the MVO than non-MVO group (15.09 ± 2.64 vs. 6.21 ± 1.25, <em>P</em> &lt; 0.05). The time to peak 20 %-80 % was shorter in the MVO group (4.07 ± 0.79 vs. 7.75 ± 1.03, <em>P</em> &lt; 0.05). Comparison revealed differences in perfusion indices (MVO: 0.32 ± 0.09 vs. non-MVO: 0.42 ± 0.04, <em>P</em> &lt; 0.05). The highest diagnostic value for MVO among rs-PWI parameters was AUC 0.90(0.84–0.97), sensitivity 94.1 %, specificity 88.7 %, and accuracy 91.1 %.</div></div><div><h3>Conclusion</h3><div>CMR rs-PWI sequence effectively evaluates reperfusion myocardial ischemia complicated with MVO, while the perfusion index has high diagnostic value in quantifying myocardial blood flow potential.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100662"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain MRI morphometry for structural alterations in patients with glioma – A systematic review","authors":"Marcin Stański ,&nbsp;Jacek Wątorek ,&nbsp;Sylwia Antczak ,&nbsp;Mateusz Ciesielski ,&nbsp;Barbara Katulska ,&nbsp;Mikołaj Goralewski ,&nbsp;Jakub Moskal ,&nbsp;Katarzyna Katulska","doi":"10.1016/j.ejro.2025.100660","DOIUrl":"10.1016/j.ejro.2025.100660","url":null,"abstract":"<div><h3>Background</h3><div>It is already known that patients with glioma develop functional plasticity, including recruiting regions of contralateral hemisphere. However, it is still unclear, if and what kind of structural changes in contralateral hemisphere are present, and there is lack of comprehensive comparison of studies on this issue.</div></div><div><h3>Objectives</h3><div>First aim of this review was to summarize methodology and findings of morphometric studies of contralateral hemisphere of patients with glioma before treatment. Second aim was to discuss the possible neurobiological background of changes, methodological difficulties and possibilities, and to identify challenges for future studies.</div></div><div><h3>Material and methods</h3><div>Neuroimaging studies were searched in four electronic databases. Found studies were compared and discussed regarding their methodology and outcomes, and undergone thorough quality assessment.</div></div><div><h3>Results</h3><div>In this systematic review, we eventually included 16 studies from 2080 initially found articles. Analyzed groups of patients suffered from different types and grades of gliomas. For brain scan analyses, authors used voxel-based or surface-based morphometry. Results differed across studies, reporting both increase and atrophy of contralateral grey matter. We identified some methodological issues in papers, which were further discussed.</div></div><div><h3>Conclusions</h3><div>Contralateral hemisphere in glioma patients undergoes complicated structural changes, including grey matter volume increase and atrophy, which both could be signs of compensation. These are dependent on tumor location, grade of glioma, individual attributes of a given patient, and should be interpreted carefully. There is still need for further research, and we present challenges and issues which should be overcome.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100660"},"PeriodicalIF":1.8,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Habitat imaging radiomics increases the accuracy of a nomogram for predicting Ki-67-positivity in laryngeal squamous cell carcinoma","authors":"Yumeng Dong ,&nbsp;Siyu Yang ,&nbsp;Xiaoke Jing ,&nbsp;Xiaoqing Hu ,&nbsp;Yun Liang ,&nbsp;Jun Wang ,&nbsp;Gang Liang ,&nbsp;Sheng He ,&nbsp;Zengyu Jiang","doi":"10.1016/j.ejro.2025.100659","DOIUrl":"10.1016/j.ejro.2025.100659","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the value of applying habitat imaging (HI) radiomics on venous-phase computed tomography (CT) images from laryngeal squamous cell carcinoma (LSCC) patients, as part of a nomogram to predict Ki-67 positivity, an indicator of poorer LSCC prognoses.</div></div><div><h3>Methods</h3><div>Clinical and CT imaging data from 128 LSCC patients, divided into training (89) and testing (39) groups, were analyzed. Conventional and HI radiomics features were extracted from enhanced venous phase images, either from the entire tumor (conventional) or 3 sub-regions (HI). Radiomics models were established, based on 5 machine learning algorithms, while clinical characteristics were analyzed by both uni- and multi-variate logistic regression analyses for their associations with Ki-67 positivity. Afterwards, a predictive nomogram was constructed by combining clinical characteristics, conventional radiomics, and HI radiomics.</div></div><div><h3>Results</h3><div>The only clinical characteristic strongly predictive for Ki-67-positivity is the degree of differentiation (low/medium vs. high). Additionally, HI radiomics was significantly more accurate than conventional for predicting Ki-67-positivity. The most accurate model, though, was the predictive nomogram, with areas under the curve of 0.945 (training) and 0.871 (testing), which was significantly higher than for clinical characteristics, conventional radiomics and HI radiomics models alone; it also had the highest net benefit, and thus greatest clinical utility under decision curve analysis.</div></div><div><h3>Conclusions</h3><div>HI radiomics features were more accurate for predicting Ki-67-positivity in LSCC than conventional radiomics. However, the combination of those features with conventional radiomics and the degree of differentiation in a predictive nomogram yields the most accurate model for Ki-67-positivity.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100659"},"PeriodicalIF":1.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study","authors":"Gagandeep Singh ,&nbsp;Annie Singh ,&nbsp;Tejasvi Kainth ,&nbsp;Sudhir Suman ,&nbsp;Nicole Sakla ,&nbsp;Luke Partyka ,&nbsp;Tej Phatak ,&nbsp;Prateek Prasanna","doi":"10.1016/j.ejro.2025.100657","DOIUrl":"10.1016/j.ejro.2025.100657","url":null,"abstract":"<div><h3>Rational and objectives</h3><div>Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. Currently, Computed Tomography Pulmonary Angiography (CTPA) serves as diagnostic gold standard for detecting PE. However, its efficacy is limited by factors such as contrast bolus timing, physician-dependent diagnostic accuracy, and time taken for scan interpretation. To address these limitations, we propose an AI-based PE triaging model (AID-PE) designed to predict the presence and key characteristics of PE on CTPA. This model aims to enhance diagnostic accuracy, efficiency, and the speed of PE identification.</div></div><div><h3>Materials and methods</h3><div>We trained AID-PE on the RSNA-STR PE CT (RSPECT) Dataset, N = 7279 and subsequently tested it on an in-house dataset (n = 106). We evaluated efficiency in a separate dataset (D₄, n = 200) by comparing the time from scan to report in standard PE detection workflow versus AID-PE.</div></div><div><h3>Results</h3><div>A comparative analysis showed that AID-PE had an AUC/accuracy of 0.95/0.88. In contrast, a Convolutional Neural Network (CNN) classifier and a CNN-Long Short-Term Memory (LSTM) network without an attention module had an AUC/accuracy of 0.5/0.74 and 0.88/0.65, respectively. Our model achieved AUCs of 0.82 and 0.95 for detecting PE on the validation dataset and the independent test set, respectively. On D₄, AID-PE took an average of 1.32 s to screen for PE across 148 CTPA studies, compared to an average of 40 min in contemporary workflow.</div></div><div><h3>Conclusion</h3><div>AID-PE outperformed a baseline CNN classifier and a single-stage CNN-LSTM network without an attention module. Additionally, its efficiency is comparable to the current radiological workflow.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100657"},"PeriodicalIF":1.8,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring scenarios for implementing fast quantitative MRI","authors":"Susan V. van Hees ,&nbsp;Martin B. Schilder ,&nbsp;Alexandra Keyser ,&nbsp;Alessandro Sbrizzi ,&nbsp;Jordi P.D. Kleinloog ,&nbsp;Wouter P.C. Boon","doi":"10.1016/j.ejro.2025.100658","DOIUrl":"10.1016/j.ejro.2025.100658","url":null,"abstract":"<div><h3>Purpose</h3><div>MRI waitlists and discomfort from long scanning sessions are significant problems in clinical radiology. Novel multiparametric quantitative MRI techniques (qMRI) for radiological imaging enable acquisition of full-brain data within minutes to address these problems. While technical and clinical work is advancing, there has been limited research on implementing fast qMRI. This paper aims to identify implementation factors and scenarios within a healthcare setting facing rising demand, staff shortages, and limited capacity of MRI systems.</div></div><div><h3>Methods</h3><div>The paper reports on data collected using qualitative methods: 1) Interviews and guided discussions, 2) co-creation workshop. Both steps involved key representatives with various backgrounds and expertise, such as radiologists, lab technicians, insurers, and patients.</div></div><div><h3>Results</h3><div>Workshop participants visualised current and future workflows, which helped articulate implementation factors for qMRI. Supply and demand in MRI will change with increased accessibility and shortened timeslots. Three implementation scenarios came forward: 1) stable deployment, 2) extension to conducting more complex diagnostic exams, and 3) (more) preventive screening.</div></div><div><h3>Discussion and conclusions</h3><div>This paper demonstrates challenges, solutions, and opportunities for successfully implementing fast qMRI in the clinic, and five lessons for adoption in the clinic: 1) importance of balancing perfectionism with confidence when it comes to clinicians’ expectations, 2) good use of Artificial Intelligence, 3) considering a learning curve associated with implementation, 4) regarding competing technologies, and 5) including patients’ experiences. Future research should investigate salient issues regarding future of AI in radiology and for moving imaging practices out of the clinic.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100658"},"PeriodicalIF":1.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics in differential diagnosis of pancreatic tumors","authors":"Riccardo De Robertis ,&nbsp;Beatrice Mascarin ,&nbsp;Eda Bardhi ,&nbsp;Flavio Spoto ,&nbsp;Nicolò Cardobi ,&nbsp;Mirko D’Onofrio","doi":"10.1016/j.ejro.2025.100651","DOIUrl":"10.1016/j.ejro.2025.100651","url":null,"abstract":"<div><div>The aim of this study was to assess whether radiomics could predict histotype of pancreatic ductal adenocarcinomas (PDAC) and pancreatic neuroendocrine tumors (PNET). Contrast-enhanced CT scans of 193 patients were retrospectively reviewed, encompassing 97 PDACs and 96 PNETs. Additionally, anamnestic data and laboratory data were evaluated. A total of 107 features were extracted for both the arterial and venous phases. ROC curves were constructed for the parameters with the highest AUC, considering two groups: one including all lesions and the other including only lesions smaller than 5 cm. The following feature differences were found to be statistically significant (p &lt; 0.05). Without considering lesion size: for the arterial phase, 16 first-order and 38 s-order features; for the venous phase, 10 first-order and 20 s-order features. When considering lesion size: for the arterial phase, 16 first-order and 52 s-order features; for the venous phase, 11 first-order and 36 s-order features. The radiomics features with the highest AUC values included ART_firstorder_RootMeanSquared (AUC = 0.896, p &lt; 0.01) in the arterial phase and VEN_firstorder_Median (AUC = 0.737, p &lt; 0.05) in the venous phase for all lesions, and ART_firstorder_RootMeanSquared (AUC = 0.859, p &lt; 0.01) and VEN_firstorder_Median (AUC = 0.713, p &lt; 0.05) for lesions smaller than 5 cm. Texture analysis of pancreatic pathology has shown good predictability in defining the PNET histotype. This analysis potentially offering a non-invasive, imaging-based method to accurately differentiate between pancreatic tumor types. Such advancements could lead to more precise and personalized treatment planning, ultimately optimizing the use of medical resources.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100651"},"PeriodicalIF":1.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction","authors":"Priyal Shrivastava ,&nbsp;Shivali Kashikar ,&nbsp;P.H. Parihar ,&nbsp;Pachyanti Kasat ,&nbsp;Paritosh Bhangale ,&nbsp;Prakher Shrivastava","doi":"10.1016/j.ejro.2025.100652","DOIUrl":"10.1016/j.ejro.2025.100652","url":null,"abstract":"<div><h3>Background</h3><div>Coronary artery disease (CAD) is a major worldwide health concern, contributing significantly to the global burden of cardiovascular diseases (CVDs). According to the 2023 World Health Organization (WHO) report, CVDs account for approximately 17.9 million deaths annually. This emphasizies the need for advanced diagnostic tools such as coronary computed tomography angiography (CCTA). The incorporation of deep learning (DL) technologies could significantly improve CCTA analysis by automating the quantification of plaque and stenosis, thus enhancing the precision of cardiac risk assessments. A recent meta-analysis highlights the evolving role of CCTA in patient management, showing that CCTA-guided diagnosis and management reduced adverse cardiac events and improved event-free survival in patients with stable and acute coronary syndromes.</div></div><div><h3>Methods</h3><div>An extensive literature search was carried out across various electronic databases, such as MEDLINE, Embase, and the Cochrane Library. This search utilized a specific strategy that included both Medical Subject Headings (MeSH) terms and pertinent keywords. The review adhered to PRISMA guidelines and focused on studies published between 2019 and 2024 that employed deep learning (DL) for coronary computed tomography angiography (CCTA) in patients aged 18 years or older. After implementing specific inclusion and exclusion criteria, a total of 10 articles were selected for systematic evaluation regarding quality and bias.</div></div><div><h3>Results</h3><div>This systematic review included a total of 10 studies, demonstrating the high diagnostic performance and predictive capabilities of various deep learning models compared to different imaging modalities. This analysis highlights the effectiveness of these models in enhancing diagnostic accuracy in imaging techniques. Notably, strong correlations were observed between DL-derived measurements and intravascular ultrasound findings, enhancing clinical decision-making and risk stratification for CAD.</div></div><div><h3>Conclusion</h3><div>Deep learning-enabled CCTA represents a promising advancement in the quantification of coronary plaques and stenosis, facilitating improved cardiac risk prediction and enhancing clinical workflow efficiency. Despite variability in study designs and potential biases, the findings support the integration of DL technologies into routine clinical practice for better patient outcomes in CAD management.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100652"},"PeriodicalIF":1.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}