Insights into Imaging最新文献

{"title":"Multi-model quantitative MRI of uterine cancers in precision medicine's era-a narrative review.","authors":"Marco Gennarini, Rossella Canese, Silvia Capuani, Valentina Miceli, Federica Tomao, Innocenza Palaia, Valentina Zecca, Alessandra Maiuro, Ilaria Balba, Carlo Catalano, Stefania Maria Rita Rizzo, Lucia Manganaro","doi":"10.1186/s13244-025-01965-z","DOIUrl":"10.1186/s13244-025-01965-z","url":null,"abstract":"<p><strong>Purpose: </strong>This review aims to summarize the current applications of quantitative MRI biomarkers in the staging, treatment response evaluation, and prognostication of endometrial (EC) and cervical cancer (CC). By focusing on functional imaging techniques, we explore how these biomarkers enhance personalized cancer management beyond traditional morphological assessments.</p><p><strong>Methods: </strong>A structured search of the PubMed database from January to May 2024 was conducted to identify relevant studies on quantitative MRI in uterine cancers. We included studies examining MRI biomarkers like Dynamic Contrast-Enhanced MRI (DCE-MRI), Diffusion-Weighted Imaging (DWI), and Magnetic Resonance Spectroscopy (MRS), emphasizing their roles in assessing tumor physiology, microstructure, and metabolic changes.</p><p><strong>Results: </strong>DCE-MRI provides valuable quantitative biomarkers such as Ktrans and Ve, which reflect microvascular characteristics and tumor aggressiveness, outperforming T2-weighted imaging in detecting critical factors like myometrial and cervical invasion. DWI, including advanced models like Intravoxel Incoherent Motion (IVIM), distinguishes between normal and cancerous tissue and correlates with tumor grade and treatment response. MRS identifies metabolic alterations, such as elevated choline and lipid signals, which serve as prognostic markers in uterine cancers.</p><p><strong>Conclusion: </strong>Quantitative MRI offers a noninvasive method to assess key biomarkers that inform prognosis and guide treatment decisions in uterine cancers. By providing insights into tumor biology, these imaging techniques represent a significant step forward in the precision medicine era, allowing for a more tailored therapeutic approach based on the unique pathological and molecular characteristics of each tumor.</p><p><strong>Critical relevance statement: </strong>Biomarkers obtained from MRI can provide useful quantitative information about the nature of uterine cancers and their prognosis, both at diagnosis and response assessment, allowing better therapeutic strategies to be prepared.</p><p><strong>Key points: </strong>Quantitative MRI improves diagnosis and management of uterine cancers through advanced imaging biomarkers. Quantitative MRI biomarkers enhance staging, prognosis, and treatment response assessment in uterine cancers. Quantitative MRI biomarkers support personalized treatment strategies and improve patient management in uterine cancers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"113"},"PeriodicalIF":4.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173768","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":"Solitary fibrous tumors from A to Z: a pictorial review with radiologic-pathologic correlation.","authors":"Fatmaelzahraa Abdelfattah Denewar, Mitsuru Takeuchi, Doaa Khedr, Fatma Mohamed Sherif, Farah A Shokeir, Misugi Urano, Ahmed E Eladl","doi":"10.1186/s13244-025-01991-x","DOIUrl":"10.1186/s13244-025-01991-x","url":null,"abstract":"<p><p>Solitary fibrous tumors (SFTs) represent a rare subset of mesenchymal neoplasms, affecting 1-2 per million people, with no gender preference. They demonstrate indolent behavior, frequent asymptomatic presentation, and widespread anatomical involvement. At imaging, SFTs typically appear as well-defined, predominantly hypervascular masses with varying degrees of cystic change and necrosis, though calcification is rare. Avid heterogeneous enhancement is typical following intravenous contrast administration, with multiple blood vessels observed at the periphery. Although findings on CT and MRI alone are generally nonspecific, a frequent feature of SFTs at MRI is the presence of rounded or linear low signal intensity foci on T1- and T2-weighted images, corresponding to the fibrous and collagenous content. Nevertheless, because the imaging features of SFTs overlap with those of many benign and malignant tumors, histologic confirmation is required for the final diagnosis. A comprehensive understanding of SFTs' multifaceted clinical, pathological, and radiological presentations across various organs is crucial for accurate diagnosis and effective management. CRITICAL RELEVANCE STATEMENT: A comprehensive understanding of the classic radiological and pathological features of solitary fibrous tumors across various organs is crucial for accurate diagnosis and effective management. KEY POINTS: Solitary fibrous tumors (SFTs) are rare hypervascular fibrous tumors with indolent behavior. Imaging features of SFTs overlap with many other tumors, necessitating histologic confirmation. Understanding SFTs' radiological presentations is crucial for accurate diagnosis and effective management.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"112"},"PeriodicalIF":4.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173769","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":"Evaluation of a deep-learning segmentation model for patients with colorectal cancer liver metastases (COALA) in the radiological workflow.","authors":"Michiel Zeeuw, Jacqueline Bereska, Marius Strampel, Luuk Wagenaar, Boris Janssen, Henk Marquering, Ruby Kemna, Jan Hein van Waesberghe, Janneke van den Bergh, Irene Nota, Shira Moos, Yung Nio, Marnix Kop, Jakob Kist, Femke Struik, Nina Wesdorp, Jules Nelissen, Katinka Rus, Alexandra de Sitter, Jaap Stoker, Joost Huiskens, Inez Verpalen, Geert Kazemier","doi":"10.1186/s13244-025-01984-w","DOIUrl":"10.1186/s13244-025-01984-w","url":null,"abstract":"<p><strong>Objectives: </strong>For patients with colorectal liver metastases (CRLM), total tumor volume (TTV) is prognostic. A deep-learning segmentation model for CRLM to assess TTV called COlorectal cAncer Liver metastases Assessment (COALA) has been developed. This study evaluated COALA's performance and practical utility in the radiological picture archiving and communication system (PACS). A secondary aim was to provide lessons for future researchers on the implementation of artificial intelligence (AI) models.</p><p><strong>Methods: </strong>Patients discussed between January and December 2023 in a multidisciplinary meeting for CRLM were included. In those patients, CRLM was automatically segmented in portal-venous phase CT scans by COALA and integrated with PACS. Eight expert abdominal radiologists completed a questionnaire addressing segmentation accuracy and PACS integration. They were also asked to write down general remarks.</p><p><strong>Results: </strong>In total, 57 patients were evaluated. Of those patients, 112 contrast-enhanced portal-venous phase CT scans were analyzed. Of eight radiologists, six (75%) evaluated the model as user-friendly in their radiological workflow. Areas of improvement of the COALA model were the segmentation of small lesions, heterogeneous lesions, and lesions at the border of the liver with involvement of the diaphragm or heart. Key lessons for implementation were a multidisciplinary approach, a robust method prior to model development and organizing evaluation sessions with end-users early in the development phase.</p><p><strong>Conclusion: </strong>This study demonstrates that the deep-learning segmentation model for patients with CRLM (COALA) is user-friendly in the radiologist's PACS. Future researchers striving for implementation should have a multidisciplinary approach, propose a robust methodology and involve end-users prior to model development.</p><p><strong>Critical relevance statement: </strong>Many segmentation models are being developed, but none of those models are evaluated in the (radiological) workflow or clinically implemented. Our model is implemented in the radiological work system, providing valuable lessons for researchers to achieve clinical implementation.</p><p><strong>Key points: </strong>Developed segmentation models should be implemented in the radiological workflow. Our implemented segmentation model provides valuable lessons for future researchers. If implemented in clinical practice, our model could allow for objective radiological evaluation.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"110"},"PeriodicalIF":4.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132314","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":"Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks.","authors":"Hongna Tan, Qingxia Wu, Yaping Wu, Bingjie Zheng, Bo Wang, Yan Chen, Lijuan Du, Jing Zhou, Fangfang Fu, Huihui Guo, Cong Fu, Lun Ma, Pei Dong, Zhong Xue, Dinggang Shen, Meiyun Wang","doi":"10.1186/s13244-025-01983-x","DOIUrl":"10.1186/s13244-025-01983-x","url":null,"abstract":"<p><strong>Purpose: </strong>We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography.</p><p><strong>Methods: </strong>Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian women between August 2012 and December 2018 were enrolled. The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3-4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. We further evaluate AIS by conducting a counterbalance-designed AI-assisted study, where ten radiologists read 1302 cases with/without AIS assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score were measured.</p><p><strong>Results: </strong>The AIS yielded AUC values of 0.995, 0.933, and 0.947 for malignancy diagnosis in the validation set, testing set 1, and testing set 2, respectively. Within BI-RADS 3-4 subgroups with pathological results, AIS downgraded 83.1% of false-positives into benign groups, and upgraded 54.1% of false-negatives into malignant groups. AIS also successfully assisted radiologists in identifying 7 out of 43 malignancies initially diagnosed with BI-RADS 0, with a specificity of 96.7%. In the counterbalance-designed AI-assisted study, the average AUC across ten readers significantly improved with AIS assistance (p = 0.001).</p><p><strong>Conclusion: </strong>AIS can accurately detect and diagnose breast cancer on mammography and further serve as a supportive tool for BI-RADS categorization.</p><p><strong>Critical relevance statement: </strong>An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists.</p><p><strong>Key points: </strong>The false positive and negative rates of mammography diagnosis remain high. The AIS can yield a high AUC for malignancy diagnosis. The AIS is important in stratifying BI-RADS categorization.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"109"},"PeriodicalIF":4.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110836","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":"Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.","authors":"Bangxin Xiao, Yang Lv, Canjie Peng, Zongjie Wei, Qiao Xv, Fajin Lv, Qing Jiang, Huayun Liu, Feng Li, Yingjie Xv, Quanhao He, Mingzhao Xiao","doi":"10.1186/s13244-025-01988-6","DOIUrl":"10.1186/s13244-025-01988-6","url":null,"abstract":"<p><strong>Objectives: </strong>Lymphovascular invasion significantly impacts the prognosis of urothelial carcinoma of the bladder. Traditional lymphovascular invasion detection methods are time-consuming and costly. This study aims to develop a deep learning-based model to preoperatively predict lymphovascular invasion status in urothelial carcinoma of bladder using CT images.</p><p><strong>Methods: </strong>Data and CT images of 577 patients across four medical centers were retrospectively collected. The largest tumor slices from the transverse, coronal, and sagittal planes were selected and used to train CNN models (InceptionV3, DenseNet121, ResNet18, ResNet34, ResNet50, and VGG11). Deep learning features were extracted and visualized using Grad-CAM. Principal Component Analysis reduced features to 64. Using the extracted features, Decision Tree, XGBoost, and LightGBM models were trained with 5-fold cross-validation and ensembled in a stacking model. Clinical risk factors were identified through logistic regression analyses and combined with DL scores to enhance lymphovascular invasion prediction accuracy.</p><p><strong>Results: </strong>The ResNet50-based model achieved an AUC of 0.818 in the validation set and 0.708 in the testing set. The combined model showed an AUC of 0.794 in the validation set and 0.767 in the testing set, demonstrating robust performance across diverse data.</p><p><strong>Conclusion: </strong>We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder. This model offers a non-invasive, cost-effective tool to assist clinicians in personalized treatment planning.</p><p><strong>Critical relevance statement: </strong>We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder.</p><p><strong>Key points: </strong>We developed a deep learning feature-based stacking model to predict lymphovascular invasion in urothelial carcinoma of the bladder patients using CT. Max cross sections from three dimensions of the CT image are used to train the CNN model. We made comparisons across six CNN networks, including ResNet50.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"108"},"PeriodicalIF":4.1,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093530","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":"Development and validation of clinical-radiomics deep learning model based on MRI for endometrial cancer molecular subtypes classification.","authors":"Wenyi Yue, Ruxue Han, Haijie Wang, Xiaoyun Liang, He Zhang, Hua Li, Qi Yang","doi":"10.1186/s13244-025-01966-y","DOIUrl":"10.1186/s13244-025-01966-y","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate a clinical-radiomics deep learning (DL) model based on MRI for endometrial cancer (EC) molecular subtypes classification.</p><p><strong>Methods: </strong>This multicenter retrospective study included EC patients undergoing surgery, MRI, and molecular pathology diagnosis across three institutions from January 2020 to March 2024. Patients were divided into training, internal, and external validation cohorts. A total of 386 handcrafted radiomics features were extracted from each MR sequence, and MoCo-v2 was employed for contrastive self-supervised learning to extract 2048 DL features per patient. Feature selection integrated selected features into 12 machine learning methods. Model performance was evaluated with the AUC.</p><p><strong>Results: </strong>A total of 526 patients were included (mean age, 55.01 ± 11.07). The radiomics model and clinical model demonstrated comparable performance across the internal and external validation cohorts, with macro-average AUCs of 0.70 vs 0.69 and 0.70 vs 0.67 (p = 0.51), respectively. The radiomics DL model, compared to the radiomics model, improved AUCs for POLEmut (0.68 vs 0.79), NSMP (0.71 vs 0.74), and p53abn (0.76 vs 0.78) in the internal validation (p = 0.08). The clinical-radiomics DL Model outperformed both the clinical model and radiomics DL model (macro-average AUC = 0.79 vs 0.69 and 0.73, in the internal validation [p = 0.02], 0.74 vs 0.67 and 0.69 in the external validation [p = 0.04]).</p><p><strong>Conclusions: </strong>The clinical-radiomics DL model based on MRI effectively distinguished EC molecular subtypes and demonstrated strong potential, with robust validation across multiple centers. Future research should explore larger datasets to further uncover DL's potential.</p><p><strong>Critical relevance statement: </strong>Our clinical-radiomics DL model based on MRI has the potential to distinguish EC molecular subtypes. This insight aids in guiding clinicians in tailoring individualized treatments for EC patients.</p><p><strong>Key points: </strong>Accurate classification of EC molecular subtypes is crucial for prognostic risk assessment. The clinical-radiomics DL model outperformed both the clinical model and the radiomics DL model. The MRI features exhibited better diagnostic performance for POLEmut and p53abn.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"107"},"PeriodicalIF":4.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077859","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":"Correction: 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/s13244-025-01982-y","DOIUrl":"10.1186/s13244-025-01982-y","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"105"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077853","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":"Correction: Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.","authors":"Junjian Shen, Qing Li, Lei Li, Tianyu Lu, Jun Han, Zongyu Xie, Peng Wang, Zirui Cao, Mengsu Zeng, Jianjun Zhou, Tianzhu Yu, Yaolin Xu, Haitao Sun","doi":"10.1186/s13244-025-01973-z","DOIUrl":"10.1186/s13244-025-01973-z","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"103"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077852","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":"Workplace equity in radiology: a nationwide survey by the Radiological Society of Finland.","authors":"Suvi Marjasuo, Milja Holstila, Jussi Hirvonen","doi":"10.1186/s13244-025-01975-x","DOIUrl":"10.1186/s13244-025-01975-x","url":null,"abstract":"<p><strong>Objectives: </strong>The issue of equity among medical professionals has been extensively discussed in recent literature. Gender inequity, in particular, is a well-documented phenomenon within scientific communities. The Radiological Society of Finland undertook a national survey to assess equity among radiologists in Finland, with the primary hypothesis of equity prevailing in the radiological community.</p><p><strong>Methods: </strong>A cross-sectional study in the form of an online questionnaire was developed to investigate occupational equity and demographic variables. This survey was disseminated to the heads of radiological departments in all Finnish public healthcare units and the largest radiological units within the private sector, with instructions to distribute to their medical staff. The questionnaire was accessible for responses from May 1 to June 16, 2024.</p><p><strong>Results: </strong>A total of 259 answers were received, representing 31% of all radiologists and residents working in Finland. Among the respondents, 137/259 (52.9%) identified as female, 118/259 (45.6%) male, and 1/259 (0.4%) other, with three choosing not to answer. A significant proportion, 63/259 (24.3%), reported having witnessed discriminatory behavior, while 41/259 (15.8%) had personally experienced discrimination. The prevalence of respondents having witnessed workplace discrimination was notably higher in female respondents (42/131, 32.1%) than in males (18/113, 15.9%) or others (0%) (p = 0.012). The most cited bases for discrimination included gender, opinion, age, and cultural background.</p><p><strong>Conclusions: </strong>Perceived discrimination is prevalent within the Finnish radiological community. Gender was reported as the most common suspected grounds of perceived discriminatory behavior.</p><p><strong>Critical relevance statement: </strong>This study is the first to explore equity and diversity among radiologists in Finland. This broader approach offers a more comprehensive perspective, and the findings aim to support efforts toward greater inclusivity and equity within the field.</p><p><strong>Key points: </strong>One-quarter of radiologists in Finland reported witnessing and one-sixth reported personally experiencing discrimination in the workplace. Gender was suspected to be the most common basis for discrimination, followed by differences in opinion, age, and cultural background. Respondents were largely unaware of whether the reported incidents had been addressed. Increasing transparency and communication may help reduce perceived discrimination.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"106"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077864","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":"Correction: The role of imaging in defining cardiovascular risk to help cancer patient management: a scoping review.","authors":"Roberto Farì, Giulia Besutti, Pierpaolo Pattacini, Guido Ligabue, Francesco Piroli, Francesca Mantovani, Alessandro Navazio, Mario Larocca, Carmine Pinto, Paolo Giorgi Rossi, Luigi Tarantini","doi":"10.1186/s13244-025-01981-z","DOIUrl":"10.1186/s13244-025-01981-z","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"104"},"PeriodicalIF":4.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077855","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}