iRadiology最新文献

{"title":"Four-Dimensional Analysis of F-18 Fluorodeoxyglucose Distribution","authors":"Nagara Tamaki,&nbsp;Tomoya Kotani,&nbsp;Yoshitomo Nakai,&nbsp;Kei Yamada","doi":"10.1002/ird3.70014","DOIUrl":"https://doi.org/10.1002/ird3.70014","url":null,"abstract":"<p>Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) is an established tool in clinical molecular imaging for evaluating metabolic activity. Advanced PET systems enable high-resolution three-dimensional analysis of FDG distribution, offering insightful information for lesion characterization. Furthermore, dynamic whole-body imaging facilitates assessment of temporal FDG uptake following its administration. This development introduces the concept of four-dimensional (4D) FDG PET/CT, enhancing the precision of lesion characterization. A critical advantage of applying 4D FDG PET/CT is the ability to distinguish pathological from physiological FDG uptake by analyzing motion-related uptake. Correcting motion and integrating serial imaging data enable accurate interpretation. Additionally, FDG uptake quantification is possible using Patlak analysis. This review discusses innovative clinical applications and quantitative techniques for analyzing FDG distribution through 4D FDG PET/CT.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 5","pages":"330-336"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145385090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Enhanced Predictive Imaging in Precision Medicine: Advancing Diagnostic Accuracy and Personalized Treatment","authors":"Aswini Rajendran,&nbsp;Rithi Angelin Rajan,&nbsp;Saranya Balasubramaniyam,&nbsp;Karthikeyan Elumalai","doi":"10.1002/ird3.70027","DOIUrl":"https://doi.org/10.1002/ird3.70027","url":null,"abstract":"<p>Artificial intelligence (AI) is changing how cancer is diagnosed, predicted, and treated, opening up new approaches to make cancer care more individualized. Rather than offering a broad but superficial overview, this review focuses on four cancers—lung, breast, brain (gliomas), and colorectal—for which AI was shown to be useful in the clinic. AI algorithms, specifically those using convolutional neural networks (CNNs), can enhance early diagnosis while realizing molecular profiling and treatment response assessment through quantitative imaging evaluations. Radiomics together with radiogenomics improves treatment accuracy through the assessment of imaging characteristics that help identify targeted genomic therapies. AI technologies can enhance tumor segmentation precision, stage determination, and target outlining capabilities, which enable adaptive radiation therapy. Initiatives that merge AI with images, clinical results, and genetic science information can deliver thorough personalized assessments that enhance treatment planning decisions. However, AI technology needs to overcome data quality issues, interpretability limitations, and generalizability challenges and needs to meet regulatory compliance requirements before achieving safe and fair implementation. The next phase of development will focus on federated learning to safeguard privacy while institutions collaborate, explainable AI to build transparent systems, and the fusion of diverse data types for comprehensive patient identification and real-time medical decision support through establishing digital twins for individualized treatment assessments. Precision oncology will be transformed by maturing innovations in predictive imaging that allow better timing of diagnosis while providing customized treatments to achieve improved medical results.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"261-278"},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Undifferentiated Embryonal Sarcoma of the Liver","authors":"Lingqing Tang,&nbsp;Bin Yang","doi":"10.1002/ird3.70025","DOIUrl":"https://doi.org/10.1002/ird3.70025","url":null,"abstract":"<p>A 45-year-old male presented with upper abdominal pain that began 1 week ago, described as intermittent and dull. Physical examination revealed tenderness in the upper abdomen. The liver was palpable 10 cm below the right midclavicular line at the costal margin. Laboratory tests showed no significant abnormalities. The computed tomography image is shown in Figure 1a. The patient underwent right hemihepatectomy with caudate lobe resection. Histopathological findings are illustrated in Figure 1b. The diagnosis was a malignant tumor with necrosis, consistent with an undifferentiated sarcoma of the liver (UESL). During a 6-month follow-up, tumor metastasis was noted in the gastrointestinal space, along with multiple masses in the anterior left lobe of the liver and right renal space, indicating tumor recurrence. Dynamic axial contrast-enhanced CT scans showing mild heterogeneous enhancement of these lesions.</p><p>UESL, is an exceedingly rare malignant liver tumor. UESL ranks third in children, with adult occurrences being particularly uncommon.</p><p>Due to the rarity of UESL, imaging features lack specificity. CT scans revealed solid components often reside at the tumor margins with irregular septations and hemorrhage. Contrast-enhanced scans may demonstrate fast in fast out enhancement or delayed enhancement patterns. This case lacks typical imaging manifestations of enhancement, showing mild heterogeneous enhancement, which may be related to extensive hemorrhage and necrosis.</p><p><b>Lingqing Tang:</b> writing – original draft (lead), resources (equal). <b>Bin Yang:</b> resources (equal), writing – review and editing (lead).</p><p>The authors have nothing to report.</p><p>The patient has provided written informed consent prior to taking part in this study.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"313-314"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Joint Prediction Model for Assessing the Severity of Hypertriglyceridemia-Induced Acute Pancreatitis","authors":"Junyao Long,&nbsp;Junjie Kuang,&nbsp;Zhuoya Ma,&nbsp;Zhuchun Guan,&nbsp;Qinghong Duan","doi":"10.1002/ird3.70024","DOIUrl":"https://doi.org/10.1002/ird3.70024","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Patients with hypertriglyceridemia-induced acute pancreatitis (HTG-AP) have a high incidence of severe disease and a poor prognosis. This study aimed to construct a joint prediction model using multiple clinical and imaging indicators to assess the severity of HTG-AP.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A retrospective analysis was conducted on 165 patients with HTG-AP, categorized into non-mild (<i>n</i> = 84) and mild (<i>n</i> = 81) groups. Clinical parameters were compared, and logistic regression was used to identify independent predictors. A joint prediction model was constructed and validated for stability and performance using receiver operating characteristic analysis, the bootstrap sampling method, the Hosmer–Lemeshow test, and the <i>Z</i>-test.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Significant intergroup differences were observed in lipid metabolism markers (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]), pancreatic injury indicators (amylase [AMY] and lipase [LPS]), imaging characteristics (modified computed tomography severity index [MCTSI] score and liver computed tomography [CT] value), and hospitalization duration (<i>p</i> &lt; 0.05). The MCTSI score, liver CT value, TC level, and LDL-C level were identified as independent risk factors for non-mild HTG-AP. The joint model demonstrated superior performance (area under the curve [AUC] = 0.841) compared with individual predictors (<i>p</i> &lt; 0.05), with good calibration according to the Hosmer–Lemeshow test (<i>p</i> = 0.914) and stable performance validated by bootstrap sampling (ΔAUC = 0.001, <i>p</i> = 0.1531).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The joint prediction model outperformed individual indicators such as the TC level, LDL-C level, MCTSI score, and liver CT value in assessing non-mild HTG-AP, offering enhanced clinical utility.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"302-310"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Clinical Knowledge and AI Interpretability in Thoracic Radiology","authors":"Mengze Xu","doi":"10.1002/ird3.70015","DOIUrl":"https://doi.org/10.1002/ird3.70015","url":null,"abstract":"&lt;p&gt;Yuan's study [&lt;span&gt;1&lt;/span&gt;] entitled “&lt;i&gt;Anatomic Boundary-Aware Explanation for Convolutional Neural Networks in Diagnostic Radiology&lt;/i&gt;” underscores a fundamental gap in existing XAI approaches: the neglect of clinical domain knowledge. Thoracic diseases primarily manifest within specific anatomical regions, such as the lung parenchyma. Yet, conventional XAI methods such as Grad-CAM or Integrated Gradients often highlight extraneous areas (e.g., medical devices, chest wall artifacts), leading to misinterpretations. By leveraging anatomic boundaries derived from a pretrained lung segmentation model, the authors enforce spatial constraints on CNN explanations, aligning them with clinically relevant regions. This innovation is particularly impactful for resource-limited settings, where annotations for fine-grained lesion localization are scarce.&lt;/p&gt;&lt;p&gt;The study's quantitative results are compelling: Across 72 scenarios involving 3 CNN architectures, 4 diseases, and 2 classification settings, the boundary-aware method outperformed baseline explanations in 71 cases. For example, in pneumothorax detection, the dice similarity coefficient (DSC) improved by up to 5.09% when integrating anatomic constraints. These findings validate the hypothesis that incorporating radiological expertise into XAI frameworks enhances explanation fidelity.&lt;/p&gt;&lt;p&gt;The paper's strengths lie in its plug-and-play design and transfer learning strategy. By decoupling lung segmentation from the CNN classifier, the authors avoid retraining on annotated target datasets, reducing computational and labeling costs. The use of publicly available segmentation datasets (e.g., Japanese Society of Radiological Technology) ensures reproducibility and scalability. However, this approach assumes minimal domain shift between external and target datasets. Future studies should evaluate robustness across diverse imaging protocols or patient populations, where anatomical variations (e.g., emphysematous lungs, postsurgical changes) might affect segmentation accuracy. Another notable aspect is the comprehensive evaluation of multiple XAI methods (saliency map, Grad-CAM, Integrated Gradients) and CNN architectures (VGG-11, ResNet-18, AlexNet) [&lt;span&gt;2&lt;/span&gt;]. The consistent improvements observed across these configurations suggest the boundary-aware framework is generalizable. However, the reliance on lightweight CNNs (e.g., VGG-11) raises questions about applicability to modern, deeper models (e.g., vision transformers), which may require different regularization strategies.&lt;/p&gt;&lt;p&gt;A limitation is the qualitative gap between improved metrics and clinical utility. Although intersection over union and DSC metrics quantify overlap with ground-truth lesions, they do not directly measure radiologists' trust in AI explanations. Future work should incorporate human-in-the-loop studies to assess how boundary-aware explanations influence diagnostic decisions and workflow efficiency.&lt;/p&gt;&lt;p&gt;Yuan's appro","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"311-312"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Gaps in Ultrasound-Guided Care: Physician Attitudes, Training, and Technological Frontiers","authors":"Mengting Zhou,&nbsp;Jiawei Shi","doi":"10.1002/ird3.70016","DOIUrl":"https://doi.org/10.1002/ird3.70016","url":null,"abstract":"&lt;p&gt;Dear Editor,&lt;/p&gt;&lt;p&gt;Barry et al.'s study [&lt;span&gt;1&lt;/span&gt;], “&lt;i&gt;Physician Attitudes About Ultrasound-Guided Procedures&lt;/i&gt;,” investigates clinicians' views on ultrasound-guided interventions and their openness to technological advancements. The group surveyed 116 physicians and found that a majority (57.5%) disagreed that there was a steep learning curve for ultrasound-guided procedures, and 85% felt confident in identifying anatomical structures under ultrasound. Attitudes toward supplemental software were mixed, though 55.4% supported real-time 3D reconstruction. Notably, physicians without formal training were significantly more receptive to additional software (&lt;i&gt;p&lt;/i&gt; = 0.0389). Radiologists perceived greater procedural complexity and were less enthusiastic about technological aids. These findings highlight a paradox: Ultrasound is perceived as accessible, but untrained clinicians may rely on technology to compensate for skill gaps. The study underscores the importance of addressing training disparities and leveraging innovation to enhance procedural safety and efficiency.&lt;/p&gt;&lt;p&gt;Ultrasound-guided procedures are widely used across specialties, including interventional radiology (e.g., tumor ablations, biopsies, drainages) [&lt;span&gt;2&lt;/span&gt;], emergency medicine (e.g., FAST exams, vascular access) [&lt;span&gt;3&lt;/span&gt;], anesthesiology (e.g., nerve blocks) [&lt;span&gt;4&lt;/span&gt;], surgery (e.g., intraoperative navigation) [&lt;span&gt;5&lt;/span&gt;], critical care (e.g., hemodynamic monitoring) [&lt;span&gt;6&lt;/span&gt;], and internal medicine (e.g., thoracentesis) [&lt;span&gt;7&lt;/span&gt;]. However, three critical challenges hinder the universal implementation of ultrasound. The first is insufficient training infrastructure. Surveys indicate that a majority of clinicians (particularly in primary care and pediatrics) see inadequate ultrasound training as a critical barrier. For instance, only 22.5% of family medicine practitioners in Hong Kong, China, routinely employ point-of-care ultrasound, with over 90% of respondents highlighting systemic disparities in training resources [&lt;span&gt;8&lt;/span&gt;]. The second challenge is temporal and resource constraints. Studies on primary care in Hungary and family medicine practices in Hong Kong, China, show that limited equipment availability, insufficient procedural time, and persistent technical support deficits are major impediments to ultrasound use [&lt;span&gt;8, 9&lt;/span&gt;]. Third, there is a discrepancy between confidence and competence. Studies have found that a notable proportion of physicians show substantial uncertainty in ultrasound image interpretation and procedural techniques. This skills gap is particularly pronounced in complex clinical scenarios such as pediatric surgery or localization of deep-seated tumors [&lt;span&gt;10-12&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Addressing these challenges requires attention to three strategic priorities. The first is standardized training frameworks. We need to develop cross-specialty certification programs for ultrasound competencie","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 5","pages":"397-399"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of MRI in Fetal Surgery","authors":"Wei Bian,&nbsp;Weizeng Zheng,&nbsp;Zesi Liu,&nbsp;Qiong Luo,&nbsp;Liqun Sun","doi":"10.1002/ird3.70020","DOIUrl":"https://doi.org/10.1002/ird3.70020","url":null,"abstract":"<p>Advances in fetal surgery techniques have enabled the treatment of certain congenital defects before birth. A critical area of focus is the role of perinatal imaging in optimizing prenatal interventions within the precision medicine framework. Magnetic resonance imaging (MRI) is emerging as an indispensable tool for guiding these intricate procedures with the potential to significantly enhance the standard of care and outcomes for affected fetuses. This review begins with an overview of the classification and indications for fetal surgical interventions. It then explores the detailed applications of prenatal MRI scanning and diagnostic techniques across various categories of fetal surgery. A key focus is how fetal MRI provides critical insights into specific lesion characteristics and tissue involvement, thereby aiding healthcare professionals in selecting the optimal surgical strategies for prenatal and postnatal interventions. Fetal MRI offers detailed visualizations that complement traditional ultrasound findings, enhancing the precision of radiological planning for fetal surgery. Finally, the review highlights how integration of fetal MRI into the decision-making process enables healthcare providers to make well-informed choices, ultimately improving the prognosis and outcomes for both the mother and fetus.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 3","pages":"191-202"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Congenital Intracranial Tumors: Prenatal Diagnosis by Fetal Magnetic Resonance Imaging","authors":"Jing-Ya Ren,&nbsp;Hui Ji,&nbsp;Ming Zhu,&nbsp;Su-Zhen Dong","doi":"10.1002/ird3.70018","DOIUrl":"https://doi.org/10.1002/ird3.70018","url":null,"abstract":"<p>Fetal intracranial tumors are rare, accounting for approximately 0.5%–1.9% of all pediatric tumors, though the true incidence may be underestimated. These tumors often present with distinct histopathological features, imaging characteristics, and clinical behavior compared to their postnatal counterparts. This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors, with a particular focus on the role of fetal magnetic resonance imaging (MRI). We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound. Common tumor types encountered in utero—including teratomas, astrocytomas, medulloblastomas, choroid plexus papillomas, and craniopharyngiomas—are reviewed in terms of imaging features, differential diagnosis, and clinical implications. Furthermore, the review addresses the diagnostic challenges, prognostic considerations, and the potential role of fetal MRI in guiding perinatal management and parental counseling.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 3","pages":"203-208"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Prenasal Thickness, Nasal Bone Length and Their Ratio in Diagnosing Down Syndrome at 16-25 weeks' of gestation in India: A Retrospective, Observational, Case Control Study","authors":"Mhaske Nilesh Madhukar,&nbsp;Rachna Gupta,&nbsp;Akshatha Sharma,&nbsp;Smriti Prasad,&nbsp;Anita Kaul","doi":"10.1002/ird3.70017","DOIUrl":"https://doi.org/10.1002/ird3.70017","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>It is found to have association of facial parameters with trisomy 21 fetuses (T 21). We have compared prenasal thickness (PNT), nasal bone length (NBL), and the PNT:NBL ratio of normal fetuses with fetuses with trisomy 21 (T 21) between 16 and 25 weeks of gestation as a diagnostic tool for T 21.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Facial profile images in the two dimensional (2D) gray scale were assessed to measure fetal NBL and PNT between 16 and 25 weeks of gestation. The PNT:NBL ratio of the fetuses was calculated. Nomograms were constructed from the data of morphologically normal fetuses at live birth. The PNT, NBL, and PNT:NBL ratio of fetuses with confirmed T 21 (<i>n</i> = 31) and morphologically normal fetuses at live birth (controls, <i>n</i> = 3485) were compared.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Nomograms for PNT, NBL, and the PNT:NBL ratio were constructed. In T 21 fetuses, PNT (&gt; 95th percentile), NBL (&lt; 5th percentile), and the PNT:NBL ratio (&gt; 95th percentile) showed a sensitivity of 25%, 29%, and 45% for PNT, NBL, and PNT:NBL, respectively, and specificity of 95%, 96%, and 94%, for PNT, NBL, and PNT:NBL, respectively. All of these markers showed a negative predictive value of 99%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>PNT, NBL, and the PNT:NBL ratio have high diagnostic value for fetuses with Down syndrome and can be incorporated easily in the current second trimester screening protocol for T 21. PNT, NBL, and the PNT:NBL ratio are more specific markers for Down syndrome than those used in previous studies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 3","pages":"239-247"},"PeriodicalIF":0.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prenatal Ultrasound and Magnetic Resonance Imaging Features and Postnatal Outcomes of Congenital Hepatic Hemangioma: A Retrospective Analysis","authors":"Luyao Yang,&nbsp;Jianbo Teng,&nbsp;Xinhong Wei","doi":"10.1002/ird3.70021","DOIUrl":"https://doi.org/10.1002/ird3.70021","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Congenital hepatic hemangioma (CHH) is a rare benign vascular tumor that occurs prenatally. However, only a few cases have been summarized and evaluated for the prenatal and postnatal imaging features of CHH, and no studies have conducted long-term follow-up on it. This study aimed to explore the ultrasound and magnetic resonance features, growth patterns, and clinical outcomes of CHH.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Thirty-six pregnancies with a prenatal fetal diagnosis and postnatal diagnosis of CHH were studied. CHHs were grouped into those with a diameter ≥ 4 cm and those with a diameter &lt; 4 cm according to the largest diameter. Fisher's exact test was used to compare the imaging characteristics between the groups. The volume of CHHs was measured at each follow-up visit to plot the growth pattern of the tumors, and the volume of CHHs was compared before and after birth using a rank sum test analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Thirty-three cases of CHHs were confirmed by postnatal imaging, and three were confirmed by a biopsy. Mixed echoes were more common in the diameter ≥ 4 cm group than in the diameter &lt; 4 cm group (<i>p</i> = 0.026). Complications were more likely to occur in the large-diameter group. Eighteen (54.5%) cases were classified as rapidly involuting congenital hemangioma, nine (27.3%) as partially involuting congenital hemangioma, and two (6.1%) as noninvoluting congenital hemangioma. A new type of CHH was identified in which four (12.1%) cases continued to proliferate after birth and spontaneously subsided in subsequent months. The CHH volume decreased with age and was significantly decreased at 9 months postnatal compared to birth (<i>p</i> = 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study showed the imaging features of CHH were associated with the lesion size. Based on postnatal follow-up, a new type of CHH was identified. If there are no complications at birth in CHH cases, a good prognosis is indicated.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 3","pages":"214-221"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}