iRadiology最新文献

{"title":"Primary Duodenal Squamous Cell Carcinoma on 18F-Flurodeoxyglucose Positron Emission Tomography/Computed Tomography","authors":"Yinting Hu,&nbsp;Lei Jiang","doi":"10.1002/ird3.70032","DOIUrl":"https://doi.org/10.1002/ird3.70032","url":null,"abstract":"<p>A 48-year-old woman complained with repeated vomiting and pallor over 3 months. Laboratory results revealed a decrease in hemoglobin (80 g/L, reference range &gt; 110 g/L), and no other findings (including tumor markers) were abnormal. Next, abdomen computed tomography (CT) revealed a duodenal mass, which may be malignant. To further define the nature and stage of the lesion, the patient underwent ¹⁸F-flurodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) scan. The maximum intensity projection (MIP) image (Figure 1a) demonstrated a high radioactivity (arrow) in the middle abdomen. The axial (Figure 1b), coronal (Figure 1c), and sagittal (Figure 1d) images of the abdomen displayed a solid lesion (arrow) with the size of 61 mm × 34 mm and a SUV_max of 15.6 in the horizontal part of the duodenum. In addition, peripheral lymph nodes with the maximum size of 8 mm × 5 mm showing mild FDG activity (SUV_max: 2.0) were noted (images not shown). The patient received surgical resection of the duodenal lesion (Figure 2a) and peripheral lymph nodes. Pathological examination (Figure 2b, hematoxylin–eosin staining and original magnification: ×100) from the duodenal specimen showed tumor cells arranged in nests with formation of keratin pearls. Immunohistochemistry indicated positive staining for P40 (Figure 2c, original magnification: ×100). These findings were consistent with a diagnosis of duodenal squamous cell carcinoma (SCC). Besides, peripheral lymph node metastases were also confirmed.</p><p>SCC of the duodenum is extremely rare and is more likely to represent metastasis from primary SCC originating in other sites, such as the head and neck, lungs, or cervix. Only occasional cases of primary SCC of the duodenum have been reported in the literature. There are four possible pathogenesis of primary duodenal SCC: (1) malignant transformation of heterotopic squamous epithelium; (2) pluripotential stem cells differentiate to malignant squamous cells; (3) squamous metaplasia malignant change due to chronic mucosal damage; and (4) adenocarcinoma transformed into adenosquamous carcinoma and eventually to SCC. Surgery might be the cornerstone in the management of such kind disease. Given the value of the differentiation of metastatic or primary duodenal SCC for treatment options and prognosis, establishing a correct diagnosis is essential. Traditionally, CT scanning has been the major imaging modality for diagnosing abdominal malignancies, which is not particularly sensitive for detecting duodenal malignancies. Increased FDG uptake in the duodenum is not uncommon, but it is usually physiological or inflammation-related. However, our case suggests that duodenal malignancy should be considered in the differential diagnosis when focal abnormal FDG uptake is present in the duodenum, especially when accompanied by a corresponding mass-like lesion on CT imaging. Furthermore, in this case, whole bo","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"315-317"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910069","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":"Evaluating the Capability of Large Language Model Chatbots for Generating Plain Language Summaries in Radiology","authors":"Pradosh Kumar Sarangi,&nbsp;Pratisruti Hui,&nbsp;Himel Mondal,&nbsp;Debasish Swapnesh Kumar Nayak,&nbsp;M. Sarthak Swarup,&nbsp; Ishan,&nbsp;Swaha Panda","doi":"10.1002/ird3.70030","DOIUrl":"https://doi.org/10.1002/ird3.70030","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Plain language summary (PLS) are essential for making scientific research accessible to a broader audience. With the increasing capabilities of large language models (LLMs), there is the potential to automate the generation of PLS from complex scientific abstracts. This study assessed the performance of six LLM chatbots: ChatGPT, Claude, Copilot, Gemini, Meta AI, and Perplexity, in generating PLS from radiology research abstracts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 100 radiology abstracts were collected from PubMed. Six LLM chatbots were tasked with generating PLS for each abstract. Two expert radiologists independently evaluated the generated summaries for accuracy and readability, with their average scores being used for comparisons. Additionally, the Flesch–Kincaid (FK) grade level and Flesch reading ease score were applied to objectively assess readability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Comparisons of LLM-generated PLS revealed variations in both accuracy and readability across the models. Accuracy was highest for ChatGPT (4.94 ± 0.18) followed by Claude (4.75 ± 0.31). Readability was highest for ChatGPT (4.83 ± 0.27) followed by Perplexity (4.82 ± 0.29). The Flesch reading ease score was highest for Claude (62.53 ± 10.98) and lowest for ChatGPT (40.10 ± 11.24).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>LLM chatbots show promise in the generation of PLS, but performance varies significantly between models in terms of both accuracy and readability. This study highlights the potential of LLMs to aid in science communication but underscores the need for careful model selection and human oversight.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"289-294"},"PeriodicalIF":0.0,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910059","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":"Applications and Implications of ChatGPT and GPT-4 in Radiology","authors":"Bo Gao,&nbsp;Weihua Ou","doi":"10.1002/ird3.70031","DOIUrl":"https://doi.org/10.1002/ird3.70031","url":null,"abstract":"&lt;p&gt;Rapid advancements in artificial intelligence (AI) technology have resulted in the emergence of state-of-the-art large language models (LLMs) such as ChatGPT and GPT-4. Originally designed for natural language processing, these models are now being applied to increasingly broader domains, particularly in medical image processing [&lt;span&gt;1&lt;/span&gt;]. Concurrently, the rise of such models has introduced innovative tools into medical image processing and diagnosis, profoundly shaping the future trajectory of this field. These tools not only enhance diagnostic accuracy and efficiency, but also alleviate substantial repetitive workloads for clinicians [&lt;span&gt;2&lt;/span&gt;]. To address the critical needs for transparency, reproducibility, and clinical reliability in biomedical AI research, Gallifant et al. [&lt;span&gt;3&lt;/span&gt;] proposed Transparent Reporting of a prediction model for Individual Prognosis or Diagnosis-LLM, an extension to the Transparent Reporting of a prediction model for Individual Prognosis or Diagnosis + artificial intelligence statement. In a domain-specific innovation, Liu et al. [&lt;span&gt;4&lt;/span&gt;] developed Radiology-GPT through training and fine-tuning on a massive radiology knowledge corpus. In comparison with general-purpose LLMs, this specialized model demonstrated superior performance, validating the feasibility of creating localized generative models for specific medical specialties. Complementing this work, Yuan et al. [&lt;span&gt;5&lt;/span&gt;] systematically evaluated the capabilities of the advanced multimodal model ChatGPT-4V for diagnosing brain tumors on 7T magnetic resonance imaging (MRI) data. Their study established a benchmark framework for ultra-high field imaging AI applications, propelling the progress of precision medicine and intelligent diagnostics.&lt;/p&gt;&lt;p&gt;This special issue on ChatGPT and GPT-4 includes four recent studies that cover applications of different LLMs, such as Meta LLaMA 3.1, ChatGPT, Claude, Gemini, and LLaVA, in various medical scenarios. Yuan et al. [&lt;span&gt;6&lt;/span&gt;] deeply explored the application of the Transformer architecture in natural language processing of chest X-ray reports, finding that this architecture holds significant potential in medical text processing. However, computational efficiency and ethical compliance require optimization, and future integration with multimodal data is needed to enhance diagnostic accuracy. Lotfian et al. [&lt;span&gt;7&lt;/span&gt;] evaluated the performance of the open-source model LLaMA 3.1 in thoracic imaging diagnostics using 126 multiple-choice questions. The model achieved an overall accuracy of 61.1%, with excellent performance in intensive care (90%) and terminology recognition (83.3%) but weaker results in basic imaging (40%) and lung cancer diagnosis (33.3%). This assessment demonstrates the potential of open-source models like LLaMA 3.1 while highlighting the need for domain-specific fine-tuning to improve stability as well as the need to balance open-source flexibility wit","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 4","pages":"259-260"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910324","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":"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}