iRadiology最新文献

{"title":"Doege–Potter Syndrome","authors":"Wenhui Ma,&nbsp;Cong Huang","doi":"10.1002/ird3.70073","DOIUrl":"https://doi.org/10.1002/ird3.70073","url":null,"abstract":"<p>A 70-year-old man presented with a 4-month history of a painless, firm lower abdominal pelvic mass. Laboratory evaluation demonstrated persistent severe hypoglycemia with suppressed insulin and C-peptide concentrations, suggesting nonislet-cell tumor hypoglycemia. Contrast-enhanced computed tomography revealed a large pelvic mass exhibiting marked heterogeneous enhancement and extensive liquefactive necrosis (arrow, Figure 1a). Cinematic rendering further delineated prominent feeding vessels arising from the internal iliac artery (Figure 1b). Complete surgical excision was performed, and histopathological examination confirmed a malignant solitary fibrous tumor (Figure 1c). Blood glucose levels normalized rapidly following resection, increasing intraoperatively to 11.46 mmol/L and declining to 6.23 mmol/L by postoperative day 1, with sustained normoglycemia thereafter. Collectively, these findings established the diagnosis of Doege–Potter syndrome. The hypoglycemia was attributed to tumor-derived insulin-like growth factor II, which activates insulin receptors and suppresses hepatic gluconeogenesis. This case underscores computed tomography's diagnostic value in localizing occult extrathoracic insulin-like growth factor II-secreting tumors.</p><p><b>Wenhui Ma:</b> writing – original draft, writing – review and editing. <b>Cong Huang:</b> writing – review and editing.</p><p>The authors have nothing to report.</p><p>The authors have nothing to report.</p><p>The patient provided written informed consent at the time of study enrollment.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"380-381"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849154","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":"Imaging the Tumor Microenvironment Beyond Anatomy: Functional, Immune, Stromal, and Metabolic Signatures for Precision Oncology","authors":"Karthikeyan Elumalai,&nbsp;Gayathri Krishnakumar","doi":"10.1002/ird3.70071","DOIUrl":"https://doi.org/10.1002/ird3.70071","url":null,"abstract":"<p>The tumor microenvironment (TME) plays an important role in predicting tumor initiation, progression, immune evasion, therapeutic resistance, relapse, and prognosis. However, cycles of oncologic imaging are mostly anatomy-based, using tumor size and morphology as surrogate variables of the disease burden. The key limitation of this approach is that critical TME processes frequently change before structural changes and may have a decisive impact on the response to modern therapies, such as immune checkpoint inhibitors, antiangiogenic agents, and metabolism-targeted interventions. Noninvasive whole-body evaluation of vascular dysfunction and hypoxia, immune infiltration and activation, stromal remodeling, and metabolic reprogramming is now comprehensible to a host of functional and molecular imaging techniques, and these changes often occur before measurable changes in tumor size. In this review, we integrate emerging ideas and clinically relevant strategies for defining the TME based on imaging of biological signatures as opposed to individual modalities or the use of single-parameter readouts. We discuss fibroblast activation protein inhibitor-positron emission tomography (PET) imaging of the stromal phenotype, immune-PET for immune mapping of the entire body, whole-body systems-level kinetics analysis by long-axial field-of-view/total-body PET, and real-time metabolic flux evaluation by hyperpolarized ¹³C-magnetic resonance imaging. We also discuss how high-dimensional multiparametric imaging can be transformed into reproducible and predictive biomarkers using radiomics and artificial intelligence. Taken together, the data indicate that TME imaging is an enabling technology for the early detection, risk stratification, response phenotyping, and adaptive treatment selection, reframing oncologic imaging as a biology-centric decision-support technology and not a descriptive adjunct.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"287-304"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848668","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":"Alignment of Self-Supervised Learning Representations With Radiomic Features in Multiphase Renal Computed Tomography","authors":"S. J. Pawan,&nbsp;R. Prajwal,&nbsp;Mehrnegar Aminy,&nbsp;Tejal Gala,&nbsp;Matthew Muellner,&nbsp;Xiaomeng Lei,&nbsp;Steven Y. Cen,&nbsp;Inderbir Gill,&nbsp;Mihir Desai,&nbsp;Vinay Duddalwar,&nbsp;Assad A. Oberai","doi":"10.1002/ird3.70090","DOIUrl":"https://doi.org/10.1002/ird3.70090","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Self-supervised learning (SSL) has emerged as a promising approach in medical image analysis, offering the ability to learn robust feature representations from unlabeled data. However, the extent to which these embeddings align with clinically interpretable features remains largely unaddressed. This study aims to assess the degree to which SSL embeddings encode radiomics-aligned, interpretable features within multiphase renal computed tomography (CT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed four-phase contrast-enhanced CT scans of renal tumors, including the noncontrast, corticomedullary, nephrographic, and excretory phases. Radiomic features included first-order statistics, texture-based features, and shape descriptors. SSL embeddings were obtained from three models: Simple framework for contrastive learning of visual representations (SimCLR), distillation with no labels (DINO)—a self-distillation with no labels method, and bootstrap your own latent (BYOL). Feature-level alignment was quantified using pairwise Spearman correlation analyses between SSL embeddings and radiomic features.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>DINO demonstrated the highest alignment with radiomic features, particularly texture-based features, such as gray-level size zone matrix (GLSZM) (up to 22%) and neighboring gray tone difference matrix (NGTDM) (up to 35%), followed by SimCLR. BYOL showed minimal alignment across all feature families. Among imaging phases, the nephrographic phase exhibited the strongest correlations overall.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings indicate that SSL models, particularly DINO and to a lesser extent SimCLR, are capable of encoding patterns that align with established radiomic descriptors, especially texture-based features. This partial alignment underscores the potential of incorporating radiomics-informed metrics into the evaluation framework for SSL models, enabling more interpretable and clinically relevant model selection in medical imaging applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"369-376"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848708","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":"Diagnostic Performance of Magnetic Resonance Cholangiopancreatography, Endoscopic Ultrasound, Endoscopic Retrograde Cholangiopancreatography, and Computed Tomography in Biliary Tract Pathologies: A Systematic Review and Comparative Analysis","authors":"Lalramdinmawia Ralte,&nbsp;Ashita Jain,&nbsp;Arshad Alam Khan,&nbsp;Shubham Gupta","doi":"10.1002/ird3.70093","DOIUrl":"https://doi.org/10.1002/ird3.70093","url":null,"abstract":"<p>Biliary tract disease is challenging to diagnose, as the anatomy is complex and complications arise. Various imaging modalities are available with differential benefits and limitations in detecting choledocholithiasis, strictures, and malignancy. This systematic review aimed to comprehensively assess and compare the diagnostic performance, clinical significance, and safety of magnetic resonance cholangiopancreatography (MRCP), endoscopic retrograde cholangiopancreatography (ERCP), computed tomography (CT), and endoscopic ultrasonography (EUS) in evaluating biliary tract disease. Thirty studies that compared at least two radiological modalities were selected and reviewed. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2, and evidence quality was graded using the Grading of Recommendations Assessment, Development and Evaluation framework. MRCP demonstrated median sensitivity and specificity of 88.5% (interquartile range [IQR] 82.1%–94.8%; range 37.5%–97.4%) and 95.7% (IQR 93.9%–96.0%; range 83.8%–100.0%), respectively, for choledocholithiasis detection. EUS exhibited superior performance with a median sensitivity of 94.8% (IQR 90.2%–97.4%; range 80.0%–100.0%) and specificity of 98.5% (IQR 97.3%–100.0%; range 95.0%–100.0%). For biliary strictures, MRCP, EUS, ERCP, and CT demonstrated median sensitivity/specificity of 83.3%/76.0%, 67.0%/79.0%, 85.0%/75.0%, and 72.0%/72.5%, respectively. Corresponding values for malignancy differentiation were 81.0%/88.9%, 71.0%/78.5%, 81.4%/80.7%, and 72.0%/72.5%, respectively. Our analysis has revealed specific diagnostic niches for each imaging modality. MRCP is generally preferred for initial imaging evaluation, whereas EUS provides higher sensitivity for stone detection and enables tissue acquisition if required. ERCP is preferred when therapeutic intervention is required and to diagnose complex strictures. CT is most appropriate for selected obstructive and malignant biliary conditions. The evidence favors a stratified, condition-specific algorithm rather than a universal one.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"318-328"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848788","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":"Assessing the Accuracy of Cine Post-Contrast Cardiac Magnetic Resonance Imaging: Application in Acute Viral Myocarditis","authors":"Amani Ben Khalifa,&nbsp;Mezri Maatouk,&nbsp;Azza Ben Ali,&nbsp;Mabrouk Abdelali,&nbsp;Mejdi Ben Messaoud,&nbsp;Ahmed Zrig,&nbsp;Mohamed Hedi Bedoui","doi":"10.1002/ird3.70088","DOIUrl":"https://doi.org/10.1002/ird3.70088","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In myocarditis, cardiac magnetic resonance imaging (MRI) provides diagnostic and prognostic insights by assessing the presence of myocarditis lesions on late gadolinium enhancement (LGE) sequences and evaluating left ventricular ejection fraction (LVEF). The balanced steady-state free precession (b-SSFP) sequence is commonly used to assess LVEF. Recently, b-SSFP after gadolinium-based contrast injection (b-SSFP-Gd) has been integrated into cardiac MRI protocols. We hypothesized that b-SSFP-Gd enhances the MRI protocol for acute viral myocarditis diagnosis by improving lesion detection and reducing examination time. This study simultaneously evaluated the performance of b-SSFP-Gd for the detection of acute viral myocarditis lesions compared with the LGE sequence and for LVEF measurement compared with the b-SSFP sequence.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This retrospective single-center study included 38 patients with strong evidence of acute viral myocarditis. The 17-Segment American Heart Association classification was used to compare the number of affected segments on LGE and b-SSFP-Gd. Diagnostic performance was assessed using sensitivity, specificity, and area under the curve analysis. LVEF was measured in 30 patients using b-SSFP and b-SSFP-Gd. The results were statistically analyzed using Pearson's correlation, paired t-tests, and intraclass correlation coefficients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sensitivity and specificity of b-SSFP-Gd were 100.0% (95% confidence interval: 90.8%–100.0%) and 100.0% (95% confidence interval: 91.2%–100.0%), respectively, with an area under the curve of 1.000 for lesion detection. The number of segments measured on LGE sections correlated with the number of segments measured on b-SSFP-Gd in diastole (<i>r</i>[36] = 0.83, <i>p</i> &lt; 0.001) and systole (<i>r</i>[36] = 0.78, <i>p</i> &lt; 0.001). There was no statistically significant difference in LVEF between b-SSFP and b-SSFP-Gd (<i>p</i> = 0.34).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>b-SSFP-Gd more accurately detected myocarditis lesions than the LGE sequence and was as effective as the b-SSFP sequence for LVEF calculation. b-SSFP-Gd could be integrated into cardiac MRI protocols to aid in the diagnosis of acute myocarditis, along with the LGE sequence, while reducing the overall examination time by replacing b-SSFP for LVEF measurement.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"338-350"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848748","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":"Vanishing White Matter in an Infant With Acute Onset","authors":"Xiaofang Chen,&nbsp;Wei Chen","doi":"10.1002/ird3.70091","DOIUrl":"https://doi.org/10.1002/ird3.70091","url":null,"abstract":"<p></p><p>A 22-month-old boy presented with a 5-day history of fever.</p><p>Axial T2-weighted MRI showed diffuse, symmetric cerebral white-matter hyperintensity with relative sparing of cortical gray matter (Panel a). An axial fluid-attenuated inversion recovery image showed marked attenuation of the white-matter signal to near cerebrospinal fluid intensity, a finding consistent with vanishing white matter (Panel b). Diffusion-weighted imaging showed no restricted diffusion (Panel c), and the apparent diffusion coefficient map showed increased white-matter diffusivity (Panel d).</p><p>Genetic testing identified variants in the <i>eukaryotic translation initiation factor 2B subunit beta (EIF2B2)</i> gene, confirming the diagnosis of vanishing white matter (VWM).</p><p>The combination of diffuse, symmetric white matter signal abnormality, cerebrospinal-fluid-like rarefaction on fluid-attenuated inversion recovery imaging, and increased diffusivity is characteristic of VWM. Recognition of this pattern can prompt targeted genetic testing and help distinguish the disorder from other inherited leukodystrophies.</p><p>This infant presents acute clinical symptom representing a scarcely documented early infantile subtype of EIF2B-related leukoencephalopathy, triggered merely by simple fever stress rather than trauma or vaccination. Most febrile pediatric leukoencephalopathies primarily involve gray matter, whereas this patient shows exclusive white matter vacuolation with spared cortex and basal ganglia. The paired multi-sequence MRI set offers trainees a concise teaching template to rapidly differentiate fever-induced acute VWM from infectious, toxic, and mitochondrial leukoencephalopathies, avoiding misdiagnosis as viral encephalitis.</p><p><b>Xiaofang Chen:</b> conceptualization, methodology, investigation, formal analysis, writing – original draft, data curation, software, visualization. <b>Wei Chen:</b> project administration, supervision, resources, writing – review and editing.</p><p>The authors have nothing to report.</p><p>This case report was conducted in accordance with the Declaration of Helsinki. Institutional ethics committee approval was not required for this single anonymized case report.</p><p>Written informed consent for publication of the clinical information and images was obtained from the patient's legal guardian.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"382-383"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848823","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":"Assessing ChatGPT-4o's Potential as a Support Tool in Neuroradiology Cases: A Comparison of Performance by Using Radiologic Images","authors":"Levent Akman Solim,&nbsp;Duygu Atasoy,&nbsp;Sabahattin Yuzkan,&nbsp;Vitali Koch,&nbsp;Ahmed Ait Bachir,&nbsp;Ibrahim Yel,&nbsp;Thomas J. Vogl","doi":"10.1002/ird3.70085","DOIUrl":"https://doi.org/10.1002/ird3.70085","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In clinical settings, particularly in radiology, artificial intelligence and large language models have already demonstrated promising results. They have the potential to become an integral component of radiologists' workflows in the future, and neuroradiology is likely to benefit from their increasing integration into clinical practice. This study aims to evaluate Chat Generative Pre-trained Transformer 4 Omni (ChatGPT-4o) as a supportive tool in neuroradiology by assessing its ability to generate accurate radiology reports from imaging and patient history and its impact on radiologists' diagnostic accuracy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Retrospective analysis was conducted using only radiological images and brief patient admission history from 30 neuroradiology cases published publicly between 2015 and 2024. ChatGPT-4o generated radiology reports with final and differential diagnoses. One radiologist and one radiology resident independently reviewed cases without and with the reports created by ChatGPT-4o at a 4-week interval. Diagnostic accuracy was compared to the published gold-standard histopathological diagnoses. Also, ChatGPT-4o was asked to detect the orientation, sequence, and contrast usage. To compare the accuracies, exact McNemar's test and Wilcoxon signed-rank test with Bonferroni correction was used for statistical analysis.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Evaluation of radiology reports demonstrated an accuracy of final diagnosis from ChatGPT-4o 33.33% (10/30), experienced radiologist 56.67% (17/30), and radiology resident 23.33% (7/30) (&lt;i&gt;p&lt;/i&gt; &lt; 0.039 for experienced radiologist vs. ChatGPT-4o and &lt;i&gt;p&lt;/i&gt; &lt; 0.375 for resident vs. ChatGPT-4o). Accuracy rates for differential diagnosis were: ChatGPT-4o 60.50% ± 21.30%, experienced radiologist 64.83% ± 17.24%, and radiology resident 42.50% ± 16.90% (&lt;i&gt;p&lt;/i&gt; = 0.242 for experienced radiologist vs. ChatGPT-4o and &lt;i&gt;p&lt;/i&gt; = 0.0004 for resident vs. ChatGPT-4o). Diagnostic performance with the use of ChatGPT-4o as a support tool demonstrated an accuracy of final diagnosis: experienced radiologist 56.67% (17/30) and radiology resident 30.00% (9/30) (&lt;i&gt;p&lt;/i&gt; = 1.000 and &lt;i&gt;p&lt;/i&gt; = 0.500, respectively) and of differential diagnosis: experienced radiologist 66.50% ± 19.30% and radiology resident 49.50% ± 15.56% (&lt;i&gt;p&lt;/i&gt; = 0.500 and &lt;i&gt;p&lt;/i&gt; = 0.013, respectively).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;ChatGPT-4o demonstrated limited autonomous and supportive diagnostic accuracy in complex neuroradiol","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"351-359"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848661","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":"Technical Assessment of Preclinical Noninvasive Imaging Methods for Endometriosis Follow-Up in Murine Models","authors":"Peyman Palay,&nbsp;Tayyebeh Zamani-Badi,&nbsp;Zeinab Ghezelayagh,&nbsp;Kiandokht Kiani","doi":"10.1002/ird3.70086","DOIUrl":"https://doi.org/10.1002/ird3.70086","url":null,"abstract":"<p>Endometriosis is a common benign condition affecting 6%–10% of women, the underlying cause of which remains largely unknown. Laboratory animal models of endometriosis offer a translational approach to advance research into this condition. To this end, the provision of a noninvasive method to monitor real-time disease progression and elucidate the pathophysiological mechanisms of endometriosis is essential. This review evaluates advanced preclinical imaging techniques for studying endometriotic lesions in murine models and comparing and highlighting their technical specifications, advantages, and limitations. Imaging methods are categorized into optical (bioluminescence, fluorescence, and nanoparticle-assisted near-infrared) and nonoptical (magnetic resonance imaging, ultrasound, and positron emission tomography) modalities. Bioluminescence imaging enables noninvasive monitoring of lesion growth and early angiogenesis in luciferase transgenic mice but is limited by low signal intensity in superficial lesions. Fluorescence imaging does not require substrate injection but cannot distinguish between viable and dead cells. Near-infrared, enhanced by nanoparticle permeability, allows effective visualization of endometriotic lesions, although accumulation in nontarget tissues may compromise accuracy in superficial regions. Magnetic resonance imaging facilitates longitudinal studies of ectopic tissue growth, offering rapid data analysis with minimal stress to animals. High-resolution ultrasound provides a noninvasive assessment of lesion dynamics but has low sensitivity for detecting small lesions. Positron emission tomography imaging shows promise for identifying small superficial lesions in humans through the use of radiopharmaceuticals. The effectiveness of these imaging techniques depends on lesion depth, with all methods performing better for deeper lesions than for superficial ones. Ongoing research aims to integrate multiple imaging modalities to improve the efficacy of preclinical studies.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"305-317"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848673","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":"Artificial Intelligence-Driven Analysis for Locating Fistulas in Sacral Terminal Filar Cysts: Technical Note","authors":"Xuesheng Zheng","doi":"10.1002/ird3.70092","DOIUrl":"https://doi.org/10.1002/ird3.70092","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Sacral terminal filar cysts often cause pain, numbness, urinary/bowel dysfunction, and sexual dysfunction due to cerebrospinal fluid (CSF) leakage at the filum terminale–dural sac junction. Accurate localization of the fistula is critical for surgical success because misidentification may lead to persistent symptoms and a requirement for reoperation. Although magnetic resonance imaging (MRI) and computed tomography (CT) are standard for diagnosis, manual localization of the fistula relies heavily on expert experience, limiting reproducibility. This study introduces an artificial intelligence (AI)-driven approach to overcome these limitations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study developed a two-stage AI algorithm using the Qwen large model for MRI–CT pattern matching. First, MRI slices were classified into three anatomical patterns (subarachnoid space, transitional, or cyst level) based on vertebral canal morphology, with the highest-scoring pattern taken to indicate the fistula level. Second, corresponding CT slices were matched using shape features of the vertebral canal. The algorithm was validated on three typical cases from our department, with the AI-generated localizations being compared with expert manual annotations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The AI-identified MRI slices (slices 12, 6, and 7 for cases 1–3, respectively) matched expert-annotated levels. The corresponding AI-identified CT slices for cases 1 and 2 (slices 146 and 183) aligned perfectly with the manual records, whereas that for case 3 (239) differed by only two slices (1.4 mm) from the manual one (237). Three-dimensional rendering of the CT confirmed the fistula's transitional location between the dural sac and cyst, validating anatomical accuracy. The algorithm demonstrated high robustness in cross-modal matching.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study presents an AI-based approach for locating the fistula in sacral terminal filar cysts, which shows potential for reducing expert reliance and enhancing accessibility. However, the small sample size limits the generalizability of our preliminary results, which are primarily based on descriptive comparisons and lack rigorous statistical analysis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"360-368"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848628","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":"Organoid Imaging: Beyond Depth, Toward Function and Standards","authors":"Jing Wang,&nbsp;Peili Cen,&nbsp;Ziyun Fang,&nbsp;Hong Zhang","doi":"10.1002/ird3.70096","DOIUrl":"https://doi.org/10.1002/ird3.70096","url":null,"abstract":"<p>Despite recent achievements in organoid models that recapitulate key aspects of human development and disease, a deceptively simple question persists: What can be seen when looking inside a living organoid? In most cases, the answer is either a well-resolved but static structure or a dynamic yet superficial and easily perturbed snapshot. As advanced imaging technologies race to capture larger, deeper, and longer organoid time-lapse recordings, the field appears to have reached a critical juncture. The scientific problem is no longer purely technical, given that faster microscopes and brighter probes are now within reach. The real issue is conceptual. Seeing deeper has been prioritized over understanding function, whereas standards for making images from different laboratories truly comparable remain absent.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 4","pages":"377-379"},"PeriodicalIF":2.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848717","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}