Frontiers in radiology最新文献

{"title":"Case Report: Epidermal growth factor receptor germline variant associated with epilepsy and rare, distinctive cerebral MRI abnormalities.","authors":"Elise A Ferreira, Machteld M Oud, Erik-Jan Kamsteeg, Clara D M van Karnebeek, Mirjam Langeveld, Edward M Leter, Maartje J Vogel, Nicole I Wolf, Stefan D Roosendaal, Jeroen J van Dillen, Olaf E M G Schijns, Simon Tousseyn, Christianne Hoeberigs, Rob P W Rouhl, Saskia N van der Crabben","doi":"10.3389/fradi.2026.1782924","DOIUrl":"10.3389/fradi.2026.1782924","url":null,"abstract":"<p><strong>Background: </strong>Somatic pathogenic variants in <i>EGFR</i> have recently been implicated in lesional focal epilepsy, typically in association with low-grade epilepsy-associated tumors. Germline <i>EGFR</i> variants, however, have not previously been linked to epilepsy-related neuroimaging phenotypes.</p><p><strong>Case presentation: </strong>We report a large multigenerational family in which multiple individuals presented with epilepsy, progressive cognitive impairment, and striking, bilateral mesiotemporal and thalamic MRI abnormalities. Through deep phenotyping and reanalysis of exome sequencing data a rare, heterozygous, germline <i>EGFR</i> variant [NM_005228.5:c.866C > A p.(Ala289Asp)] was identified and shown to segregate with the neurological phenotype.</p><p><strong>Conclusion: </strong>This case report expands the phenotypic spectrum associated with <i>EGFR</i> by demonstrating that a germline variant can underlie epilepsy and characteristic non-neoplastic MRI abnormalities. Our findings underscore the importance of multidisciplinary re-evaluation of variants of uncertain significance (VUS) and segregation analysis in large families.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1782924"},"PeriodicalIF":4.0,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13223146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148152379","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":"Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.","authors":"Erika Spada, Katharina Erb-Eigner, Aurélie Pahud de Mortanges, Mauricio Reyes, Alexander Pöllinger","doi":"10.3389/fradi.2026.1810708","DOIUrl":"10.3389/fradi.2026.1810708","url":null,"abstract":"<p><p>Artificial intelligence (AI) has become increasingly integrated into radiology across multiple domains, including image acquisition, interpretation, workflow optimization, and clinical decision support. Among these applications, advanced image retrieval systems have the potential to assist clinicians in identifying visually and clinically similar cases, as well as relevant prior studies. This article provides a comprehensive overview of content-based image retrieval (CBIR) and introduces the emerging paradigm of trajectory retrieval, which focuses on the longitudinal evolution of diseases over time. We discuss the clinical applications of these systems, including diagnostic decision support, workflow optimization, educational uses, and research cohort building, highlighting their potential to enhance diagnostic reasoning and reduce uncertainty in complex or rare cases. Despite substantial research progress, adoption in real-world clinical settings remains limited due to challenges such as data heterogeneity, privacy constraints, the need for multimodal integration, and difficulties in capturing temporal dynamics. Trajectory-based approaches, combined with multimodal data integration and human-in-the-loop feedback mechanisms, offer a promising path toward overcoming these barriers by aligning retrieval systems more closely with the longitudinal and holistic nature of clinical decision-making. By addressing these challenges, AI-powered radiology image retrieval has the potential to transform workflows, support more precise and confident diagnoses, and ultimately improve patient care outcomes.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1810708"},"PeriodicalIF":4.0,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139950","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":"Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.","authors":"Xie Jiazhi, Dai Dajian, Tang Shilong, Fan Xiao, Zhu Luyao, He Ling","doi":"10.3389/fradi.2026.1803622","DOIUrl":"10.3389/fradi.2026.1803622","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the feasibility and value of the deep learning full model iterative algorithm (AIIR) in reducing radiation dose during contrast-enhanced whole abdominal CT scans in children.</p><p><strong>Methods: </strong>Data from 100 pediatric patients undergoing contrast-enhanced whole abdominal CT scans due to clinical indications were retrospectively collected. The patients were divided into a conventional group (<i>n</i> = 50,100 kVp, automatic current modulation 100 mAs) using hybrid iterative reconstruction (HIR) and an experimental group (<i>n</i> = 50,80 kVp, automatic current modulation 180 mAs) employing HIR and AIIR reconstruction. Subjective image quality scores (five-point scale evaluation), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of major organs, contrast agent dosage, and radiation dose (CTDIvol, DLP, ED) were compared among the three groups during the arterial phase.</p><p><strong>Results: </strong>The experimental group exhibited significantly lower radiation dose compared to the conventional group (ED reduction by 23.61%, <i>P</i> < 0.001). In terms of image quality, subjective scores indicated superior performance of the AIIR group over both conventional HIR and experimental HIR groups (<i>P</i> < 0.05), with excellent inter-physician agreement (Kappa>0.70). The AIIR group demonstrated significantly higher SNR and CNR values for the liver, spleen, kidneys, gallbladder, pancreas, and abdominal aorta compared to the HIR groups (all <i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>The AIIR algorithm achieves superior image quality at low dose levels (80 kVp) compared to conventional HIR and low-dose HIR protocols, demonstrating significant clinical value and potential for widespread application in pediatric abdominal imaging.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1803622"},"PeriodicalIF":4.0,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139966","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":"Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.","authors":"Taseer Bashir, Hanadi Abdullah Alwafi","doi":"10.3389/fradi.2026.1754772","DOIUrl":"10.3389/fradi.2026.1754772","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate dental age estimation is a vital component of forensic identification, orthodontic treatment planning, and medico-legal assessments. Conventional approaches, such as Demirjian's and Cameriere's methods, are limited by observer variability and population dependency. Recent advances in artificial intelligence (AI) and deep learning (DL) offer potential for objective, reproducible, and scalable assessment through panoramic radiology. This study aimed to develop and validate a convolutional neural network (CNN)-based model for dental age estimation among adolescents using panoramic radiographs and to compare its accuracy with traditional manual methods.</p><p><strong>Methods: </strong>A total of 296 panoramic dental radiographs from adolescents aged 9-19 years were analyzed, with balanced representation across age groups and comparable distribution between male and female participants. Images were divided into training (70%, <i>n</i> = 207), validation (15%, <i>n</i> = 44), and testing (15%, <i>n</i> = 45) subsets. All images were preprocessed, normalized, and augmented prior to model development. A CNN-based regression model was trained using mean squared error as the loss function and evaluated using mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (r). Grad-CAM visualization was employed to enhance interpretability of model predictions.</p><p><strong>Results: </strong>The proposed CNN model achieved an MAE of 1.12 years, RMSE of 1.37 years, and a strong correlation (r = 0.96) between predicted and chronological age, outperforming traditional Demirjian and Cameriere methods. Activation maps confirmed that predictions were based on biologically relevant anatomical regions, including developing third molars and mandibular growth zones.</p><p><strong>Conclusion: </strong>AI-driven panoramic radiographic analysis provides a robust, interpretable, and ethically compliant framework for adolescent dental age estimation. These findings highlight its translational potential in forensic odontology, pediatric dentistry, and legal age verification, supporting a shift toward standardized, data-driven dental diagnostics.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1754772"},"PeriodicalIF":4.0,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13213847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058754","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":"BT-CAP: a subcomponent-aware and anatomically constrained data augmentation framework for multi-modal brain tumor MRI segmentation.","authors":"Amin Tavallaii, Shamim Shah Ghasi","doi":"10.3389/fradi.2026.1785108","DOIUrl":"10.3389/fradi.2026.1785108","url":null,"abstract":"<p><strong>Background: </strong>Data scarcity and class imbalance remain critical challenges in medical image analysis, particularly for brain tumor MRI segmentation, where subcomponents such as enhancing tumor, non-enhancing tumor, cystic component, and peritumoral edema are underrepresented. Existing augmentation strategies, from classical geometric transforms to GAN-based and diffusion model-based synthesis, either lack subcomponent-level control or require extensive generative model training, limiting their practicality in low-data settings.</p><p><strong>Materials and methods: </strong>We propose the Brain Tumor Compositional Augmentation Pipeline (BT-CAP), a subcomponent-aware and anatomically constrained augmentation framework for multi-modal MRI. BT-CAP decomposes tumor subcomponents and recomposes them through a sequence of targeted operations, including isotropic scaling, B-spline deformation, Powell-optimized spatial rearrangement, inpainting, interface smoothing, and constrained edema deformation, applied consistently across all MRI modalities and segmentation masks, producing label-ready augmented volumes without additional annotation. We evaluated augmentation diversity (SSIM, label distribution, intensity variation, centroid displacement) and anatomical plausibility on 50 BraTS-PEDs 2025 cases, yielding 250 augmented volumes, and assessed segmentation performance on the full 256-case dataset using 3-fold cross-validation.</p><p><strong>Results: </strong>BT-CAP achieved a mean SSIM of 0.956 ± 0.014 with wide subcomponent volume change ranges and realistic intensity heterogeneity, confirming meaningful structural diversity. By architectural design, all augmented segmentation mask voxels were confined within brain boundaries, and zero overlap between deformed edema and the tumor core was observed across all 250 cases. Segmentation experiments showed mean Dice score improvements of 6%-7% for tumor subcomponents and 2%-3% for tumor core and whole tumor compared to training without compositional augmentation, with a computational cost of approximately 2 min per case on CPU.</p><p><strong>Conclusion: </strong>BT-CAP establishes a new class of compositional augmentation methods that deliver anatomically structured, label-ready, and scalable data generation without generative model training. The framework is applicable to any multi-class segmentation task where data scarcity and structural fidelity are critical, and is openly available at https://github.com/dramintavallaii/BT-CAP.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1785108"},"PeriodicalIF":4.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044981","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":"Enhancing low-dose CT denoising via multi-view knowledge transfer without paired data.","authors":"Yueyang You, Li Xu, Gen Wei, Fuzhou Hua, Yingbo Hu","doi":"10.3389/fradi.2026.1691258","DOIUrl":"10.3389/fradi.2026.1691258","url":null,"abstract":"<p><strong>Introduction: </strong>Despite remarkable advancements in deep learning for low-dose computed tomography (LDCT) denoising, two significant challenges persist: (i) the requirement for paired LDCT and high-dose computed tomography (HDCT) images, which are often impractical to obtain in clinical settings; and (ii) the tendency of existing methods to train models using a single axial view, thereby overlooking complementary information from other views and consequently limiting their performance.</p><p><strong>Methods: </strong>To address these issues, we propose a Multi-view-to-Single Knowledge Transfer (MvSKT) framework for unpaired LDCT denoising. Our approach involves splitting the 3D unpaired computed tomography (CT) data into 2D images from various views, including axial, sagittal, and coronal. This allows us to train three view-independent 2D GAN models in an unsupervised manner. By stacking successive 2D outputs from each view-independent model into a volumetric format and splitting them into axial-view images, we generate multiple complementary predictions for each axial CT image. Leveraging these predictions as priors, we transfer multi-view knowledge to a single-view model through pseudo-supervision. This process involves fusing multiple view-complementary predictions into reliable pseudo-images using a cycle-consistency-weighted method.</p><p><strong>Results: </strong>Extensive experiments on the AAPM-Mayo dataset demonstrate that MvSKT outperforms other unpaired denoising methods and even achieves performance comparable to supervised approaches.</p><p><strong>Discussion: </strong>Consequently, the MvSKT framework effectively harnesses multi-view information from unpaired data to enhance LDCT denoising without the strict requirement of paired clinical data.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1691258"},"PeriodicalIF":4.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13194603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019271","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":"Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report.","authors":"Elena Filimonova, Anton Pashkov, Galina Moysak, Azniv Martirosyan, Vladimir Kurilov, Aleksandra Poptsova, Renata Morozova, Jamil Rzaev","doi":"10.3389/fradi.2026.1809871","DOIUrl":"10.3389/fradi.2026.1809871","url":null,"abstract":"<p><p>Olfactory groove meningiomas are uncommon skull base tumors that often present at advanced stages and may cause persistent cognitive and behavioral disturbances despite successful surgical treatment. Neuroimaging studies of this tumor entity have been limited, and publicly available multimodal MRI datasets remain scarce. Here, we present a prospective, single-center dataset comprising multimodal magnetic resonance imaging and longitudinal clinical data from patients with olfactory groove meningiomas acquired before and after surgical intervention. The dataset includes high-resolution structural MRI, diffusion MRI with tensor-derived metrics, resting-state functional MRI, tumor and peritumoral edema segmentation masks, and detailed clinical and neuropsychological assessments. Imaging data were acquired using a standardized protocol and processed with reproducible pipelines, including quality control and de-identification procedures, and organized in a BIDS format. This dataset is intended to support reproducible research and secondary analyses focused on tumor-related brain alterations, imaging biomarker development, and postoperative recovery in neuro-oncology.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1809871"},"PeriodicalIF":4.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13194104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019232","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":"Intravenous contrast medium impairs CT-based muscle quality but not quantity assessment: a translational study.","authors":"Luca Salhöfer, Gregor Jost, Mathias Holtkamp, Jannis Straus, Marcel Opitz, Sebastian Zensen, Rene Hosch, Johannes Harmes, Lale Umutlu, Michael Forsting, Felix Nensa, Hubertus Pietsch, Johannes Haubold","doi":"10.3389/fradi.2026.1746296","DOIUrl":"10.3389/fradi.2026.1746296","url":null,"abstract":"<p><strong>Objectives: </strong>CT-based body composition analysis (BCA) provides imaging biomarkers, including muscle volume and surrogates of muscle quality. Concerns over the comparability of Non-contrast and contrast-enhanced CT scans have limited their clinical application. This study aims to assess the influence of various contrast phases on a volumetric CT-based BCA.</p><p><strong>Materials and methods: </strong>20 Göttingen minipigs were subjected to a Non-contrast (NC) and five contrast-enhanced [Early Arterial, Late Arterial, Vascular Portal Venous, Parenchymal Portal Venous (PPV), Late] CT scans. 114 tri-phasic (Non-Contrast, Arterial, Venous) CT scans were analyzed for human validation. A volumetric BCA network [Body and Organ Analysis (BOA)] extracted muscle radiodensity and the following features as volumes: Muscle, Subcutaneous Adipose Tissue (SAT), Inter- and Intramuscular Adipose Tissue (IMAT), Visceral Adipose Tissue (VAT), and Total Adipose Tissue (TAT). Significance was assessed by a one-way ANOVA with Tukey's multiple comparisons test.</p><p><strong>Results: </strong>In the animal model, there was a tendency toward reduced IMAT volumes after CM injection [NC = 245 ml (±105 ml), e.g., PPV = 241 ml (±105 ml)]. Muscle radiodensity was significantly higher following CM administration [Non-contrast: 51.1 HU (±1.9 HU), e.g., Late: 56.6 HU (±2.4 HU), <i>p</i> < 0.001]. The human validation analysis showed similar tendencies for the IMAT volume [Non-contrast: 1750ml (±729 ml), Venous: 1,552 ml (±696 ml), <i>p</i> = 0.10] and significantly higher muscle radiodensity [Venous: 39.2 HU (±9.1 HU), Non-contrast: 35.6 HU (±7.8), <i>p</i> = 0.007].</p><p><strong>Conclusion: </strong>Myosteatosis surrogates, such as muscle radiodensity or IMAT, are susceptible to interference by CM, while quantification of muscle tissue and extramuscular fat remains robust.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1746296"},"PeriodicalIF":4.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13195011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019216","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":"A preoperative nomogram for predicting 2-year postoperative recurrence after percutaneous transforaminal endoscopic decompression in degenerative lumbar spinal stenosis.","authors":"Xinyi Luo, Yiwen Wang, Lele Xue, Qi Liu, Yue Yang, Qin Zhang, Shiwu Yin","doi":"10.3389/fradi.2026.1821920","DOIUrl":"10.3389/fradi.2026.1821920","url":null,"abstract":"<p><strong>Background and objectives: </strong>Postoperative recurrence after percutaneous transforaminal endoscopic decompression (PTED) for degenerative lumbar spinal stenosis (DLSS) remains a clinically relevant challenge, complicating preoperative counseling and long-term management. Reliable tools for predicting individual 2-year recurrence risk using routinely available preoperative data are currently lacking. This study aimed to develop and internally validate a practical preoperative nomogram for individualized recurrence risk prediction after PTED.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study including 206 patients with DLSS who underwent single-level PTED between August 2021 and August 2023. Preoperative clinical and imaging variables were extracted to construct a multivariable logistic regression model. Candidate predictors were prespecified based on clinical relevance and routine availability. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. Internal validation was performed using 1000 bootstrap resamples and leave-one-out cross-validation (LOOCV).</p><p><strong>Results: </strong>During the 2-year follow-up period, 29 patients (14.08%) experienced postoperative recurrence. The final nomogram incorporated five preoperative predictors: body mass index, diabetes mellitus, lumbosacral transitional vertebrae, number of levels with senior grade facet degeneration, and paraspinal skeletal muscle index. The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.845 (95% CI, 0.778-0.912). Bootstrap validation showed a mean AUC of 0.842 (95% CI, 0.772-0.912), and LOOCV yielded an AUC of 0.797 (95% CI, 0.716-0.878). Calibration was satisfactory, and decision curve analysis demonstrated net clinical benefit across a wide range of threshold probabilities.</p><p><strong>Conclusions: </strong>We developed a clinically interpretable preoperative nomogram that reliably predicts 2-year postoperative recurrence after PTED in patients with DLSS. By integrating routinely assessed clinical and imaging factors, this tool may facilitate individualized risk stratification, support informed preoperative counseling, and guide risk-adapted perioperative management. External validation in independent cohorts is warranted.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1821920"},"PeriodicalIF":4.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977415","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 MRI features of fetal complete agenesis of the corpus callosum associated with unilateral hemispheric cortical malformation: a retrospective study.","authors":"Huihui Lin, Xiaoyu Wang, Chang Wang, Gengwu Li, Xu Li","doi":"10.3389/fradi.2026.1802335","DOIUrl":"10.3389/fradi.2026.1802335","url":null,"abstract":"<p><strong>Introduction: </strong>To explore the prenatal MRI features of fetal complete agenesis of the corpus callosum (cACC) associated with unilateral hemispheric cortical malformation.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 101 cases diagnosed with cACC via prenatal MRI. Among them, 16 cases were found to be associated with unilateral hemispheric cortical malformation. The imaging characteristics of this specific malformation were analyzed, and its associations with fetal gender and gestational age were investigated.</p><p><strong>Results: </strong>In this group of 16 cases of cACC with unilateral cortical malformation, 13 fetuses were male and 3 were female. The gestational age at diagnosis ranged from 23 weeks to 27 weeks and 2 days. The malformation was located in the left hemisphere in 7 cases and in the right hemisphere in 9 cases. Type I (C1 type: extensive cortical twisting/folding) was observed in 11 cases, all of which exhibited the \"rake sign\" and \"garland sign\". The \"rake sign\" manifests as abnormally folded dysplastic cortex resembling a rake on axial or coronal views, while the \"garland sign\" appears as abnormally folded dysplastic cortex resembling a garland on sagittal views. Type II (C4 type: transcortical cleft formation) was observed in 1 case, and Type III (C5 type: focal cortical indentation or serrated changes) was observed in 4 cases. The unilateral cortical malformation involved the frontal lobe in 16 cases, the parietal lobe in 10 cases, and the occipital lobe in 8 cases, with no involvement of the temporal lobe.</p><p><strong>Discussion: </strong>Fetal agenesis of the corpus callosum has a relatively high probability of being associated with unilateral cortical malformation. This malformation occurs more frequently in males, is mostly detected during the mid-trimester, and most commonly involves the frontal lobe. The \"rake sign\" and \"garland sign\" are its characteristic imaging features. Prenatal definitive diagnosis aids in the perinatal management of this malformation. Prenatal MRI is a crucial supplementary examination for fetal cACC diagnosed by prenatal ultrasound, as it can accurately detect associated unilateral hemispheric cortical malformations that are easily missed by ultrasound, and thus is essential for optimizing perinatal management and parental genetic counseling.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1802335"},"PeriodicalIF":4.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13175969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965821","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}