BACKGROUND:Jaw lesions, like cysts and tumors, demonstrate substantial variation in internal architectural organization, spatial heterogeneity, and voxel-level complexity on cone-beam computed tomography (CBCT). Conventional radiological interpretation relies predominantly on subjective visual assessment and may not adequately capture these underlying imaging phenotypes. AIM:To evaluate whether CBCT-derived radiomic features can quantitatively characterize architectural phenotypes of jaw lesions and to assess their discrimination using interpretable artificial intelligence (AI) models. METHODS:This retrospective study analyzed 100 histopathologically confirmed jaw lesions using CBCT. Lesions were manually segmented using 3D Slicer and 107 radiomic features were extracted after standardized preprocessing and voxel normalization using PyRadiomics. Lesions were classified into homogeneous fluid-dominant, intermediate septated, and complex heterogeneous phenotypes. Feature stability was assessed using intraclass correlation coefficients, while selection employed false discovery rate (FDR) correction, correlation pruning, and LASSO regression. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models underwent stratified five-fold cross-validation and independent chronological validation. RESULTS:Forty radiomic features demonstrated statistically significant differences among architectural phenotypic groups following FDR correction. Feature reduction yielded a compact radiomic signature predominantly composed of texture-derived descriptors reflecting gray-level non-uniformity, spatial dependence variability, entropy, and structural complexity. The LR model demonstrated the highest performance, achieving an area under the receiver operating characteristic curve of 0.92, with robust discrimination between architectural phenotypes. SVM and RF models demonstrated comparable but lower performance. Lesions categorized within the complex heterogeneous phenotype exhibited significantly elevated texture heterogeneity metrics compared with homogeneous fluid-dominant lesions, supporting the biological relevance of radiomic architectural characterization in differentiating complex jaw pathologies. CONCLUSION:CBCT-derived radiomic features enable quantitative assessment of internal architectural phenotypes in jaw lesions, particularly patterns related to spatial heterogeneity and structural organization. Texture-based radiomic signatures, when integrated with interpretable AI models, may function as imaging biomarkers for objective lesion characterization and may support future development of biologically informed diagnostic decision-support systems in oral and maxillofacial radiology.
Pediatric computed tomography (CT) is clinically indispensable for emergency care, neurologic disorders, thoracoabdominal diseases, trauma assessment, and oncologic diagnosis and follow-up. However, children should not be regarded as small adults. They are actively growing and developing, and organs or tissues such as the hematopoietic system, thyroid, breast, gonads, lens, central nervous system, and bone marrow are more sensitive to ionizing radiation. In addition, children have a longer life expectancy, allowing a longer latency window for radiation-related late effects. Epidemiological studies suggest associations between pediatric CT exposure and cumulative-dose-related risks of leukemia, brain tumors, and overall cancer; although the individual absolute risk is generally low, stricter justification, optimization, and dose recording remain necessary in pediatric populations. Conventional volume CT dose index (CTDIvol) and dose-length product primarily describe scanner output under standard phantom conditions and cannot adequately characterize differences in patient body size, tissue attenuation, organ location, and scan coverage. Size-specific dose estimate (SSDE), which corrects CTDIvol using a patient-size conversion factor, represents an important intermediate dose descriptor linking scanner output, pediatric body-size characteristics, organ-dose estimation, and scan-protocol optimization. This review focuses on the specific requirements of pediatric CT dose assessment. It summarizes the conceptual evolution of SSDE, the selection of size metrics, applications across examination sites, relationships with organ dose and radiation-risk assessment, pediatric diagnostic reference levels, and future directions in automated dose management. The aim is to provide a conceptual and practical basis for individualized pediatric CT dose optimization.
Accurate, noninvasive assessment of disease activity remains one of the central challenges in the management of Crohn's disease (CD). The study by Dong et al in World Journal of Radiology represents a meaningful advance in this effort, demonstrating that dual-energy computed tomography with the material decomposition algorithm can reliably quantify intestinal creeping fat (CrF), a pathological hallmark of CD, and that this measurement correlates robustly with clinical, endoscopic, histological, and molecular indicators of disease activity. Notably, CrF volume tracked not only inflammatory burden and fibrosis, but also the expression of peroxisome proliferator-activated receptor gamma 2 and occludin, implicating CrF in both adipogenesis and intestinal barrier dysfunction. Crucially, CrF remained elevated during clinical remission, underscoring its potential as a more sensitive marker of subclinical disease persistence than conventional activity indices alone. As the field continues to seek non-invasive surrogates for mucosal healing and histological remission, dual-energy computed tomography-based CrF quantification emerges as a promising, clinically accessible tool. With further validation in larger, multicenter cohorts, this approach could meaningfully reshape how we monitor disease activity, evaluate treatment response, and predict disease course in CD.
Small cell lung cancer (SCLC) remains among the most aggressive thoracic malignancies and continues to present major therapeutic challenges despite advances in multimodality treatment. Limited-stage SCLC (LS-SCLC), representing approximately one-third of cases, is conventionally managed with concurrent chemoradiotherapy delivered with curative intent. Radiation therapy (RT) is central to treatment; however, rapid treatment related anatomical and volumetric changes may alter target geometry and reduce the accuracy of plans generated from baseline imaging. Adaptive RT (ART) has emerged as an imaging-guided strategy that enables treatment modification according to evolving anatomy during RT delivery. This narrative review examines the rationale and current evidence supporting imaging-guided ART in LS-SCLC, with emphasis on imaging-informed decision-making and contemporary implementation considerations. LS-SCLC represents a biologically compelling setting for adaptive treatment because of its rapid treatment responsiveness; however, important uncertainties remain regarding patient selection, imaging schedules, adaptive triggers, and workflow standardization. Prospective studies incorporating standardized adaptive workflows and clinically meaningful endpoints are required to determine whether observed dosimetric advantages translate into measurable clinical benefit.
Radiology, particularly neuroradiology, has become a major focus of research and industrial investment in artificial intelligence and machine learning (ML). These technologies may help address increasing imaging volumes, workforce shortages, and the need for faster and more consistent interpretation. This article summarizes recent developments in ML applications across neuroradiology. In acute ischemic stroke, ML supports early lesion detection, automated Alberta Stroke Program Early Computed Tomography Score assessment, large-vessel-occlusion detection, infarct core and penumbra estimation, collateral evaluation, workflow prioritization, and outcome prediction. Further applications include cerebral aneurysm detection and prediction of intracerebral hemorrhage expansion and prognosis. In neuro-oncology, current uses include tumor segmentation and classification, molecular-marker prediction, treatment-response assessment, surgical and radiotherapy planning, differentiation of recurrence from pseudoprogression, and prognostication. Additional advances involve image reconstruction, automated quantification, diagnostic classification, and outcome prediction in spine imaging; lesion detection and segmentation in demyelinating disease; and identification and characterization of neurodegenerative disorders. Despite this progress, limited generalizability, insufficient external validation, and poor interpretability remain major barriers to clinical adoption. Explainable artificial intelligence, federated learning, and robust multicenter validation are likely to be central to future clinical implementation.
We comment on the recent observational study by Hopley et al reporting outcomes of 17-year branch duct intraductal papillary mucinous neoplasm surveillance and proposing de-escalation criteria based on cyst stability below 30 mm and serum carbohydrate antigen 19-9 below 43 KU/L after two years of follow-up. While acknowledging the clinical value of these findings, we raise several radiological concerns that deserve further consideration before these criteria can be safely implemented across institutions. Specifically, we discuss the absence of standardised imaging protocols and field strength reporting, the well-documented inter-observer variability in magnetic resonance imaging (MRI)-based cyst size measurement, the evolving radiological definitions of worrisome and high-risk features across guidelines updates, the complementary roles of MRI/ magnetic resonance cholangiopancreatography and endoscopic ultrasound, and the lack of a specified imaging algorithm for the de-escalated surveillance phase. These considerations are intended to help refine the safe implementation of the proposed de-escalation strategy rather than to argue against it.
Radiomics-the high-throughput extraction of quantitative features from standard medical images-has transformed oncologic imaging by revealing subvisual patterns linked to tissue biology, yet its role in perioperative medicine remains largely unexplored. This narrative review summarises current evidence linking imaging-derived radiomic biomarkers to perioperative outcomes and proposes a conceptual framework for integrating radiomics into precision anaesthesia. Quantitative assessment of body composition, organ function, and vascular morphology from routine preoperative computed tomography and magnetic resonance imaging can provide objective indicators of physiologic reserve, drug-handling capacity, and recovery potential. Across heterogeneous, largely oncological cohorts, combined radiomic-clinical models have reported higher discrimination (area under the curve 0.84-0.93) than conventional risk scores for selected postoperative complications, and artificial intelligence-based airway assessment has shown sensitivity and specificity exceeding traditional bedside tests; these figures are pooled from methodologically diverse studies rather than single validated estimates. The Image Biomarker Standardisation Initiative has substantially reduced cross-platform feature variability. However, perioperative-specific, prospectively validated evidence remains scarce. Prospective multicentre trials, standardized and automated feature-extraction pipelines, transparent cost and equity appraisal, and integration with electronic health records are critical priorities before radiomics-driven preoperative assessment can enter routine anaesthetic practice.
BACKGROUND Selective internal radiation therapy (SIRT) has been widely used in the treatment of hepatocellular carcinoma (HCC). However, marked intrahepatic shunt (IHS) secondary to arterio-portal shunt (APS) often precludes safe SIRT administration. AIM To evaluate the efficacy and safety of portal vein embolization (PVE) in reducing IHS and enabling SIRT therapy in HCC patients with APS. METHODS This retrospective study enrolled seven HCC patients with APS confirmed by hepatic angiography during mapping. PVE was performed to close the APS outlet and single photon emission computed tomography/computed tomography was performed to determine the distribution of (99m) technetium-labeled macroaggregated albumin. These patients meeting the criteria for SIRT subsequently underwent Yttrium-90 (Y-90) resin microspheres administration. Changes in liver function and complications were monitored during the 6-month follow-up period. RESULTS Complete APS occlusion was achieved in six patients (n = 6, 6/7). The hepatopulmonary shunt rate was below 20% (4.65%-16.95%) in five of six patients with APS occluded, and Y-90-SIRT was subsequently administered in these patients. No severe procedural complications occurred. CONCLUSION PVE is a safe and effective strategy to reduce IHS in HCC patients with APS, thereby improving eligibility for SIRT.
BACKGROUND Acute pancreatitis is a common abdominal pathology with significant morbidity and mortality in select patients. Currently, prediction of outcome and prognosis in patients with acute pancreatitis is based on a variety of indices, scores, and criteria calculated from biochemical, clinical, and imaging parameters, but all currently available predictors of outcome have limitations. Analytic morphomics, a quantitative technique that assesses body composition from cross-sectional imaging, may offer a novel approach to outcome prediction. AIM To evaluate whether analytic morphomics-derived parameters can predict recurrence and mortality after an index episode of acute pancreatitis. METHODS Following ethical approval, a retrospective single-centre study was conducted at Cork University Hospital. Adult patients presenting with acute pancreatitis between January 2012 and December 2013 were included and followed for 10 years. Patients with pancreatic malignancy, equivocal diagnoses, or age < 18 years were excluded. Cases of acute pancreatitis were classified as acute interstitial oedematous pancreatitis or necrotising pancreatitis. Demographic, biochemical, and clinical data were collected, including the modified Glasgow Imrie severity score. Computed tomography examinations were analysed using CoreSlicer for semi-automated segmentation of skeletal muscle and adipose tissue at the L3 vertebral level. Statistical analysis was performed using Microsoft Excel and Jamovi, including the Mann-Whitney U test, Pearson correlation, and logistic regression. RESULTS Seventy-two patients were included with a median modified Glasgow Imrie severity score of 2. Wall muscle (WM) density correlated inversely with disease severity [WM density (Hounsfield units): r = -0.480, P < 0.001; psoas muscle density (Hounsfield units): r = -0.465, P < 0.001]. Higher WM density and greater subcutaneous fat (SCF) area were associated with reduced 30-day mortality (mean difference 14.4 HU, P = 0.003; 65.3 cm2, P = 0.033, respectively) and improved 10-year survival. On multivariate analysis, lower WM density and lower SCF area independently predicted short- and long-term mortality, while no morphomic variables were independently associated with recurrence. CONCLUSION Skeletal muscle quality and SCF are protective factors in acute pancreatitis. Integration of morphomics with existing severity scores may enhance prognostication. Larger studies are needed to validate these findings.
BACKGROUND Chronic cardiopulmonary diseases, including chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease, represent a major global health burden. Low-dose computed tomography (LDCT) combined with artificial intelligence (AI) quantitative imaging enables the identification of cardiopulmonary imaging abnormalities in asymptomatic individuals. AIM To evaluate early risk factors for cardiopulmonary imaging abnormalities in asymptomatic middle-aged and elderly population using single-inspiratory phase LDCT combined with AI-based whole-lung quantitative analysis. METHODS A retrospective collection was conducted on 1035 asymptomatic individuals aged ≥ 40 years who underwent routine single-inspiratory-phase LDCT screening at Zhuzhou 331 Hospital in 2025. An AI platform was utilized to automatically extract airway wall area percentage, low-attenuation area percentage, interstitial lung abnormality, and coronary artery calcification score. Based on risk stratification criteria, the population was divided into a high-risk group (n = 689) and a low-risk group (n = 346) and randomly stratified into a training set (n = 724) and a validation set (n = 311) at a 7:3 ratio. Independent sample t -test or χ 2 test was applied to analyze between-group differences. Binary logistic regression was used to screen independently associated factors, and a multivariate logistic regression model was constructed after excluding collinear variables using variance inflation factor (VIF < 5), followed by the establishment of a nomogram prediction model. The receiver operating characteristic curve and DeLong test were employed to evaluate model discrimination. The Hosmer-Lemeshow test and calibration curves were used to assess goodness of fit. Decision curve analysis (DCA) was applied to evaluate clinical net benefit, and internal validation was performed using the bootstrap method (1000 resamplings). RESULTS Univariate analysis showed that smoking history, abnormal metabolic status, body mass index (BMI), age, and gender were significantly associated with cardiopulmonary imaging-defined high-risk status (P < 0.05), while work style showed a marginal association in univariate analysis (P = 0.043) but did not retain statistical significance in the multivariate model (P = 0.851). After VIF collinearity screening (all VIF < 5) and multivariate logistic regression analysis with forced entry of six candidate variables, smoking history [odds ratio (OR) = 1.968 per level, 95% confidence interval (CI): 1.665-2.325, P < 0.001], abnormal metabolic status (OR = 3.266, 95%CI: 2.259-4.723, P < 0.001), BMI (OR = 1.150 per unit, 95%CI: 1.079-1.226, P < 0.001), and age (OR = 1.028 per year, 95%CI: 1.005-1.052, P = 0.018) were identified as independently associated factors for cardiopulmonary imaging-defined high-risk status, while male gender demonstrated an (inverse) association (OR = 0.427, 95%CI: 0.276-0.660, P < 0.001) after adjustment for smoking and other covariates. The nomogram-based risk stratification model integrating the above five independently associated factors achieved an area under the curve (AUC) of 0.833 (95%CI: 0.787-0.879) in the validation set, significantly outperforming the baseline model containing only age and gender (AUC = 0.647, 95%CI: 0.582-0.712; DeLong test: Delta AUC = 0.186, z = 5.256, P < 0.001). The Hosmer-Lemeshow goodness-of-fit test confirmed satisfactory calibration (training set: χ 2 = 10.40, P = 0.238; validation set: χ 2 = 6.50, P = 0.591). Calibration curves and DCA demonstrated good agreement and positive clinical net benefit. Bootstrap internal validation (1000 resamples) confirmed model robustness (mean AUC = 0.792, 95%CI: 0.760-0.824). CONCLUSION The LDCT-based nomogram model combined with AI quantitative imaging analysis may assist in identifying individuals with cardiopulmonary imaging abnormalities in asymptomatic screening populations, potentially guiding further diagnostic evaluation. Prospective studies are warranted to establish its role in improving clinical outcomes.
Magnetic resonance imaging (MRI) has an established role for identifying and monitoring inflammatory bowel disease. This review summarizes key MRI findings and select technique considerations for bowel pathologies beyond inflammatory bowel disorders, emphasizing how imaging features within this modality can support diagnosis and assist with management. We highlight MRI features of inflammatory and infectious conditions, mechanical obstruction, neoplasms, and mesenteric ischemia. We also discuss how particular imaging techniques may improve diagnostic confidence, including diffusion-weighted imaging when contrast cannot be administered and rapid motion-robust sequences to reduce motion artifacts from peristalsis and breathing.
The study by Yang and Li in the World Journal of Radiology reports strong predictive performance for hematoma enlargement, perihematomal edema (PHE) and hospital mortality using hand crafted quantitative radiomics and deep learning models in patients with spontaneous intracerebral haemorrhage (ICH). While the authors report strong predictive performance using quantitative radiomics and deep learning features, several methodological concerns warrant discussion. By letting the deep learning models based on pretrained Convolutional Neural Network (CNN) to detect the density range of 50-400 Hounsfield units, may falsely detect age related and pathological basal ganglionic calcifications as heterogeneity within the ICH. This may lead to increased false positive rates in the prediction of hematoma enlargement. Similarly, by not restricting the density range between 25-35 Hounsfield units for pretrained CNN to detect PHE, may also add bias by falsely detecting age related micro ischaemic areas and chronic lacunar infarcts in basal ganglionic region as PHE. Further, hand crafted quantitative radiomics by trained clinical radiologists will calculate volume of ICH and PHE more accurately as compared to the CNN models using only three consecutive axial slices centered on the maximum haematomal cross-sectional area, as reported in the original study. Hence, an integrated model consisting of hand crafted quantitative radiomics by trained clinical radiologist with deep learning models based on pre trained CNN with the above-mentioned enhancements and a human touch by a trained clinical radiologist to remove the mentioned confounding factors is highly recommended for better outcomes.
Radiolabeled exosomes have emerged as a transformative platform at the intersection of nanomedicine, molecular imaging, and precision theranostics. These nanoscale extracellular vesicles exhibit intrinsic biocompatibility, low immunogenicity, and inherent targeting capabilities, making them highly attractive for both diagnostic and therapeutic applications. The integration of radiochemistry with exosome biology enables noninvasive, real-time tracking of biodistribution, pharmacokinetics, and target engagement using advanced imaging modalities such as positron emission tomography and single-photon emission computed tomography. This minireview comprehensively summarizes current radiolabeling strategies for exosomes, including direct and indirect approaches, highlighting their advantages, limitations, and impact on vesicle integrity and imaging accuracy. Furthermore, we discuss key imaging platforms, in vivo biodistribution patterns, and pharmacokinetic profiles that influence therapeutic efficacy. Critical challenges such as rapid clearance by the mononuclear phagocyte system, labeling instability, and the lack of standardized protocols are also addressed. Finally, we outline future perspectives focusing on advanced bioengineering, multimodal imaging integration, and clinical translation frameworks. Radiolabeled exosomes represent a promising next-generation theranostic system with the potential to enable personalized, image-guided therapies across oncology and regenerative medicine.
Pancreatic cystic neoplasms are being identified with increasing frequency, largely because high-resolution cross-sectional imaging is now routinely performed in a wide variety of clinical settings. Among available imaging techniques, magnetic resonance imaging (MRI), especially when combined with magnetic resonance cholangiopancreatography, plays a central role in lesion characterization thanks to its excellent soft-tissue contrast and detailed evaluation of the pancreatic ductal system. The most common pancreatic cystic neoplasms include intraductal papillary mucinous neoplasms, serous cystic neoplasms, mucinous cystic neoplasms, and solid pseudopapillary neoplasms, which together account for the majority of cystic pancreatic tumors. Many of these lesions are detected incidentally during imaging examinations performed for unrelated indications, creating an increasingly common diagnostic challenge in routine radiological practice. This review summarizes both typical and atypical MRI findings of the most common cystic pancreatic neoplasms, with particular attention to imaging features that may support differential diagnosis and influence clinical management. In particular, MRI and magnetic resonance cholangiopancreatography are essential for evaluating lesion morphology, intracystic components, and communication with the pancreatic ductal system, all of which are important for lesion characterization and risk stratification. Familiarity with the broad spectrum of MRI appearances is crucial for radiologists in order to improve diagnostic accuracy, recognize atypical presentations, and guide appropriate patient management.
BACKGROUND:Preeclampsia (PE) is a multifactorial hypertensive disorder specific to pregnancy that significantly contributes to maternal and perinatal morbidity and mortality worldwide. According to hospital-based studies, its prevalence in India ranges from 5% to 15%. Despite advances in obstetric care, the early identification of women at risk remains a major clinical challenge. Ophthalmic artery Doppler (OAD), a noninvasive and reproducible imaging technique, provides important information on cerebral autoregulation and maternal vascular resistance with potential exploratory utility in the early detection of PE. AIM:To evaluate the predictive value of maternal OAD indices measured during the second trimester (17-23 weeks of gestation) for the subsequent development of PE in a cohort of initially normotensive pregnant women. METHODS:This prospective observational study, conducted from March 2024 to February 2025, enrolled 90 antenatal women in their second trimester with no prior history of hypertension or renal disease. OAD evaluation was performed using the transorbital approach with the Alpinion E-CUBE 8 ultrasound system, and key Doppler indices were recorded. Participants were subsequently followed through the third trimester to assess the development of PE. Statistical analysis was performed using the independent samples t-test for continuous variables, Fisher's exact test for categorical variables, and receiver operating characteristic curve analysis to assess the predictive performance of Doppler parameters. RESULTS:Out of the 90 participants, 8 (8.89%) developed PE during follow-up. The resistive index (P = 0.004), pulsatility index (P = 0.0003), and most notably, the peak ratio (PR) (P < 0.0001) showed statistically significant differences between the normotensive and preeclamptic groups. Among the Doppler parameters, PR demonstrated the best diagnostic performance, with a diagnostic accuracy of 91.11%, sensitivity of 100%, specificity of 90.24%, and area under the receiver operating characteristic curve of 0.90. Elevated PR values were strongly associated with the subsequent development of PE, indicating potential predictive value that requires further validation in larger studies. CONCLUSION:Maternal OAD measurements, particularly PR, demonstrated significant associations with the subsequent development of PE in this study cohort. Its noninvasive nature, ease of application, and observed diagnostic performance suggest that it may warrant further evaluation as an adjunctive risk stratification tool in antenatal screening protocols, particularly in resource-limited settings. The early detection and management of high-risk pregnancies could be greatly improved by incorporating OAD into standard obstetric care, potentially improving maternal and perinatal outcomes.
Percutaneous biliary interventions are indicated for both benign or malignant biliary strictures to treat obstructive jaundice and/or biliary leak. Major vascular complications include active bleeding or more frequently pseudoaneurysm formation, bilio-portal, bilio-venous or arterio-biliary fistulae and intra- or peri-hepatic hematoma. Such adverse events can occur during the procedure itself or, more often, several days to weeks after the initial procedure. Major vascular complications frequently manifest with symptoms of hemobilia (jaundice, pain, melena or hemochezia and reduced serum hemoglobin levels), which in some cases lead to life-threatening hemodynamic compromise. Computed tomography angiography is mandatory to diagnose the cause and site of bleeding, while simultaneously depicting the anatomy, in order to plan the treatment. Covered stents, liquid embolic agents and coils are the commonest devices used, depending on the underlying pathology. Although such complications are rare, they are frequently life-threatening, and require prompt treatment. Therefore, interventional radiologists must be proficient in their management. This narrative minireview aims to outline management strategies of the most frequent intra- or post-procedural vascular complications following biliary interventions.
We read with great interest the prospective study by Zhuang et al published in the recent issue of the World Journal of Radiology , regarding the cerebral blood flow changes following transjugular intrahepatic portosystemic shunt creation provides a vital hemodynamic correlate to the pathogenesis of hepatic encephalopathy. However, this letter argues that cerebrovascular alterations should not be interpreted in isolation but rather as the downstream consequence of broader systemic dysregulation. We highlight the critical role of emerging predictors such as sarcopenia, shunt magnitude, and portal vein anatomy, in modulating the neurotoxic burden within the gut-liver-brain axis. By integrating these systemic variables with cerebral hemodynamic metrics, we propose a multidimensional approach to improve risk stratification. This perspective is essential for developing personalized transjugular intrahepatic portosystemic shunt planning strategies that minimize hepatic encephalopathy risk while maintaining shunt efficacy.
Hepatic encephalopathy (HE) is a common complication following transjugular intrahepatic portosystemic shunt (TIPS) placement and is thought to result from reduced hepatic detoxification of neurotoxins. However, Zhuang et al in the World Journal of Radiology demonstrate that this understanding may be incomplete through exploration of the pathophysiology from a hemodynamic perspective. They found significant changes in cerebral blood flow (CBF) and spontaneous brain activity following TIPS. Initially, CBF increased in specific areas within the first month, followed by a return to baseline at three months. These CBF changes coincided with increased spontaneous brain activity that persisted in some areas, suggesting compensation for functional loss in other regions. Together with the fact that serum ammonia remained unchanged in this cohort and prior work showing that, after three months, HE risk returns to pre-TIPS levels, these results suggest that HE may develop in individuals susceptible to these hemodynamic and adaptive changes, warranting further research.
