Search Results (3,715)

Background and Objectives: Acetabular fracture surgery is associated with substantial perioperative blood loss and prolonged operative time. Routine preoperative pelvic computed tomography (CT) carries information about body composition that is not currently exploited for risk stratification. We tested whether (i) CT-defined pelvic sarcopenia is associated with lower preoperative haemoglobin and (ii) preoperative subcutaneous fat cross-sectional area (CSA) is independently associated with operative time, after adjustment for surgical approach, age, fracture complexity and sarcopenia status. Materials and Methods: In this single-centre retrospective cohort study, 48 adults (37 men, 11 women; mean age 40.2 ± 16.5 years) who underwent open reduction and internal fixation (ORIF) for unilateral acetabular fractures between 2016 and 2024 were included. Pelvic muscle and subcutaneous fat CSAs were measured on the contralateral side of preoperative CT images using ImageJ. Sarcopenia was defined as an internal, cohort-relative classification based on the sex-specific bottom tertile of psoas CSA. Normality was assessed by Shapiro–Wilk testing; Pearson or Spearman correlation was used accordingly, and the 36 pairwise correlations were controlled with the Benjamini–Hochberg false-discovery-rate procedure. The multivariable model used ordinary least squares regression with heteroscedasticity-consistent (HC3) standard errors and a median quantile-regression robustness check. Results: Sarcopenic patients (n = 17) had significantly lower preoperative haemoglobin (12.63 ± 1.24 vs. 14.00 ± 1.53 g/dL; p = 0.002; Cohen’s d = 0.96). The absolute perioperative haemoglobin drop was numerically smaller in the sarcopenic group (ΔHb 1.64 ± 0.91 vs. 2.46 ± 1.87 g/dL) but did not reach statistical significance (p = 0.079); estimated blood loss (p = 0.258) and transfusion requirement (p = 0.567) did not differ between groups. Pelvic muscle CSAs correlated positively with preoperative haemoglobin (all q < 0.05 after Benjamini–Hochberg correction). In the multivariable model (F[6, 41] = 3.71, p = 0.005; adjusted R² = 0.26; all variance inflation factors 1.06–1.26), subcutaneous fat CSA (B = +0.25 min/cm², p = 0.004) and the modified Stoppa approach (vs. Kocher–Langenbeck; +65 min, p = 0.001) were independently associated with operative time. Conclusions: In this exploratory retrospective cohort, routine preoperative pelvic CT contained two body-composition signals that may warrant prospective evaluation: pelvic sarcopenia, which was associated with lower baseline haemoglobin, and subcutaneous adiposity, which was associated with longer operative time in the primary regression model. Both signals require confirmation—the sarcopenia–bleeding relationship was not statistically significant, and the subcutaneous fat association was attenuated under robust inference. These findings are hypothesis-generating; prospective multicentre validation with height-normalised sarcopenia thresholds and body mass index is required before clinical implementation. Full article

Background: Nasopharyngeal carcinoma (NPC) presents significant diagnostic and therapeutic challenges, often due to late-stage detection and its complex anatomical location. The increasing integration of artificial intelligence (AI) into oncology offers potential opportunities to enhance the precision of NPC management. This systematic review aims to synthesise the current evidence of AI applications in NPC diagnosis, prognostication, and treatment planning. Methods: A systematic literature search was conducted following PRISMA guidelines across multiple databases (PubMed, Scopus, Embase, Google Scholar, IEEE Xplore) for studies published up to June 2025. From an initial pool of 2549 articles, 55 studies meeting the inclusion criteria were selected for qualitative analysis. The review focuses on AI models applied to key diagnostic modalities: computed tomography (CT), magnetic resonance imaging (MRI), and histopathological whole-slide images (WSI). Results: AI, particularly deep learning (DL), shows promising performance in automating critical tasks across all modalities. For CT and MRI, models have been reported to achieve accurate tumor and organ-at-risk segmentation, potentially supporting radiotherapy planning, and show strong performance in predicting survival outcomes and treatment toxicity. In digital pathology, AI enables automated diagnosis and facilitates the extraction of prognostic “pathomic” features from WSIs, with some studies suggesting performance comparable to or exceeding traditional radiomics. The most significant advances are seen in multimodal AI systems that integrate radiological, pathological, and clinical data, which, in some studies, show modest improvements in prognostic performance compared to single-modality approaches. However, these findings are preliminary, as none of the reviewed multimodal models underwent rigorous external validation in large, multi-center cohorts. Reported performance varies considerably across studies, and claims of superiority should be interpreted with caution. Full article

Background/Objectives: Pain is one of the most disabling symptoms in patients with unresectable pancreatic ductal adenocarcinoma (PDAC), having a major impact on quality of life, functional status, and tolerance to oncologic treatment. Percutaneous computed tomography (CT)-guided cryoablation may provide tumor-directed pain palliation in selected patients. This study aimed to evaluate the safety and palliative clinical benefit of percutaneous CT-guided cryoablation in patients with painful unresectable PDAC. Methods: This retrospective single-center pilot case series included 11 consecutive patients with painful unresectable PDAC treated with percutaneous CT-guided cryoablation between January 2022 and May 2024. Primary endpoints were change in visual analogue scale (VAS) score and reduction in analgesic requirement. Secondary endpoints included technical success, adverse events, supportive clinical outcomes, imaging evolution, progression status, and survival. Results: Technical success was achieved in all procedures (11/11, 100%). No major procedure-related complications occurred; minor adverse events were observed in 3/11 patients (27.3%). Mean VAS score decreased from 6.72 ± 1.56 at baseline to 3.45 ± 1.44 at 1 month, 2.54 ± 1.29 at 3 months, 2.27 ± 1.43 at 6 months, and 1.60 ± 1.07 at 12 months. At 1 month, all patients showed a reduction of at least 3 VAS points. A reduction in analgesic requirement was documented in all patients during early follow-up, with complete opioid discontinuation in 5/11 patients (45.5%). At 1-month CT, residual enhancement was present in 9/11 patients (81.8%), although with an estimated 50–80% reduction in enhancing tumor burden. Observed survival proportions at 6 and 12 months were 90.9% and 72.7%, respectively. Conclusions: Percutaneous CT-guided cryoablation appears to be a feasible palliative option for selected patients with painful unresectable PDAC, with meaningful pain relief, opioid sparing, acceptable short-term safety, and exploratory imaging evidence of local cytoreductive effect. Further prospective studies are warranted. Full article

Background: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, is becoming increasingly prevalent with the global population aging. Computed tomography (CT) is widely used for muscle assessment; however, concerns regarding radiation exposure have prompted interest in lower-dose imaging protocols. This study investigated the performance of radiomics-based machine learning (ML) models for sarcopenia detection using abdominal CT (APCT) and low-dose CT (LDCT). Methods: Radiomics features were extracted from CT images following skeletal muscle segmentation, and ML models were developed using logistic regression, support vector machine, and random forest. Model performance was evaluated using fivefold cross-validation with out-of-fold predictions. Results: The random forest model demonstrated the best performance among the evaluated models, achieving an area under the receiver operating characteristic curve of 0.720 (95% CI: 0.532–0.881) for APCT and 0.692 (95% CI: 0.573–0.801) for LDCT. Model interpretation using SHapley Additive exPlanations analysis identified several intensity-based radiomics features, including TotalEnergy, as important contributors to sarcopenia prediction. Conclusions: These findings suggest that radiomics features derived from LDCT images may provide useful information for sarcopenia detection. Because LDCT is widely used in clinical settings such as lung cancer screening, radiomics analysis of LDCT images may offer an additional opportunity for opportunistic sarcopenia assessment. Full article

Background and Clinical Significance: The crista terminalis (CT) is a physiological fibromuscular ridge in the right atrium. While benign, rare cases of CT hypertrophy present a diagnostic challenge, as it can mimic a pathological right atrial mass on cardiac imaging. The CT also presents arrhythmogenic potential and is known to be associated with right atrial tachyarrhythmias. Case Presentation: We present the case of a 58-year-old female that presented with rapid, irregular palpitations, accompanied by hypertension. Holter electrocardiography (ECG) confirmed self-limiting episodes of atrial tachycardia (max heart rate 170 bpm). Initial transthoracic echocardiography (TTE) identified an echogenic, non-mobile mass on the posterolateral right atrial wall. Transesophageal echocardiography (TEE) confirmed a 12 × 9 mm homogenous structure with a broad base of implantation and no intrinsic mobility, initially raising the suspicion of an atrial lipoma. Subsequent cardiac computed tomography angiography (CCTA) provided high-resolution tissue characterization, identifying the mass as a hypertrophied CT due to its precise anatomical orientation and its lack of contrast enhancement, also ruling out neoplastic and thrombotic aetiologies. Conclusions: CT hypertrophy is a key differential diagnosis for right atrial masses, particularly in females in their sixth decade. A multimodal imaging approach, transitioning from TTE to TEE and finally CCTA or Cardiac Magnetic Resonance Imaging (CMR), is advantageous in preventing unnecessary invasive interventions or anticoagulation. Full article

Accurate and automatic segmentation of the liver and liver tumors from computed tomography (CT) images is essential for computer-assisted diagnosis, treatment planning, and clinical decision-making. Although deep learning-based segmentation models, particularly U-Net and its variants, have achieved promising results in medical image analysis, many existing approaches mainly focus on local pixel-level feature extraction and may have limited ability to explicitly model long-range spatial relationships among anatomically meaningful regions. In addition, liver tumor segmentation remains challenging due to low contrast, irregular tumor boundaries, heterogeneous tumor appearances, and noise or artifacts in CT images. To address these limitations, this study proposes a hybrid ensemble neural network architecture that integrates an improved U-Net and a Graph U-Net for automatic liver and liver tumor segmentation. The improved U-Net is designed to capture fine-grained local features and preserve detailed spatial information through an encoder–decoder structure with skip connections, while the Graph U-Net uses Simple Linear Iterative Clustering (SLIC)-based superpixels to construct a graph representation of CT images and model spatial dependencies between adjacent image regions. By combining these complementary representations through an ensemble learning strategy, the proposed framework enhances both pixel-level segmentation accuracy and robustness against noisy imaging conditions. The proposed method was evaluated on the LiTS17 dataset, where CT images were preprocessed using intensity filtering, resizing, data augmentation, and normalization. Experimental results demonstrate that the proposed ensemble architecture achieves 99.2% accuracy for liver segmentation and 98.1% accuracy for liver tumor segmentation, outperforming representative segmentation models such as MultiresUnet and R2U-Net. Furthermore, robustness experiments under different signal-to-noise ratio conditions show that the proposed model maintains stable performance in noisy CT images, achieving 85% accuracy even under severe noise at −4 dB SNR. This result highlights the advantage of integrating convolutional feature learning with graph-based spatial relationship modeling for improving segmentation stability when image quality is degraded by noise or artifacts. These findings indicate that the integration of improved U-Net, SLIC-based graph construction, and Graph U-Net provides an effective and noise-robust solution for liver and liver tumor segmentation, with potential applicability as a computer-assisted tool in clinical image analysis after further validation on larger and external datasets. Full article

Anterior cervical discectomy and fusion (ACDF) is one of the most commonly performed surgical procedures for the treatment of cervical degenerative disease, myelopathy, radiculopathy, and segmental instability. Although clinical outcomes are generally favorable, pseudarthrosis remains a relevant complication, with a reported incidence ranging from 5% to 20%. In a field with no yet clear main directions, this narrative review aims at giving the reader a broad picture and a wide analysis of the recent advances in cervical spinal fusion, with particular focus on biomaterials, intervertebral cage technologies, cervical spine biomechanics and imaging methods used for fusion assessment. The literature regarding quantitative imaging parameters and emerging applications of artificial intelligence (AI) is also reviewed. Current bone grafts include autologous grafts, allografts, xenografts and polymeric grafts, while the materials for the intervertebral cages comprehend titanium, polyetheretherketone and silicon nitride, with reported fusion rates distributed in a very large range. Computed tomography (CT) remains the standard imaging modality to assess whether fusion has occurred, due to its high spatial resolution. However, the lack of shared diagnostic criteria and the significant interobserver variability continue to limit its reliability. Quantitative parameters, such as Hounsfield Unit measurements and MRI-derived bone quality scores, may contribute to a more objective evaluation, although current evidence remains heterogeneous. In parallel, AI-based imaging analysis is showing promising results for quantitative assessment and longitudinal monitoring of bone fusion; however, large prospective clinical studies are still needed to confirm its clinical applicability. In conclusion, despite advances in surgical technologies and biomaterials, radiological assessment of cervical fusion still lacks universally accepted diagnostic standards. Future AI applications may improve diagnostic accuracy and reproducibility, promoting a more standardized approach in clinical practice. Full article

Background: Head trauma is a major public health concern. Computed tomography (CT) is frequently used to evaluate these patients but may expose them to unnecessary radiation exposure. Various biomarkers have been investigated to predict prognosis and reduce the need for unnecessary imaging. Red cell distribution width (RDW), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) have been proposed as inflammatory markers; however, their diagnostic value in head trauma remains controversial. This study aimed to determine the value of complete blood count parameters in identifying intracranial injury in patients with mild head trauma. Methods: This prospective, single-center study enrolled 100 adults with mild head trauma. Demographic data, vital signs, neurological assessments, complete blood counts, CT results, and clinical outcomes were also recorded. Patients were categorized as intracranial injury positive (Group 1) or intracranial injury negative (Group 2). We statistically compared the laboratory and demographic data of the groups. Statistical significance was set at p < 0.05. Results: The study included 100 patients with mild head trauma who presented to the emergency department, of whom 11 were in Group 1. The median PLR and lymphocyte levels differed significantly between the groups (p < 0.05). Conclusions: The PLR may serve as a preliminary supportive marker to aid clinical assessment; however, its modest discriminatory performance suggests that it should not be used as a standalone diagnostic tool. Full article

Background: Preoperative assessment of Head and Neck Squamous Cell Carcinoma (HNSCC) aggressiveness is often hindered by the sampling errors of incisional biopsies. While Contrast-Enhanced Computed Tomography (CECT) is the standard for staging, its potential to serve as a non-invasive complementary radiological tool of the entire tumor volume remains underutilized. Objective: To evaluate the predictive performance of preoperative CECT-derived tumor volume, densitometric values, and morphological features as predictors of histopathological grade and lymph node metastasis (pN) in HNSCC. The primary outcome was predicting lymph node metastasis (pN+), and the secondary outcome was predicting histopathological grade. Methods: This retrospective observational study analyzed 42 patients with SCC of the oral cavity, larynx, or maxilla. Quantitative (3D volume, Hounsfield Units [HU], HU Delta) and qualitative (margins, lobulations, necrosis) CT parameters were correlated with definitive histopathology. Diagnostic performance was assessed using Receiver Operating Characteristic (ROC) curve analysis and Spearman’s rank correlation. Results: High-grade tumors (G2/G3) demonstrated significantly larger median volumes (18.1 vs. 2.9 cm³, p = 0.006), lower contrast density (55 vs. 68 HU, p = 0.010), and reduced vascular wash-in (23 vs. 30 HU Delta, p = 0.008) compared to G1 lesions. ROC analysis identified a volume threshold of ≥ 9.43 cm³ for high-grade disease (AUC = 0.865; sensitivity 67.6%, specificity 100%). For regional metastasis (pN+), tumor volume was the only significant predictor (25.4 vs. 6.2 cm³, p = 0.036), with an optimal cut-off of ≥6.76 cm³ (AUC = 0.769; sensitivity 100%). Strong negative correlations were observed between contrast enhancement and internal necrosis (r = −0.812, p < 0.001). Conclusions: Preoperative CECT parameters show promise as non-invasive imaging surrogates of HNSCC aggressiveness. A paradoxical reduction in contrast enhancement characterizes high-grade biology, reflecting disorganized neo-angiogenesis and internal hypoxia. Integrating 3D volumetric analysis and morphological markers shows potential as a complementary exploratory tool that, pending future prospective validation, may support risk stratification and surgical planning alongside traditional histopathological assessment. Full article

Background: Pneumocystis jirovecii pneumonia (PJP) increasingly affects non-HIV immunocompromised patients; however, the spectrum of computed tomography (CT) findings in this population remains poorly defined. Objectives: To describe and compare chest CT findings of PJP in patients with and without HIV infection and to evaluate the impact of respiratory coinfections on imaging patterns. Methods: This retrospective single-centre cohort study included 72 adult patients with confirmed PJP diagnosed between 2011 and 2024, 27 HIV-positive and 45 non-HIV immunocompromised patients. Chest radiography was available in 71 patients and chest CT in 62. Imaging studies were independently reviewed for predefined patterns, including ground-glass opacities, alveolo-interstitial pattern, mosaic attenuation, crazy paving, pulmonary cysts, consolidation, and pleural effusion. CT findings were compared between HIV-positive and non-HIV patients, and a subgroup analysis was performed in non-HIV patients according to the underlying type of immunosuppression. Respiratory coinfections were recorded and classified based on microbiological results. Results: Chest radiography was normal in 32.4% of patients. An interstitial pattern tended to be more frequent in HIV-positive patients, whereas consolidations were more commonly observed in non-HIV patients (p = 0.051). On CT, ground-glass opacities were the predominant finding in both groups. HIV-positive patients more frequently demostrated an alveolo-interstitial pattern, mosaic attenuation, and pulmonary cysts, while consolidations and pleural effusions were more common in non-HIV patients, particularly among solid organ transplant recipients. Respiratory coinfections were identified in 63.9% of patients; however, no statistically significant differences in CT patterns were observed between patients with and without coinfections. Conclusions: PJP demonstrates different CT presentations according to immune status. HIV-positive patients more frequently demonstrated alveolo-interstitial patterns, mosaic attenuation, and pulmonary cysts, whereas consolidations were more commonly observed in non-HIV immunocompromised patients. Respiratory coinfections do not appear to significantly influence CT patterns. Full article

Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy because of its low radiation dose and on-board acquisition capability. However, CBCT images often suffer from scatter artifacts, increased noise, reduced soft-tissue contrast, and inaccurate Hounsfield Unit (HU) values, which limit their direct use for accurate dose calculation and quantitative analysis. To address this limitation, we propose a CBCT-to-CT synthesis framework based on 2.5D context encoding (concatenating five adjacent slices along the channel dimension) and latent-space variational diffusion. The proposed method combines a Vector Quantized Variational Autoencoder (VQ-VAE) and a U-shaped Vision Transformer (U-ViT)-based latent-space Variational Diffusion Model (VDM) to translate CBCT images into synthetic CT (sCT) images in a compressed latent space. To incorporate inter-slice anatomical context while preserving the computational efficiency of 2D processing, five adjacent CBCT slices are concatenated along the channel dimension and used as input. We evaluated the proposed method on the SynthRAD2025 paired CBCT-CT dataset covering head-and-neck, thoracic, and abdominal regions. Under the provided benchmark setting, quantitative evaluation on the validation set showed that the proposed 2.5D model improved peak signal-to-noise ratio (PSNR) from 25.39 dB to 27.44 dB (averaged across regions), structural similarity index measure (SSIM) from 0.813 to 0.846, reduced mean squared error (MSE) from 0.00313 to 0.00200, and lowered Fréchet inception distance (FID) from 1009.33 to 869.53 compared with the 2D baseline. Qualitative results also showed improved anatomical consistency and reduced artifact-related distortions. These findings suggest that neighboring-slice context can enhance HU fidelity and overall image quality in a computationally practical synthesis framework, supporting the usefulness of efficient AI-based cross-modality reconstruction for radiotherapy-related imaging workflows. Full article

This study compares three methods for 2D bone metastasis segmentation on computed tomography slices-the self-configuring nnU-Net pipeline, a fine-tuned DINOv3 foundation model, and a prompt-free MedSAM foundation model adaptation-to assess their suitability for clinical-grade lesion delineation. Methods: A dataset of 2D CT slices from 88 patients (11,006 image–label pairs) was annotated by experts. The three models were trained and evaluated under comparable conditions, using model-specific native input resolutions and training schedules. Performance was evaluated using the Dice similarity coefficient (DSC) and Normalized Hausdorff distance (NHD) on a held-out test set, with a separate cohort of previously unseen patients. On a held-out test set, the MedSAM, DINOv3, and nnU-Net models achieved the following Dice scores: 0.6280, 0.4480, and 0.6849, respectively. Additionally, on a held-out test set, the MedSAM, DINOv3, and nnU-Net models achieved the following normalized Hausdorff distances: 0.1008, 0.1019, and 0.0473, respectively. In conclusion, the nnU-Net framework provides robust segmentation performance and serves as a strong baseline for 2D slice-wise bone metastasis delineation even with limited annotated data. Full article

Background/Objectives: Pancreatic cancer (PC) should be diagnosed in its early stages. Therefore, it is necessary to identify high-risk individuals of PC. Methods: Between 2001 and 2017, 1542 PC cases were diagnosed at two tertial care institutions. Of these, 117 cases had undergone abdominal contrast-enhanced computed tomography (CE-CT) 1–10 years before PC diagnosis and were classified as the PC group. Meanwhile, 43,102 cases underwent abdominal CE-CT for close examination of non-pancreatic diseases in the same period, of which 1170 were randomly selected. Of these, 117 cases were matched to the PC group with the propensity score and designated the non-PC group. Pancreatic volumetry was performed using the 3D image analysis system for abdominal CE-CT in both groups and various measurements were compared. In PC group, CE-CT taken 1–10 years before the onset of PC was analyzed. Results: After propensity score matching, baseline characteristics did not significantly differ between the two groups. The whole pancreatic volume/body surface area (BSA) (p = 0.014), volume of main pancreatic duct (MPD) plus cystic lesion/BSA (p < 0.001), volume of pancreatic parenchyma/BSA (p = 0.002), ratio of cross-sectional areas (p = 0.033), and MPD diameter/BSA (p < 0.001) significantly differed between the two groups. In subgroup analysis of patients without cystic lesions, the whole pancreatic volume/BSA, volume of MPD/BSA, volume of pancreatic parenchyma/BSA, ratio of cross-sectional areas, and MPD diameter/BSA significantly differed between the two groups. Conclusions: Pancreatic volumetry could identify patients at high risk of PC. Full article

High-resolution thin-slice computed tomography (CT) images are often compressed into lower-quality thick-slice images for long-term storage, necessitating synthesis for medical diagnosis. In this paper, we propose a novel 3D adversarial diffusion model (3DAD) for high-fidelity synthesis of thin-slice CT from compressed thick-slice CT. 3DAD is composed of a generator and a discriminator for synthesizing denoised thin-slice images from random noise and source images and distinguishing between noised samples from real and denoised synthetic thin-slice images. Specific models were trained on two-slice to six-slice scenarios for abdominal data, using thick-slice CT compressed from real thin-slice CT as the source. 3DAD was evaluated at the time of HCC diagnosis, at the observation and patient levels, using real thin-slice and synthetic thin-slice CT, with DeLong’s test to compare the similarity of receiver operating characteristic (ROC) curves. We further evaluated 3DAD on real-world data with both thin and thick images, with the synthetic image quality assessed by radiologists and in radiomics feature analysis. Based on the external dataset with 548 samples, the achieved mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) values were 81.374, 29.478, and 0.916, respectively, for the five-slice scenarios at the portal venous phase. The Areas Under Curves (AUCs) achieved were 0.896 on synthetic thin-slice images compared with 0.889 on real thin-slice images at the observation level (p = 0.028) and 0.854 versus 0.846, correspondingly, at the patient level (p = 0.055). For evaluation on the real-world testing dataset after fine-tuning at the portal venous phase, the MSE, PSNR, and SSIM were 70.435, 30.243, and 0.94, respectively. Radiologist evaluation confirmed the high quality of the synthetic image, with no significant difference in the majority of cases across all five parameters, except for radiologist 2, in realistic and consistent situations, under which at least 41 of 43 synthetic images were assessed as equal to or above grade 3. Our 3DAD enabled the synthesis of thick-slice CT images into high-resolution thin-slice images, facilitating high-fidelity volume image application in HCC diagnosis. Full article

Arterial wall fragility represents a unifying pathophysiological substrate underlying a broad spectrum of aortic diseases, including aneurysms, dissections, intramural hematoma, penetrating atherosclerotic ulcers, and aortitis. Rather than distinct entities, these conditions increasingly appear as interconnected manifestations of impaired wall integrity and maladaptive vascular remodeling. This narrative review provides a structured overview of the imaging correlates of arterial wall fragility from a CT-centered, multimodality perspective. Computed Tomography Angiography (CTA) remains the first-line imaging modality in acute settings, enabling rapid and comprehensive assessment of vascular anatomy, luminal integrity, and the presence of life-threatening complications. Complementary modalities, including magnetic resonance imaging and ultrasound, contribute additional information on tissue characterization and hemodynamic evaluation in selected stable patients, follow-up settings, or specific clinical scenarios. Across imaging modalities, specific features—such as false lumen patency, intramural hematoma characteristics, ulcer-like projections, aneurysm morphology, and periaortic inflammatory changes—have been reported as markers of wall instability. These imaging-derived findings may provide clinically relevant information beyond traditional diameter-based assessment and support more refined risk stratification. Emerging approaches, including artificial intelligence, radiomics, computational modeling, and advanced MRI techniques, are expanding the role of imaging toward quantitative evaluation. However, their routine clinical implementation still requires standardization and prospective validation. Overall, a CT-centered multimodality imaging strategy may support a more comprehensive assessment of arterial wall fragility and contribute to individualized clinical decision-making in patients with aortic disease. Full article