Background: The literature shows that anterior communicating artery (AcoA) aneurysms are the most common intracranial aneurysms. To date, there has only been one postmortem study focused on the correlations between autopsy findings and imaging results in cases of intracranial aneurysms associated with anatomical variants of the circle of Willis (CW). Methods: We investigated the anatomical variants of the CW associated with the occurrence and rupture of AcoA aneurysms by performing comparative analyses, in the same patients, of postmortem autopsy data with antemortem computed tomography–angiography (CTA) results obtained in the first 48 h after the onset of subarachnoid hemorrhage. Our retrospective observational study identified the anatomical variants of the CW at autopsy in 16 deceased adult Romanian patients with AcoA aneurysms over a 12-year period (2010–2022). Results: The autopsy findings revealed that the AcoA ruptured aneurysms had a mean external diameter of 9.50 mm, and 71.4% of them presented three or four anatomical variants inside the same CW. The initial antemortem CTA examination correctly located the AcoA aneurysms in all cases (100%), and an anatomical variant of the CW was only noted in 18.75% of patients. The final postmortem re-analyzed the same CTA images identified in all cases (100%), focusing on both the AcoA aneurysm and all anatomical variants of the CW found during the autopsies. Conclusions: Although it was previously thought that the occurrence of AcoA aneurysms is related only to the hemodynamic changes induced by the nearby arterial anatomical variants, we identified the simultaneous involvement of at least one hypoplastic artery and one or two PCA fetal-type anatomical variants that were located in both the anterior and posterior parts of the CW. Furthermore, if sufficient time is devoted to the CT–angiography analysis and interpretation of the images, anatomical variants of the circle of Willis associated with AcoA aneurysms can be identified as accurately as they are in invasive postmortem autopsy examinations.

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease.

The objective is to analyze possible correlations between histological types, hematological tests and erythrocyte sedimentation rate that can be identified in vertebral metastases secondary to digestive cancers. Materials and methods: The present study is a retrospective observational research of 27 patients with spinal metastases identified in the Prof. Dr. N. Oblu Emergency Clinical Hospital in Iași over a period of 8 years (2014-2021). For each patient, were recorded: patient demographics, including age and gender; tumor histology; hematological analyses (erythrocyte count, hemoglobin, hematocrit) and erythrocyte sedimentation rate. Results: The mean age of the group was 64.3 years. The male: female ratio was 19: 8=2.375. The most frequent histological subtypes of vertebral metastases were colonic adenocarcinomas (CAC) (n=12 cases; 44.4%,) and hepatocellular carcinomas (HCC) (n=6 cases; 22.2%), followed by squamous cell carcinomas (SCC) of oral and rectal origin (n=3 cases; 11.1%), rectal adenocarcinomas (RAC) (n=2 cases; 7.4%), and cholangiocarcinomas (CC) (n=2 cases; 7.4%). RAC cases presented the lowest erythrocyte counts (2.62 ×10⁶/mL), while neuroendocrine carcinoma (NEC) of the small intestine showed the highest mean values (4.20 ×10⁶/mL). RAC cases presented the lowest hemoglobin levels (8.3 g/dL), while CC and NEC (small intestine) demonstrated relatively higher averages (>11 g/dL). RAC cases demonstrated the lowest mean hematocrit (~31%), while oral cavity SCC presented the highest mean values (~38%). Spinal metastases of CAC, HCC, or CC showed intermediate levels (36-37%). Spinal metastases of RAC cases presented with the highest mean ESR values (~60-70 mm/h), and NEC of small intestine showed the lowest averages (~25 mm/h). Conclusions: Hematological profile and erythrocytes sedimentation rate (ESR) in spinal metastases of digestive origin show reproducible, histology‑specific patterns: rectal primaries cluster with profound iron‑deficiency anemia and high ESR; colon adenocarcinoma and hepatocellular carcinoma show intermediate anemia with variable inflammation; cholangiocarcinoma and neuroendocrine tumors of small intestine often preserve hematological parameters with lower ESR. Integrating RBC/Hb/Ht with ESR enhances clinical triage, informs supportive care, and may improve prognostication when embedded in multivariable models.

Background: Pulmonary fibrosis (PF) and post-infectious fibrotic lung disease are characterized by marked heterogeneity in radiologic patterns, physiologic impairment, and clinical presentation. Conventional analytic approaches often fail to capture non-linear and multimodal relationships between structural imaging findings and functional limitation. Integrating imaging-derived representations with clinical and functional data using artificial intelligence (AI) may provide a more comprehensive characterization of disease heterogeneity. Objectives: The objective of this study was to develop and evaluate a multimodal AI framework combining imaging-derived embeddings and structured clinical data to identify atypical clinical–radiologic profiles in patients with pulmonary fibrosis using unsupervised anomaly detection. Methods: A retrospective cohort of 41 patients with radiologically confirmed pulmonary fibrosis or post-infectious fibrotic lung disease was analyzed. Deep imaging embeddings were extracted from baseline thoracic CT examinations using a pretrained convolutional neural network and integrated with standardized clinical and functional variables. A multimodal variational autoencoder (VAE) was trained in an unsupervised manner to learn the distribution of typical patient profiles. Patient-specific anomaly scores were derived from reconstruction error plus latent regularization (β·KL divergence). Associations between anomaly scores, disease severity, and clinical markers were assessed using Spearman rank correlation. Results: Anomaly scores were right-skewed (median 26.91, IQR 22.87–32.11; range 19.75–46.18). Patients above the 85th percentile (anomaly score ≥ 33.85) comprised 7/41 (17.1%) of the cohort and occurred across all clinician-assigned severity categories (mild 3, moderate 1, severe 3). Anomaly scores overlapped substantially across severity groups, with similar medians (mild 26.47, moderate 28.55, severe 28.23). Correlations with conventional severity markers were weak and non-significant, including DLCO (% predicted; ρ = −0.25, p = 0.115) and FEV₁ (% predicted; ρ = −0.22, p = 0.165), a pattern consistent with anomaly scores reflecting multimodal deviation rather than severity alone, while acknowledging the exploratory nature of the analysis. Highly anomalous patients frequently exhibited discordant clinical–radiologic profiles, including preserved functional capacity despite marked imaging-derived deviation or disproportionate physiological impairment relative to imaging patterns. Conclusions: This proof-of-concept study demonstrates that multimodal VAE-based anomaly detection integrating imaging-derived embeddings with clinical data can quantify clinical–radiologic heterogeneity in pulmonary fibrosis beyond conventional severity stratification. Unsupervised anomaly detection provides a complementary framework for identifying atypical multimodal profiles and supporting individualized phenotyping and hypothesis generation in fibrotic lung disease. Given the modest cohort size, these findings should be interpreted as illustrative and hypothesis-generating rather than generalizable.

Background: Coronary artery disease remains the leading cause of morbidity and mortality worldwide. Increasing evidence has demonstrated that many acute coronary events originate not from severely obstructive lesions but from structurally unstable atherosclerotic plaques. Consequently, the identification of vulnerable plaques has become a major objective in contemporary cardiovascular imaging. Coronary computed tomography angiography (CCTA) has evolved from a modality primarily focused on luminal stenosis assessment into a comprehensive non-invasive technique capable of evaluating plaque morphology, composition, and overall coronary atherosclerotic burden. Results: Our review summarizes current evidence regarding the role of CCTA in the detection and characterization of vulnerable coronary plaques. Particular emphasis is placed on imaging biomarkers associated with plaque instability, including low-attenuation plaque, positive remodeling, spotty calcifications, and the napkin-ring sign, which have been consistently linked to an increased risk of adverse cardiovascular events. Advances in quantitative plaque analysis have further enhanced the ability of CCTA to assess plaque burden and monitor disease progression. Emerging technologies are rapidly expanding the capabilities of coronary CT imaging. Spectral CT techniques and photon-counting detector CT offer improved tissue characterization and enhanced visualization of plaque components. In parallel, artificial intelligence and machine learning algorithms are enabling automated plaque detection, segmentation, and risk prediction, facilitating more precise and reproducible analysis of large imaging datasets. Conclusions: Despite these advances, limitations remain, including restricted spatial resolution for microstructural plaque assessment and the limited ability of conventional CT to directly evaluate biological processes such as inflammation. Future developments integrating advanced CT technologies, artificial intelligence, and multimodality imaging are expected to further improve the non-invasive identification of vulnerable plaques and personalized cardiovascular risk stratification.