Search Results (5,813)

Introduction: Pneumonitis represents one of the clinically significant adverse events observed in patients with non-small-cell lung cancer (NSCLC) who receive durvalumab as consolidation therapy after chemoradiotherapy (CRT). Although clinical factors such as radiation dose (e.g., V20) and interstitial lung abnormalities (ILAs) have been reported as risk predictors, accurate and objective prognostication remains difficult. This study aimed to develop a radiomics-based machine learning model to predict grade ≥ 2 pneumonitis. Methods: This retrospective study included patients with unresectable NSCLC who received CRT followed by durvalumab. Radiomic features, including first-order and texture and shape-based features with wavelet transformation were extracted from whole-lung regions on pre-durvalumab computed tomography (CT) images. Machine learning models, support vector machines, k-nearest neighbor, neural networks, and naïve Bayes classifiers were developed and evaluated using a testing cohort. Model performance was assessed using five-fold cross-validation. Conventional predictors, including V20 and ILAs, were also assessed using logistic regression and receiver operating characteristic analysis. Results: Among 123 patients, 44 (35.8%) developed grade ≥ 2 pneumonitis. The best-performing model, a support vector machine, achieved an AUC of 0.88 and accuracy of 0.81, the conventional model showed lower performance with an AUC of 0.71 and accuracy of 0.64. Conclusions: Radiomics-based machine learning demonstrated superior performance over clinical parameters in predicting pneumonitis. This approach may enable individualized risk stratification and support early intervention in patients with NSCLC. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

This paper presents the results of a comprehensive technological study of three bronze sculptures from the Jin Empire period (1115–1234 AD) from the collection of the Museum of Archaeology and Ethnography at the Institute of History, Archaeology and Ethnography of the Peoples of the Far East, Far Eastern Branch of the Russian Academy of Sciences (IHAE FEB RAS). Using photon-counting computed tomography (PCCT) and energy-dispersive X-ray spectroscopy (EDS), the production techniques were reconstructed, differences in alloy composition were identified, and specific features of the casting processes were determined. Tomographic analysis revealed two fundamentally different manufacturing approaches: a multi-stage technology involving the use of different alloys and the assembly of separately cast elements, and a single-cast technology with a homogeneous structure. Elemental analysis of the three sculptures using EDS demonstrated significant compositional variability—from 21% to 67% copper and from 9% to 69% tin in different parts of the objects—confirming the complexity of the technological processes. An expanded study of 20 bronze sculptures using portable X-ray fluorescence analysis (pXRF) allowed for the identification of four typological alloy groups: classic balanced lead–tin bronzes (Cu 30–58%, Sn 16–23%, Pb 16–28%), high-lead bronzes (Pb up to 52%), high-tin bronzes (Sn up to 30%), and low-tin alloys (Sn less than 11%). The morphological features of the sculptures suggest one of their possible interpretations as ancestor spirits used in ritual practices. The research findings contribute to the study of Jurchen metallurgical traditions and demonstrate the potential of interdisciplinary, non-destructive analytical methods for reconstructing the technological, social, and cultural aspects of medieval Far Eastern societies. Full article

Background/Objectives: To date, no studies have examined radiologic findings by histologic patterns of primary hepatic angiosarcoma; this study clarified radiologic findings of primary hepatic angiosarcoma according to distinct histologic patterns. Methods: From January 2010 to October 2024, 17 individuals (mean age, 69 years ± 11; 11 men) with pathologically confirmed primary hepatic angiosarcoma underwent computed tomography (CT) with or without magnetic resonance imaging (MRI). Histologic patterns were classified as mass-forming, subdivided into vasoformative and non-vasoformative (epithelioid and spindled) patterns, or non-mass-forming, subdivided into sinusoidal and peliotic patterns. Two radiologists independently reviewed CT and MRI images, classifying lesions as non-mass-forming or mass-forming. Hypervascular portions and targetoid patterns were also assessed. Associations between histologic patterns and radiologic findings were evaluated using Fisher’s exact test. Results: Mass-forming tumors were observed in 13 individuals (76.5%), and non-mass-forming tumors in 4 individuals (23.5%). Significant correlation (p < 0.05) was found between radiologic classification (non-mass-forming or mass-forming) and corresponding pathologic patterns. Pathologic subdivision into vasoformative and non-vasoformative patterns did not correlate with hypervascular portions on imaging. Conclusions: Pathological classification into mass-forming and non-mass-forming patterns corresponds closely to radiologic classification of mass-forming and non-mass-forming lesions, indicative of strong pathologic features in imaging. Full article

Background: Accurate risk stratification in acute pulmonary embolism (PE) is critical for guiding management. This study assessed the prognostic value of computed tomography (CT) indicators of right-heart strain and thrombus burden for predicting in-hospital adverse events. Methods: In this retrospective cohort of 300 patients with CT-confirmed acute PE, the right-to-left ventricular (RV/LV) diameter ratio, Pulmonary Artery Obstruction Index (PAOI), and inferior vena cava (IVC) contrast reflux were measured. The primary endpoint was in-hospital adverse events, including hemodynamic collapse, vasopressor or ventilatory support, rescue reperfusion therapy, or death. Logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: Adverse events occurred in 106 patients (35.3%). Compared with stable patients, those with events had higher RV/LV ratios (1.45 vs. 1.03), higher PAOI (38.8 vs. 24.3), and more frequent IVC reflux (74% vs. 7%) (all p < 0.001). Independent predictors were RV/LV ratio (aOR 3.22 per 0.1), PAOI (aOR 5.53 per 10 points), and IVC reflux (aOR 428.5; all p < 0.001). The model showed excellent discrimination (AUC = 0.96). Conclusions: CT-derived indices of right-heart strain and thrombus burden are strong, independent predictors of in-hospital adverse events in acute PE and should be integrated into routine CT-based risk assessment. Full article

Background/Objectives: Intracranial hemorrhage (ICH) is a life-threatening condition that can be rapidly detected by non-contrast head computed tomography (NCCT). RAPID ICH is a deep learning (DL) tool for automatic ICH identification using NCCT. Our aim was to assess the real-world performance of RAPID ICH compared to that of a first-year radiology resident on consecutively acquired NCCTs from patients referred from the Emergency Department. Methods: This single-center retrospective cohort study included NCCTs acquired on the same CT scanner over three months. Exclusion criteria were motion or metallic artifacts that substantially degraded the NCCT quality and incomplete NCCTs. Two senior neuroradiologists conducted ground-truth labeling of the NCCTs regarding ICH presence in a binary manner. The first-year radiology resident assessed NCCTs for ICH presence and was blinded to the ground-truth labeling. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed for the RAPID ICH identifications and for the first-year radiology resident’s ICH identifications. Results: After applying exclusion criteria, 844 NCCTs remained. Ground-truth labeling found ICH in 63 NCCTs. RAPID ICH showed 87.3% sensitivity, 74% specificity, 21.3% PPV, and 98.6% NPV, while the first-year radiology resident achieved 95.2% sensitivity, 90.8% specificity, 45.5% PPV, and 99.6% NPV. There were 8 false-negative and 203 false-positive RAPID ICH identifications. Conclusions: RAPID ICH’s sensitivity and specificity were lower than in prior studies performed using RAPID ICH, and there was a high number of false-positive RAPID ICH identifications, limiting the generalizability of the assessed version of this DL tool. Testing DL tools by comparing them with radiologists of varying experience can provide valuable insights into their performance. Full article

Background: Sarcoidosis is a multisystem inflammatory disorder characterized by non-caseating granulomas, most commonly affecting the lungs and intrathoracic lymph nodes. Angiotensin-converting enzyme (ACE) levels and calcium abnormalities are recognized biomarkers, while ^18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is increasingly used to assess disease activity. However, neither provides sufficient diagnostic accuracy alone. Therefore, this study aimed to investigate the relationship between FDG-PET/CT metabolic findings and serum ACE and calcium (Ca²⁺) levels as surrogate indicators of inflammatory metabolic intensity in sarcoidosis. Methods: In this retrospective single-center study, 127 patients with pulmonary sarcoidosis who underwent PET/CT at diagnosis were evaluated. Demographic and clinical data, ACE, and Ca²⁺ levels were recorded. FDG uptake in mediastinal, pulmonary, and extrapulmonary sites was analyzed, and correlations with biomarkers were assessed. Results: The cohort included 89 females (70.1%) and 38 males (29.9%), mean age 51.3 ± 11.9 years. FDG uptake was most frequent in mediastinal lymph nodes (84.3%) and lung parenchyma (40.9%). ACE levels correlated weakly with total SUVmax (r = 0.214, p = 0.019). Calcium levels correlated with extrapulmonary SUVmax (r = 0.327, p = 0.001) and were higher in patients with extrapulmonary involvement (p = 0.045). No associations were found between symptom presence and biomarkers or SUVmax values. Conclusions: FDG-PET/CT metabolic parameters, particularly total and extrapulmonary SUVmax, demonstrated modest yet statistically significant associations with ACE and calcium levels. These findings suggest that a combined biomarker-imaging approach may provide complementary information regarding inflammatory metabolic intensity and systemic involvement; however, the results should be interpreted as exploratory and require validation in prospective studies. Full article

Objectives: This study aims to evaluate the efficacy of hydroxyapatite-tricalcium phosphate (HAP-TCP) as a bone substitute in subantral augmentation for dental implants. Specifically, it investigates the effects of HAP-TCP on bone quality, density, and integration with implants over time. Methods: A prospective controlled longitudinal study was conducted on 22 patients (39–75 years of age) undergoing subantral augmentation and dental implantation. A total of 52 sites of augmented bone and 67 sites of native bone were analyzed using computed tomography (CT) to assess bone density in Hounsfield Units (HU), insertion torque measurements, and the Misch classification for bone quality. Augmented and native bone measurements were compared within each patient. Results: The augmented bone exhibited an average density of 1132.6 ± 334.9 HU, which is significantly higher (45.9%) than the average density of native bone at 519.3 ± 395.0 HU. Insertion torque values in the HAP-TCP augmented sites averaged 35 N·cm, showing a 71.4% increase compared to adjacent native bone sites (25 N·cm). The study found notable improvements in bone homogeneity and vascularization within the augmented zones. Conclusion: HAP-TCP demonstrates significant potential as a reliable and effective synthetic bone substitute for subantral augmentation in dental implants. It yields higher radiodensity and insertion torque than adjacent native bone, while mitigating complications associated with autogenous grafts. These observational findings support the potential clinical use of HAP-TCP for sinus augmentation. Full article

Objective: To assess whether chronic rhinosinusitis (CRS) severity is associated with obstructive sleep apnea (OSA) in adult people with cystic fibrosis (pwCF). Methods: We conducted a retrospective single-center study of 44 adults with CF who underwent overnight polysomnography (PSG), Epworth Sleepiness Scale (ESS) assessment, and sinus computed tomography (CT). CRS severity was quantified using the Lund–Mackay score (LMS) and the main nasal cavity score (MNCS). OSA was defined by Apnea–Hypopnea Index (AHI) thresholds per American Academy of Sleep Medicine criteria. Results: Participants had a mean age of 31.1 ± 8.4 years and a mean percent predicted FEV1 of 51.8 ± 15.7. Sinus CT showed radiological evidence of CRS in all participants. Mean AHI was 5.3 ± 4.4/h; 48% had AHI ≥ 5/h. There were no significant differences between pwCF with and without OSA in age, sex, BMI, lung function, total sleep time, sleep efficiency, or ESS score (all p > 0.05). Mean LMS and MNCS did not differ between OSA and non-OSA groups (both p > 0.05), and neither score correlated with PSG parameters or ESS (all p > 0.05). Receiver operating characteristic (ROC) analysis demonstrated low discriminative ability of LMS and MNCS for predicting OSA (AUCs < 0.70, p < 0.05). Conclusions: In this cohort of adults with CF, CT-based CRS severity was not associated with OSA. Given the substantial prevalence of OSA observed, PSG screening should be considered irrespective of CRS severity. Full article

Each year, around 23 million miscarriages occur worldwide, which have a substantial emotional impact on parents, and impose significant societal costs. While medical care accounts for most expenses, work productivity loss contributes significantly. Addressing underlying causes of miscarriage could improve parents’ mental health and potentially their economic impact. In most countries, investigations into miscarriage causes are only recommended after recurrent cases, focusing mainly on maternal factors. Fetal and placental tissue are rarely examined, as current guidelines do not advise routine genetic analyses of pregnancy tissue, because the impact of further clinical decision making and individual prognosis is unclear. However, this leaves over 90% of all miscarriage cases unexplained and highlights the need for alternative methods. We therefore conducted a narrative review on genetic analysis, autopsy, and imaging of products of conception (POC). Karyotyping, QF-PCR, SNP array, and aCGH were reviewed in different research settings, with QF-PCR being the most cost-effective, while obtaining the highest technical success rate. Karyotyping, historically being considered the gold standard for POC examination, was the least promising. Post-mortem imaging techniques including post-mortem ultrasound (PMUS), ultra-high-field magnetic resonance imaging (UHF-MRI), and microfocus computed tomography (micro-CT) show promising diagnostic capabilities in miscarriages, with micro-CT achieving the highest cost-effective performance. In conclusion, current guidelines do not recommend diagnostic testing for most cases, leaving the majority unexplained. Although genetic and imaging techniques show promising diagnostic potential, they should not yet be implemented in routine clinical care and require thorough evaluation within research settings—assessing not only diagnostic and psychosocial outcomes but also economic implications. Full article

Objective: Mobile head CT enables bedside neuroimaging in critically ill patients, reducing risks associated with intrahospital transport. Despite increasing clinical use, evidence on dose optimization for mobile CT systems remains limited. This study evaluated whether an optimized CT protocol can reduce radiation exposure without compromising diagnostic image quality in neurointensive care unit patients. Methods: In this retrospective single-center study, twenty-two non-contrast head CT examinations were acquired with a second-generation mobile CT scanner between March and May 2023. Patients underwent either a default (group A, n = 14; volumetric computed tomography dose index (CTDI_vol) 44.1 mGy) or low-dose CT protocol (group B, n = 8; CTDI_vol 32.1 mGy). Regarding dosimetry analysis, we recorded dose length product (DLP) and effective dose (ED). Quantitative image quality was assessed by manually placing ROIs at the basal ganglia and cerebellar levels to determine signal, noise, signal-to-noise ratio, and contrast-to-noise ratio. Two neuroradiologists independently rated qualitative image quality using a four-point Likert scale. Statistical comparisons were performed using a significance threshold of 0.05. Results: Median DLP and ED were significantly lower for group B (592 mGy·cm, 1.12 mSv) than for group A (826 mGy·cm, 1.57 mSv; each p < 0.0001). Quantitative image quality parameters did not differ significantly between groups (p > 0.05). Qualitative image quality was rated excellent (median score 4). Conclusions: The optimized mobile head CT protocol achieved a 28.7% reduction in radiation exposure while maintaining high diagnostic image quality. These findings support the adoption of low-dose strategies in mobile CT imaging in line with established radiation protection standards. Full article

Background: CKDB-322, a combination of Lactiplantibacillus plantarum Q180 and Phaeodactylum tricornutum, has shown anti-obesity potential in preclinical models, although human evidence is still limited. This randomized, double-blind, placebo-controlled, 12-week trial evaluated the efficacy and safety of CKDB-322 in overweight adults. Methods: Participants were aged 19–65 years; had a body mass index (BMI) of 25–30 kg/m², and a waist circumference of ≥90 cm for men or ≥85 cm for women. They were randomly assigned to receive either CKDB-322, which provided 1.0 × 10⁹ CFU of L. plantarum Q180 and 200 mg of P. tricornutum daily (n = 50), or a placebo (n = 50). Results: CKDB-322 supplementation resulted in statistically significant reductions in body fat mass and body fat percentage, as measured by dual-energy X-ray absorptiometry (DEXA), compared to the placebo group (p < 0.05). Computed tomography (CT) analyses also revealed significant reductions in abdominal fat area in the CKDB-322 group (p < 0.05). Additional improvements were observed in body weight and anthropometric parameters. Among metabolic biomarkers, serum triglycerides and leptin levels decreased significantly in the CKDB-322 group compared to the placebo. Exploratory microbiome analyses indicated an increase in the relative abundance of Lactobacillus, suggesting potential modulation of the gut–adipose axis. CKDB-322 was well tolerated, with no clinically significant adverse events or laboratory abnormalities. Conclusions: Collectively, CKDB-322 demonstrated a favorable safety profile and produced statistically significant improvements in multiple adiposity-related outcomes, including reductions in body fat mass, abdominal adiposity, and key anthropometric measures, supporting its potential as a functional ingredient for body fat reduction and metabolic health. Full article

Persistent Idiopathic Dentoalveolar Pain (PIDAP) is a persistent idiopathic toothache that frequently remains unresponsive to medical therapy. Precise targeting of the infraorbital nerve is essential for successful intervention, yet anatomical variability often limits the consistency of conventional radiofrequency ablation (RFA). This report describes a medial-to-lateral ultrasound- and computed tomography-guided approach, intended to align with the natural orientation of the infraorbital canal and potentially enhance electrode–nerve contact. A 48-year-old woman with refractory maxillary incisor pain underwent RFA after only transient benefit from a diagnostic nerve block. Ultrasound enabled accurate identification of the infraorbital foramen and confirmed the canal’s medial-to-lateral course, which then guided CT-assisted needle advancement into the orbitomaxillary segment. The patient experienced immediate analgesia. Pain reduction was maintained at the one-month follow-up. At the two-month assessment, although a mild symptom rebound was observed, no procedure-related complications were noted. This trajectory-based medial-to-lateral technique offers an anatomically grounded alternative for infraorbital nerve RFA and may represent a valuable option for refractory PIDAP. Full article

Background/Objectives: Although cross-sectional muscle quality has shown prognostic relevance, the impact of temporal changes in muscle composition in septic shock has not been fully explored. This study aimed to investigate whether deterioration in muscle quality on serial computed tomography (CT) scans is associated with mortality in patients with septic shock. Methods: We conducted a retrospective single-center study using a prospectively collected registry of adult patients with septic shock between May 2016 and May 2022. Patients who underwent CT on the day of emergency department (ED) presentation and had a CT performed more than 180 days earlier were included. Muscle quality maps were generated and segmented based on CT attenuation values into normal-attenuation muscle area (NAMA), low-attenuation muscle area (LAMA), and intramuscular adipose tissue area. Differences between the ED and prior CT scans were also calculated. The primary outcome was the 28-day mortality. Results: Among the 768 enrolled patients, the 28-day mortality was 18.0%. Both survivors and non-survivors showed a significantly greater increase in LAMA (20.8 vs. 9.8 cm²) and a greater decrease in NAMA (−26.0 vs. −18.8 cm²). Multivariate analysis identified increased LAMA as an independent risk factor for 28-day mortality (adjusted OR 1.03; 95% CI: 1.01–1.04; p < 0.01). Conclusions: An increase in LAMA on serial CT scans was associated with higher short-term mortality in patients with septic shock, suggesting that temporal degradation of skeletal muscle quality may serve as a potential prognostic marker. Full article

The use of machine learning (ML) and deep learning (DL) in lung cancer detection and classification offers great promise for improving early diagnosis and reducing death rates. Despite major advances in research, there is still a significant gap between successful model development and clinical use. This review identifies the main obstacles preventing ML/DL tools from being adopted in real healthcare settings and suggests practical advice to tackle them. Using PRISMA guidelines, we examined over 100 studies published between 2022 and 2024, focusing on technical accuracy, clinical relevance, and ethical aspects. Most of the reviewed studies rely on computed tomography (CT) imaging, reflecting its dominant role in current lung cancer screening workflows. While many models achieve high performance on public datasets (e.g., >95% sensitivity on LUNA16), they often perform poorly on real clinical data due to issues like domain shift and bias, especially toward underrepresented groups. Promising solutions include federated learning for data privacy, synthetic data to support rare subtypes, and explainable AI to build trust. We also present a checklist to guide the development of clinically applicable tools, emphasizing generalizability, transparency, and workflow integration. The study recommends early collaboration between developers, clinicians, and policymakers to ensure practical adoption. Ultimately, for ML/DL solutions to gain clinical acceptance, they must be designed with healthcare professionals from the beginning. Full article