Tomography, Volume 11, Issue 11 (November 2025) – 9 articles

This review focuses on the diverse etiologies of secondary spontaneous pneumothorax (SSP) and the crucial role of imaging in their diagnosis. Unlike primary spontaneous pneumothorax (PSP), which is typically due to ruptured blebs, SSP results from a wide array of underlying pulmonary conditions that can pose significant diagnostic challenges. These include infections like tuberculosis, airway diseases such as chronic obstructive pulmonary disease, malignancies (primary and metastatic), interstitial lung diseases like sarcoidosis, cystic lung diseases such as lymphangioleiomyomatosis, and connective tissue disorders. In women, catamenial pneumothorax secondary to endometriosis should be considered. The role of radiologists is crucial in uncovering these underlying conditions. While chest radiography is the initial imaging modality, computed tomography (CT) provides superior sensitivity for detecting subtle parenchymal abnormalities. Advanced techniques like photon-counting detector CT offer further benefits, including enhanced spatial resolution, reduced noise, and lower radiation dose, potentially revealing underlying causes that might be missed with conventional CT. This enhanced visualization of subtle parenchymal changes, small airways, and vascular structures can be the key to diagnosing the underlying cause of pneumothorax. Recognizing the diverse etiologies of SSP and utilizing advanced imaging techniques is paramount for accurate diagnosis, appropriate management, and improved patient outcomes. Full article

Purpose: To compare retinal thickness measurements obtained with the Optos Monaco and Heidelberg Spectralis optical coherence tomography (OCT) systems across 9 Early Treatment Diabetic Retinopathy Study (ETDRS) sectors in a cohort comprising normal eyes. Methods: Paired OCT scans from 64 eyes of 32 participants with normal retinal findings were acquired on both devices. Thickness measurements were obtained for the central subfield and the inner and outer sectors of the superior, nasal, inferior, and temporal quadrants. Outcomes included mean thickness, mean interdevice difference (Heidelberg minus Monaco), Pearson correlation coefficients, and Bland–Altman analyses. Scatterplots and Bland–Altman plots were constructed to evaluate agreement and assess potential interchangeability. Results: The Heidelberg Spectralis yielded significantly greater retinal thickness values than the Optos Monaco in all ETDRS sectors (p < 0.001), with mean differences ranging from +16.9 µm (outer superior) to +26.8 µm (inner superior). Pearson correlation coefficients indicated strong positive agreement (r ≥ 0.8) for the central subfield and most inner sectors, and moderate to strong positive agreement (r ≥ 0.5) in a single outer sector. Bland–Altman analyses demonstrated a statistically significant systematic bias favoring greater measurements with Heidelberg in most quadrants, with limits of agreement indicating clinically relevant variability. Although the relative agreement was high, absolute differences limit direct interchangeability. Conclusions: Optos Monaco and Heidelberg Spectralis exhibit strong linear correlation in retinal thickness measurements but show significant systematic differences. Interchangeable use requires the application of correction factors where segmentation variability may be greater. Full article

Objectives: Kidneytumors, particularly renal cell carcinoma (RCC), represent a critical public health concern due to their prevalence and the severe consequences of late diagnosis. Traditional diagnostic techniques, though widely used, are often limited by human error, inter-observer variability, and delayed recognition of malignant subtypes, underscoring the urgent need for automated, accurate, and reproducible solutions. Methods: To address these challenges, this study introduces a hierarchical, AI-driven framework for early detection and precise classification of kidney tumors from CT scans. At its core, the framework uses a specialized encoder, RAD-DINO-MAIRA-2, to extract highly discriminative imaging features, which are subsequently processed through multiple machine learning classifiers tailored to distinct hierarchical levels of diagnosis. Results: Using benchmark kidney tumor datasets, the framework was rigorously validated across 25 independent trials. Performance was assessed using accuracy, reproducibility, and robustness metrics, with results revealing a maximum accuracy of 98.29% and a mean accuracy of 94.72%. Notably, the Gaussian Process classifier achieved perfect performance in tumor type classification, while the MLP classifier attained flawless results in malignant subtype differentiation. Comparative analyses demonstrate that our hierarchical approach outperforms conventional DL-based pipelines by reducing sensitivity to dataset variability and providing a clinically viable path for integration into diagnostic workflows. Combining state-of-the-art feature extraction with hierarchical classification, the proposed framework delivers a robust and interpretable tool with substantial promise for improving patient outcomes in real-world clinical practice. Full article

Background: Pneumothorax (PTX) requires rapid recognition in emergency and critical care. Lung ultrasound (LUS) offers a fast, radiation-free alternative to computed tomography (CT), but its accuracy is limited by operator dependence. Artificial intelligence (AI) may standardize interpretation and improve performance. Methods: This retrospective single-center study included 46 patients (23 with CT-confirmed PTX and 23 controls). Sixty B-mode and M-mode frames per patient were extracted using a Clarius C3 HD3 wireless device, yielding 2760 images. CT served as the diagnostic reference. Experimental studies were conducted within the framework of three scenarios. Transformer-based models, Vision Transformer (ViT) and DINOv2, were trained and tested under two scenarios: random frame split and patient-level split. Also, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were trained on the feature maps extracted by using Video Vision Transformer (ViViT) for ultrasound video sequences in Scenario 3. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve (AUC). Results: Both transformers achieved high diagnostic accuracy, with B-mode images outperforming M-mode inputs in the first two scenarios. In Scenario 1, ViT reached 99.1% accuracy, while DINOv2 achieved 97.3%. In Scenario 2, which avoided data leakage, DINOv2 performed best in the B-mode region (90% accuracy, 80% sensitivity, 100% specificity, F1-score 88.9%). ROC analysis confirmed strong discriminative ability, with AUC values of 0.973 for DINOv2 and 0.964 for ViT on B-mode images. Also, both RF and XGBoost classifiers trained on the ViViT feature maps reached 90% accuracy on the video sequences. Conclusions: AI-assisted LUS substantially improves PTX detection, with transformers—particularly DINOv2—achieving near-expert accuracy. Larger multicenter datasets are required for validation and clinical integration. Full article

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors. Full article

Background/Objectives: Optimization of pediatric head computed tomography (CT) protocols is essential to minimize radiation exposure while maintaining diagnostic image quality. Previous studies mainly relied on phantom-based measurements or visual assessments, and validation using clinical images remains limited. This study aimed to establish quantitative thresholds for noise and contrast-to-noise ratio (CNR) in pediatric head CT by integrating multicenter clinical data with phantom evaluations. Methods: A multicenter retrospective study was conducted using CT systems from eight hospitals, combined with Catphan phantom experiments and pediatric head CT data. Scan parameters, automatic exposure control settings, and reconstruction methods were collected. Image quality was quantified by the standard deviation (SD) of noise and CNR obtained from regions of interest in gray and white matter. Radiation dose was represented by CTDIvol. Relationships among CTDIvol, SD, and CNR were analyzed across scanners from three manufacturers (Canon, FUJI, and GE). Results: Consistent dose–response trends were observed across institutions and manufacturers. Image noise decreased as CTDIvol increased, but reached a plateau at higher doses. CNR improved with dose escalation, then stabilized. Both phantom experiments and clinical analyses identified a target SD of 5 and CNR of 2 as optimal indicators for pediatric head CT. Conclusions: Quantitative thresholds were determined as practical indicators for balancing diagnostic image quality with dose reduction. Further reduction may be achieved through advanced reconstruction methods, such as deep learning-based algorithms. These findings may contribute to standardizing pediatric head CT protocols and supporting safer and more effective diagnostic imaging. Full article

Objective: This study aims to assess the prevalence of clinically significant incidental findings as well as incidental findings of minor clinical significance in multiparametric MRI (mpMRI) of the prostate. Materials and Methods: A retrospective analysis was conducted on 607 male patients (mean age: 72 years) who underwent prostate MRI between 2018 and 2023 at a single center. Two radiologists reviewed in consensus the scans for incidental findings during multiparametric MRI of the prostate. The findings were classified according to their clinical relevance, organ group and patient age. Results: Among 607 male patients (mean age: 72 years), 665 incidental findings were identified in 410 patients (67.5%; 95% CI 63.7–71.1). This corresponds to an average of 1.10 incidental findings per patient across the entire cohort. Of the 665 findings, 12 (1.8%; 95% CI 0.9–3.1) were classified as clinically significant. These included cases of sarcoma, rectal carcinoma, hydronephrosis, aortic aneurysm, avascular necrosis of the femoral head and high-grade disc protrusion with spinal canal stenosis and diverticulitis. Conclusions: Our data indicate that incidental findings are common in prostate mpMRI examinations; however, only a small proportion are clinically significant. This underscores the need for awareness of such findings, while avoiding unnecessary follow-up for those without clinical relevance. Full article

Background/Objectives: Cerebral blood flow (CBF) and cerebral blood volume (CBV) are critical perfusion metrics in diagnosing ischemic stroke. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the evaluation of these cerebral perfusion metrics; however, accurately assessing them remains challenging. This study aimed to: (1) assess CBF asymmetry by quantifying and comparing it between contralateral hemispheres (right vs. left) within the MCA, ACA, and PCA territories using paired t-tests, and describe pattern of CBV; (2) evaluate overall inter-territorial regional variations in CBF across the different cerebral arterial territories (MCA, ACA, PCA), irrespective of the hemisphere, using ANOVA; (3) determine the correlation between CBF and CBV using both Pearson’s and Spearman’s correlation analyses; and (4) assess the influence of age and gender on CBF using multiple regression analysis. Methods: A cross-sectional study of 55 ischemic stroke patients was conducted. DCE-MRI was used to measure CBF and CBV. Paired t-tests compared contralateral hemispheric CBF in MCA, PCA, and ACA, one-way ANOVA assessed overall inter-territorial CBF variations, correlation analyses (Pearson/Spearman) evaluated the CBF-CBV relationship, and linear regression modeled demographic effects. Results: Significant contralateral asymmetries in CBF were observed for each cerebral pair of cerebral arteries using a paired t-test, with descriptive asymmetries noted in CBV. Separately, ANOVA revealed significant overall variability in CBF between the different cerebral arteries, irrespective of hemisphere. A strong positive correlation was found between CBF and CBV (Pearson r = 0.976; Spearman r = 0.928), with multiple regression analysis identifying age and gender as significant predictors of CBF. Conclusions: This study highlights hemispheric asymmetry and inter-territorial variation, the impact of age, and gender on CBF. DCE-MRI provides perfusion metrics that can guide individualized stroke treatment, offering valuable insights for therapeutic planning, particularly in resource-limited settings. Full article