Tomography

Background/Objectives: Artificial intelligence (AI) can support lesion detection in gadolinium-based contrast agent-enhanced (GBCA-enhanced) breast MRI. However, its effectiveness on virtual contrast-enhanced (vCE) images remains unclear. This feasibility study evaluated the publicly available MAMA-MIA nnU-Net model trained on GBCA-enhanced data using an independent cohort of both GBCA-enhanced and vCE breast MRI. Methods: This IRB-approved retrospective study included the publicly available nnU-Net model trained on n = 1506 MAMA-MIA breast MRI scans and a cohort of n = 2126 in-house 3T breast MRI scans. A generative adversarial network (Pix2Pix-GAN) was developed on n = 1870 of the in-house scans and used to generate vCE data on the remaining independent n = 256 in-house cases. The MAMA-MIA nnU-net was applied to both GBCA-enhanced (GBCA) and corresponding vCE images. Ground-truth segmentations of malignant lesions served to calculate the Dice score, Hausdorff distance, and lesion dimension differences. Results: The final test set comprised n = 250 cases (n = 69 malignant, n = 181 benign). Lesion detection rates were 91% (n = 63/n = 69; 95% confidence interval (CI): 82.3–96.0%) for GBCA and 84% (n = 58/n = 69; 95% CI: 73.7–90.9%) for vCE. Two lesions missed in GBCA were identified by vCE. The Hausdorff distances were similar (GBCA: 6.4 (IQR: 3.2–9.3; 95% CI: 5.2–7.8) mm; vCE: 6.7 (IQR: 3.9–9.7; 95% CI: 5.3–8.0) mm, p = 0.564). The Dice scores showed minor differences (GBCA: 0.829 (IQR: 0.723–0.900; 95% CI: 0.786–0.865) vs. vCE: 0.826 (IQR: 0.720–0.857; 95% CI: 0.770–0.836); p < 0.001). vCE images had slightly higher non-target tissue segmentation (median 6072 mm³ vs. 5754 mm³). Conclusions: A GBCA-trained algorithm demonstrated some cross-domain transferability to vCE images, albeit with a reduced case-level sensitivity of 84% (95% CI: 73.7–90.9%) vs. 91% (95% CI: 82.3–96.0%). Based on these preliminary results, further research, including larger cohorts and more diverse datasets, is warranted. Full article

Background/Objectives: Differences among scanners and reconstruction methods may limit the comparability of quantitative metrics derived from fluorodeoxyglucose positron emission tomography (FDG PET)/computed tomography (CT). Although harmonization reduces inter-scanner variability in standardized uptake values (SUVs), its impact on the preservation of lesion-level ranking in real-world clinical datasets remains unclear. Here, we evaluated the robustness of PET quantitative metrics, particularly focusing on rank preservation after harmonization. Methods: Phantom and clinical data retrospectively acquired from three institutions using four PET/CT scanner types were analyzed. Harmonization parameters were derived from National Electrical Manufacturers Association IEC Body Phantom data, using the oldest scanner as the reference, and were directly applied to the clinical datasets. Clinical evaluation included head and neck malignant melanoma (HNMM; high FDG avidity) and adenoid cystic carcinoma (ACC; low FDG avidity). Rank preservation between pre- and post-harmonization values was assessed using Spearman’s rank correlation coefficient (ρ). Results: Phantom-based harmonization reduced inter-scanner differences and enabled consistent evaluation in clinical datasets (HNMM: 34 patients, 93 lesions; ACC: 18 patients, 38 lesions). SUVpeak demonstrated the highest rank preservation across tumor types and lesion sizes (ρ = 0.94–1.00). Metabolic tumor volume (MTV) showed a high rank correlation in HNMM with an absolute threshold (MTV2.5; ρ = 0.99), but robustness varied depending on threshold definition, tumor type, and lesion size. Tumor-to-liver ratios showed moderate rank preservation. Conclusions: Our results suggested that SUVpeak is the most robust preservation of lesion ranking across tumor types after harmonization, suggesting its suitability as a reliable imaging biomarker in multicenter studies. Meanwhile, careful standardization is needed when using MTV-based metrics for prognostic evaluation. Full article

Background and Objectives: Knee osteoarthritis (OA) is a prevalent musculoskeletal disorder. This study aims to assess the hip-knee-ankle anatomical alignment in patients with OA at different stages. Materials and Methods: Radiological images of 200 OA patients were analyzed to measure parameters such as femoral and tibial lengths (both anatomical and mechanical), abductor length, hip center length, femoral offset, collum femoris length, and shaft angle using ImageJ software (version 1.53k; National Institutes of Health, Bethesda, MD, USA). Results: The female-to-male ratio was 2.17:1. Male participants exhibited greater values for femoral and tibial lengths, abductor length, hip center length, femoral offset, collum femoris length, and collum femoris shaft angle, whereas females showed higher Q angle, hip–knee–ankle angle, mechanical lateral distal femoral angle, mechanical medial proximal tibial angle, and femoral angle measurements. Significant differences in both linear and angular parameters were observed among age groups. Q angle, hip–knee–ankle angle, femoral mechanical axis shaft angle, plateau angle, and ankle tilt angle values were significantly higher in individuals aged 62–75 years. According to Kellgren–Lawrence staging, significant increases were observed in Q angle, hip–knee–ankle angle, femoral mechanical axis shaft angle, plateau angle, and ankle tilt angle in advanced-stage disease (Stage 4) (p < 0.05). Although mechanical lateral distal femoral angle, femoral Q angle, tibial Q angle, condylar plateau angle, and tibiotalar angle showed numerical differences across disease stages, these findings did not reach statistical significance. Conclusions: Comprehensive assessment of lower-extremity alignment may provide complementary information regarding biomechanical changes associated with knee osteoarthritis progression and could support individualized clinical follow-up and treatment planning. Full article

Objectives: This study investigates the repeatability and reproducibility of radiomic features extracted from different energy levels of virtual monoenergetic reconstruction (VMER) and polyenergetic reconstruction (PER) obtained with photon-counting computed tomography (PCCT). Methods: Sixteen organic phantoms were scanned twice in a test–retest format using a 120 kV tube potential and tube currents of 10, 50, and 100 mAs. After rotating the phantoms 90° around their z-axis, additional test–retest scans were performed. A PER and 16 VMERs were generated. Segmentation and extraction of 105 original radiomic features followed. The repeatability and reproducibility of these features were assessed using the concordance correlation coefficient (CCC) for agreement and the intraclass correlation coefficient (ICC) for reliability, excluding 14 shape-based features from the analysis. Results: On average, 85 out of 91 radiomic features from VMER showed high repeatability. Approximately 30% of features demonstrated high intra-scan and inter-scan reproducibility when comparing PER and VMER. For different energy levels of VMER, around 78% showed high intra-scan reproducibility, and 74% showed high inter-scan reproducibility. Comparing the average values of test and retest scans in both the initial and rotated states revealed that 65% of features showed high agreement and 73% high reliability for PER, while for VMER, these values were 51% and 55%, respectively. Conclusions: Radiomic features from VMERs showed high test–retest repeatability, whereas reproducibility across reconstruction types and widely separated energy levels was more limited. These findings suggest that energy levels should be carefully standardized when radiomic features are extracted from PCCT-derived VMER images. Full article

Background: Posterior malleolar fractures are commonly assessed using two-dimensional measurements and morphology-based classification systems. However, ankle fracture morphology is inherently three-dimensional, and the reproducibility of CT-based volumetric segmentation for malleolar fracture fragments has not been sufficiently established. Objectives: This study aimed to evaluate the relationship between lateral and posterior malleolar fragment volumes in homogeneous supination–external rotation (SER)-type trimalleolar ankle fractures and to assess the intraobserver and interobserver reproducibility of a manual CT-based three-dimensional volumetric segmentation workflow. Methods: This retrospective musculoskeletal imaging study included 71 patients with SER-type trimalleolar ankle fractures who underwent preoperative computed tomography (CT). Posterior and lateral malleolar fracture fragments were segmented on thin-slice axial CT images using a standardized manual contour-based slice-by-slice workflow. Fragment volumes were calculated using dedicated volumetric imaging software. The association between lateral and posterior malleolar fragment volumes was assessed using Spearman correlation and multivariable linear regression analyses. Measurement reproducibility was evaluated using intraclass correlation coefficients for absolute agreement [ICC(A,1)] and Bland–Altman analyses. Results: The median lateral and posterior malleolar fragment volumes were 8.63 cm³ (interquartile range [IQR], 7.18–10.71) and 2.64 cm³ (IQR, 1.88–4.24), respectively. A weak but statistically significant positive correlation was observed between lateral and posterior malleolar fragment volumes (Spearman rho = 0.313, p = 0.008). In multivariable linear regression analysis, lateral malleolar fragment volume remained independently associated with posterior malleolar fragment volume after adjustment for age, sex, and body mass index (B = 0.316, standardized β = 0.39, p = 0.002). Intraobserver and interobserver reproducibility were excellent for all volumetric measurements, with ICC(A,1) values ranging from 0.996 to 0.999. Bland–Altman analyses demonstrated low mean bias and narrow limits of agreement across all comparisons. Geometric agreement was also excellent, with Dice similarity coefficient values ranging from 0.93 to 0.96 across intraobserver and interobserver segmentation comparisons. Full article

Purpose: To develop and technically validate a reproducible multicentre MRI radiomics workflow for pancreatic cyst risk stratification using paired T1- and T2-weighted imaging from public datasets. Methods: Public datasets were screened and Cyst-X was selected as the primary cohort because it contained pancreatic MRI, risk labels, masks and metadata. A linked Cyst-X subset was enriched with metadata, filtered to an exact paired T1/T2 cohort, and processed through image–mask quality control, 1.0 mm isotropic resampling, intensity normalisation, PyRadiomics feature extraction, feature reduction and patient-level centre-held-out validation. The revised modelling strategy used a T2 + clinical all-patient primary analysis (n = 409) and a complete-case paired T1/T2 sensitivity analysis (n = 299). Results: The final cohort comprised 409 patients and 818 image-level rows across EMC, IU, MCF and NYU. All 818 image–mask pairs passed post-preprocessing QC. T2 radiomics were complete for all 409 patients; however, 110 T1 feature sets were missing, all from MCF. In the all-patient T2 + clinical model comparison, logistic regression achieved the highest macro-AUC (0.737). The T2 + clinical random forest comparator achieved macro-AUC 0.716 (95% CI 0.678–0.755), accuracy 0.545 (95% CI 0.496–0.592) and macro-F1 0.530 (95% CI 0.481–0.577). The paired T1/T2 complete-case random forest sensitivity model achieved macro-AUC 0.735 (95% CI 0.691–0.777), accuracy 0.575 (95% CI 0.520–0.632) and macro-F1 0.554 (95% CI 0.494–0.605). Conclusion: This study demonstrates the feasibility of constructing a reproducible public data MRI radiomics workflow for pancreatic cyst risk stratification. Model performance was modest, and independent external validation is required before clinical application. Full article

Objective: The objective of this study was to investigate the relationship between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters in soft tissue tumors (STTs). Methods: This retrospective study included patients with histopathologically confirmed STTs who underwent both DCE-MRI and IVIM-DWI between March 2022 and February 2024. Patients with prior therapy and lipomatous tumors were excluded. DCE-MRI parameters (K_trans, K_ep, V_e, iAUC) were obtained from pharmacokinetic maps using manually placed regions of interest (ROIs) in the most perfused tumor areas, avoiding necrotic and cystic regions. Corresponding ROIs were applied to IVIM-DWI maps. IVIM parameters (D, D*, f) were calculated using 11 b-values. Results: Twenty-nine patients (mean age, 56 ± 18 years; 14 malignant, 15 benign) were included. Interobserver agreement was excellent for DCE-MRI parameters, whereas IVIM-DWI parameters showed moderate-to-good agreement, with D showing the lowest reproducibility. In malignant tumors, f demonstrated strong positive correlations with K_trans (r = 0.81, p < 0.001) and iAUC (r = 0.79, p < 0.001), both of which remained significant after correction for multiple comparisons. fD* was higher in malignant than in benign lesions in the unadjusted group comparison; however, diagnostic performance was not evaluated in the present study. No significant differences were observed for DCE-MRI parameters between benign and malignant tumors. Conclusions: IVIM-DWI parameters demonstrated associations with DCE-MRI metrics in malignant STTs and may provide complementary information regarding tumor perfusion. However, the findings should be interpreted cautiously because ROI analysis was limited to a single representative slice. Further validation using larger cohorts and volumetric tumor assessment is required. Full article

Background/Objectives: To evaluate whether day-of-procedure ambient ozone exposure is associated with pneumothorax after CT-guided lung biopsy. Methods: This retrospective single-centre study included 160 CT-guided lung biopsies performed between January 2018 and February 2026. Environmental data from the day of biopsy were assigned from the nearest national monitoring station. The primary outcome was any pneumothorax on post-biopsy CT; the secondary outcome was drainage-requiring pneumothorax. Multivariable logistic regression included ozone exposure, emphysema, and access route through dependent lung area (ARDA). Ozone was analysed as a continuous variable per 10 μg/m³ and, exploratorily, using a ROC-derived threshold of ≥75.8 μg/m³. Restricted cubic splines assessed nonlinearity. Sensitivity models adjusted for needle size, biopsy system, operator identity, and season. Drainage-requiring pneumothorax was analysed using Firth logistic regression. Results: Pneumothorax occurred after 86 of 160 biopsies (53.8%), and 13 biopsies (8.1%) required drainage. Ozone was not associated with pneumothorax when modelled linearly (OR, 1.09 per 10 μg/m³; 95% CI, 0.97–1.23; p = 0.167). In exploratory threshold modelling, ozone ≥ 75.8 μg/m³ was associated with pneumothorax (OR, 2.76; 95% CI, 1.39–5.61; p = 0.004). Emphysema increased pneumothorax odds (OR, 2.16; 95% CI, 1.03–4.68; p = 0.047), whereas ARDA was protective (OR, 0.23; 95% CI, 0.11–0.45; p < 0.001). Spline analysis supported nonlinearity (p = 0.001). For drainage-requiring pneumothorax, only emphysema was significant. Conclusions: Ambient ozone showed an exploratory nonlinear association with pneumothorax after CT-guided lung biopsy, with a threshold signal around 70–80 μg/m³. ARDA was protective, whereas emphysema was associated with drainage-requiring pneumothorax. Full article

Aim: This study aimed to evaluate the impact of cone beam computed tomography (CBCT) on preoperative surgical decision-making and risk assessment for mandibular third molar (MM3) extractions in cases identified as high-risk by orthopantomography (OPG). Materials and Methods: This prospective observational diagnostic study utilized the purposive sampling method, recruiting 50 MM3s from 33 patients (21 females, 12 males; mean age 24.24 ± 6.77 years, range 16–42). Samples were categorized into five distinct radiographic groups based on the proximity of roots to the inferior alveolar nerve (IAN) on OPG. The methodology involved a comparative 3D analysis to determine neurovascular contact, spatial orientation, and the presence of a cortical border. Surgical strategies, specifically the necessity for coronectomy or the lingual split technique, were reassessed following 3D evaluation. Postoperative neurosensory outcomes were recorded. Statistical analysis was performed using the Fisher–Freeman–Halton and Kruskal–Wallis tests. Results: CBCT identified direct IAC contact in 74% of the cases. In 18% of the cases initially deemed high-risk by OPG, CBCT revealed a safe distance, thereby altering the surgical approach. Tooth angulation (p = 0.012) and Pell and Gregory classification (p = 0.024) were significant predictors of contact. Temporary neurosensory disturbances occurred in 4% (n = 2) of the sample, specifically in cases where CBCT had confirmed the loss of nerve canal cortication. Conclusions: In accordance with the study aim, CBCT provides essential 3D data that refines surgical planning in nearly one-fifth of high-risk cases. The findings justify selective CBCT use, guided by the ALADA principle, to minimize iatrogenic injury. Full article

Background/Objectives: Reliable uncertainty quantification (UQ) is a prerequisite for deploying automated systems in safety-critical medical imaging workflows, yet existing approaches either sacrifice computational efficiency or provide poorly calibrated confidence estimates. We present UQ-Mamba, a lightweight architecture that embeds uncertainty quantification natively into a Mamba state space model via linearized error propagation. Methods: UQ-Mamba yields per-prediction approximate epistemic and aleatoric uncertainty estimates in a single deterministic forward pass at only 9.5% additional inference overhead. We note that these components are heuristic approximations derived under three explicit assumptions (diagonal covariance, first-order linearization, and scalar mean-activation reduction) and have not been empirically validated as true Bayesian posteriors. By propagating learnable log-variance parameters through the SSM state transition matrix, UQ-Mamba bridges the gap between parameter efficiency and principled calibration without requiring stochastic sampling or multiple forward passes. Results: Evaluated across four medical imaging modalities—CT organ classification, colorectal histopathology, dermoscopy, and chest radiography—UQ-Mamba achieves 89.71% accuracy with ECE = 0.0217 on OrganMNIST using only 466K parameters ($3.3\times$ lower ECE than ResNet-50 at $50\times$ fewer parameters; note that UQ-Mamba optimizes NLL, whereas ResNet-50 uses standard cross-entropy, which is a confounding factor in the ECE comparison), improves the Mamba baseline by 2.42 percentage points on PathMNIST (ECE = 0.1188 after temperature scaling), achieves 68.88% test accuracy with ECE = 0.0597 on HAM10000 dermoscopy (matching EfficientNet-B0 at $9\times$ fewer parameters), and reaches mAUC = 0.8196 on CheXpert chest radiographs. Conclusions: Ablation studies confirm that the SSM propagation mechanism is necessary for meaningful uncertainty decomposition. These results establish uncertainty-aware SSMs as a promising proof-of-concept direction for calibrated, parameter-efficient medical image classification, with potential relevance to resource-constrained deployment settings pending further clinical validation. Full article

Background/Objectives: Virtual Bronchoscopic Navigation is used to guide bronchoscopes toward peripheral pulmonary lesions, but broad clinical and research adoption remains limited by the cost of proprietary software and by segmentation failures in small distal airways that can interrupt path planning. This study presents Virtual Bronchoscopic Pathfinder, an open-source, web-based system designed to provide automated airway segmentation, robust path generation, and browser-based three-dimensional visualization. Methods: The system integrates five components: a connectivity-aware deep learning model for pulmonary airway segmentation using Connectivity-Aware Surrogate and Local-Sensitive Distance modules; TotalSegmentator for automated tumor localization; a topology-preserving three-dimensional thinning algorithm implemented in C++ for centerline extraction; a bidirectional Dijkstra algorithm operating on a three-tier anatomical cost field with centerline, airway lumen, and parenchymal costs; and a zero-footprint visualization interface built on vtk.js with synchronized axial viewing and interactive volume rendering. VBP was validated on 306 thin-section CT series from 154 subjects in the public Lung-PET-CT-Dx dataset. Results: Among the 306 CT series, 33 series (10.8%) were excluded because of scanner-specific segmentation artifacts. In the remaining 273 anatomically valid series, the system successfully generated complete end-to-end navigation paths for all cases. The overall pipeline success rate was therefore 273 of 306 series (89.2%). The web-based interface was also confirmed to operate without client-side installation across desktop, laptop, and mobile device configurations. Conclusions: Virtual Bronchoscopic Pathfinder demonstrates that a reliable and accessible virtual bronchoscopic navigation workflow can be constructed entirely from open-source components. By combining connectivity-aware segmentation, cost-field path planning, and browser-based visualization, the system provides a practical foundation for imaging informatics research and future development of intra-procedural bronchoscopic guidance. Full article

Background: Patients with diabetes mellitus and nonobstructive coronary artery disease (NOCAD) may remain at increased cardiovascular risk despite the absence of flow-limiting stenosis. Quantitative coronary CT angiography (CCTA) enables comprehensive assessment of anatomical, functional, and inflammatory imaging biomarkers beyond luminal stenosis. This study aimed to evaluate the prognostic value of an automated multiparametric CCTA-derived imaging framework for risk stratification in patients with NOCAD, with exploratory assessment in those with diabetes mellitus. Methods: This retrospective single-center study included 485 patients with NOCAD who underwent CCTA between January 2020 and December 2021. Automated CCTA analysis was performed to quantify plaque burden, high-risk plaque features, CT-derived fractional flow reserve (CT-FFR), and perivascular fat attenuation index. The primary endpoint was major adverse cardiovascular events (MACE) during follow-up. Prognostic associations were assessed using Kaplan–Meier analysis, Cox regression, and hierarchical models. Results: During a median follow-up of approximately three years, MACE occurred in 56 patients. Patients with diabetes had a higher event rate than those without diabetes. Increased plaque burden, high-risk plaque features, elevated perivascular fat attenuation index, and reduced CT-FFR were associated with adverse outcomes. The fully integrated model combining anatomical, functional, and inflammatory CCTA-derived biomarkers improved risk stratification compared with plaque-based assessment alone. Conclusions: Automated multiparametric CCTA phenotyping may provide complementary prognostic information for risk stratification in patients with NOCAD. The diabetes-specific findings should be considered exploratory and require validation in larger prospective cohorts. Full article

Background: Multimodal breast ultrasound, including B-mode imaging, color Doppler flow imaging, and elastography, provides complementary information for lesion characterization. However, effectively integrating heterogeneous modalities remains challenging due to inconsistent feature distributions, limited cross-modal interaction, computational cost in existing methods, and sensitivity to noise and missing data. Methods: We presented an efficient Cross-Modal Interaction and Dynamic Fusion Network (CIDFNet) for multimodal breast ultrasound analysis. The framework integrates a multi-scale feature enhancement module to improve modality-specific representations, a cross-modal interaction module to enable early-stage feature exchange across modalities, and a dynamic fusion strategy to adaptively combine modality information based on feature reliability estimation. In addition, an invertible neural network is incorporated to reconstruct missing modality features during training. Results: Experiments on an internal dataset of 248 patients with 1532 images show that CIDFNet obtains an AUC of 85.69%, accuracy of 75.51%, recall of 50.00%, F1-score of 62.50%, and precision of 83.33%, while requiring 49.51 M parameters and 79.79 G FLOPs, respectively. Under a simplified Gaussian noise perturbation setting, performance degradation is observed. Conclusions: CIDFNet presents a framework for multimodal breast ultrasound analysis that reflects a trade-off between performance and computational efficiency. Full article

Background: Spinocerebellar ataxia type 2 (SCA2) is an inherited neurodegenerative disorder characterized by progressive cerebellar degeneration. One difficulty in treating this disease lies in identifying preclinical carriers: individuals who carry the pathogenic ATXN2 mutation but remain asymptomatic with respect to motor manifestations. Though magnetic resonance imaging (MRI) has proven valuable in supporting the diagnosis of ataxia, traditional univariate approaches using linear measurements have shown limited ability to capture the complex anatomical changes that occur across the disease spectrum, particularly during the preclinical phase. Methods: This study employed a comprehensive multivariate approach to improve the classification of individuals across the SCA2 spectrum. We developed a multinomial logistic regression model incorporating multiple linear measurements derived from magnetic resonance imaging to discriminate between healthy controls (n = 72), preclinical carriers (n = 17), and patients with manifest SCA2 (n = 61). To mitigate inherent class imbalance, particularly in the smaller preclinical subgroup, we implemented the Synthetic Minority Over-sampling Technique (SMOTE), generating a balanced dataset that enhances the model’s ability to discern the distinctive anatomical features. This was compared to the model applied to the unbalanced data. An improvement was observed when applying SMOTE. Results: The multivariate model demonstrated discriminatory performance, achieving an overall accuracy of 80.7%. The ability to identify healthy controls (AUC: 0.96), preclinical individuals (AUC: 0.75), and clinical individuals (AUC: 95%). This represents an advance over previous univariate approaches, which have had difficulty capturing the neurodegenerative changes characteristic of the preclinical stage. Conclusions: By integrating multiple neuroimaging biomarkers into a multivariable model, this study provides a tool for early identification of preclinical SCA2 carriers. The ability to accurately classify these individuals opens an opportunity for early therapeutic intervention before irreversible neurological deterioration occurs. This approach shows promise for optimizing clinical trial design and personalized care in SCA2. Full article

Purpose: To investigate the agreement on perfusion parameters derived from two different commercially available solutions for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with prostate cancer (PCa). Methods: A total of 50 patients (mean age, 71.6; range 56–86) who had undergone radical prostatectomy between December 2021 and September 2022 were included in this retrospective study. All patients had undergone DCE-MRI on a single 3T-MR scanner. Tumor segmentation on MR images was performed by two radiologists in consensus after radiologic-pathologic correlation using topographic maps as a reference standard. Subsequently, four perfusion parameters were calculated by dedicated commercially available solutions from two different vendors. Both solutions adopted a population-based arterial input function and an extended Tofts model as the pharmacokinetic model. The perfusion parameters were as follows; volume transfer constant (K^trans), rate constant (k_ep), volume fraction of extravascular extracellular space (v_e), and volume fraction of plasma (v_p). The differences between paired measurements were compared by Bland–Altman analyses and the reproducibility was evaluated using the intraclass correlation coefficient (ICC). Results: The study population consisted of Gleason score (GS) 6 (n = 12), GS 7 (n = 34), GS 8 (n = 1), and GS 9 (n = 3). Significant differences were found for all parameters (p < 0.0001). Mean differences were as follows: K^trans, −0.2102 (95% confidence interval; −0.2687 to −0.1518); k_ep, −0.7632 (−0.9005 to −0.6258); v_e, −0.1507 (−0.2422 to −0.05907); v_p, −0.02929 (−0.03383 to −0.02476). ICCs for average measures were as follows: K^trans, 0.2989 (−0.2355 to 0.6021); k_ep, 0.6883 (0.4507 to 0.8231); v_e, −0.1331 (−0.9967 to 0.3570); v_p, 0.2653 (−0.3106 to 0.5881). Conclusion: All perfusion parameters were significantly different between the two solutions. Therefore, comparison of perfusion parameters across different solutions is not recommended. Full article

Background/Objectives: Left ventricular (LV) geometry reflects structural adaptation to aging and biological sex. While cardiac magnetic resonance (CMR) provides precise morphologic assessment, most prior studies have focused on volumetric and mass-based parameters rather than routinely reported linear indices. This study aimed to evaluate the influence of age and sex on LV geometry using wall thickness, LV end-diastolic diameter (LVEDD), and proportional indices derived from standard CMR reports. Methods: In this retrospective cross-sectional study, 95 adult patients who underwent clinically indicated CMR were included. LV wall thickness, LVEDD, relative wall thickness (RWT), and wall thickness-to- LVEDD ratio (WT/LVEDD) were recorded. Participants were stratified by sex and age groups (18–40, 41–60, >60 years). Group comparisons, correlation analysis, multivariable linear regression, logistic regression, and Age × Sex interaction testing were performed to evaluate independent associated parameters of LV morphology and concentric remodeling. Results: The mean age was 34.94 ± 16.00 years; 60.0% were male. Males had significantly larger LVED (43.12 ± 6.83 mm vs. 39.76 ± 6.11 mm, p = 0.014) and greater wall thickness measurements (p < 0.05 for septal and posterior wall thickness). Age showed a significant positive correlation with mean LV wall thickness (r = 0.275, p = 0.007) and WT/LVEDD ratio (r = 0.241, p = 0.019), but not with LVEDD (p = 0.414). In multivariable analysis, male sex was independently associated with larger LVED (B = 3.345, p = 0.017), whereas age was independently associated with WT/LVEDD ratio (B = 0.0018, p = 0.019). Logistic regression demonstrated that age independently increased the odds of concentric remodeling (OR = 1.041 per year, 95% CI: 1.011–1.072, p = 0.006). No significant Age × Sex interaction was observed. Conclusions: Advancing age was independently associated with proportional LV geometric remodeling, whereas male sex primarily influenced absolute ventricular dimensions. Routine CMR report-derived linear measurements were sufficient to detect these distinct structural patterns. These findings highlighted the feasibility of using standardized morphologic indices in daily clinical practice to identify early age-related concentric remodeling. Full article

Objectives: To evaluate the diagnostic performance of automated vertebral trabecular Hounsfield unit (HU) measurements derived from routine fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) for identifying low bone density. Methods: This retrospective study included 131 consecutive women (mean age, 53.5 ± 9.6 years) undergoing health screening with FDG PET/CT and dual-energy X-ray absorptiometry (DXA) between January 2020 and December 2024. A deep learning-based model (TotalSegmentator) automatically segmented the lumbar vertebrae (L1–L4). HU-based metrics in trabecular regions were calculated, and their correlations with DXA-derived bone mineral density (BMD) were assessed. Diagnostic performance was evaluated using receiver operating characteristic analysis. A multivariable logistic regression model incorporating mean HU, age, and body mass index was developed and internally validated using bootstrap resampling. Results: According to WHO criteria, 47 of 131 participants (35.9%) had low bone density. Mean HU demonstrated strong diagnostic performance (area under the curve [95% confidence interval]: L1, 0.861 [0.800–0.923]; L2, 0.852 [0.788–0.915]; L3, 0.861 [0.800–0.921]; L4, 0.845 [0.781–0.909]). L1 mean HU provided the most balanced performance (sensitivity, 0.851; specificity, 0.750); L3 mean HU was slightly inferior. L1 mean HU was strongly correlated with BMD (r = 0.821, p < 0.001). In multivariable analysis, mean HU independently predicted low bone density (odds ratio: 0.949, p < 0.001). The model achieved an accuracy of 0.786 and demonstrated favorable calibration performance. Conclusions: The automated assessment of vertebral trabecular HU from routine FDG PET/CT provides a reliable and highly efficient method for screening low bone density without additional radiation exposure or cost. Full article

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation. Full article

Background/Objectives: Apparent diffusion coefficient (ADC) calculated from diffusion-weighted MRI (DWI) can predict tumor response to neoadjuvant chemotherapy for breast cancer. However, obtaining consistently adequate image quality in breast DWI can be challenging, and the effect of image quality on ADC’s predictive performance is unclear. The objective of this study was to evaluate inter-reader variability in image quality assessment and the effect of DWI image quality on the predictive performance of ADC. Methods: This multi-institutional study included 428 patients. Two readers assessed three DWI image quality factors—fat suppression, artifacts, and signal-to-noise ratio (SNR). Inter-reader agreement was estimated using Fleiss’ Kappa. The percent change in tumor ADC from pretreatment (T0) to early treatment (T1) was used to predict pathologic complete response (pCR), assessed at surgery. Results: Out of 428 patients, 134 were excluded (missing pCR [n = 17]; missing/incorrect DWI [n = 23]; inability to define region-of-interest [ROI, n = 94]) and 294 were included in the analysis. Kappa coefficients were estimated as: 0.47 (95% confidence interval [CI]: 0.42, 0.52) for fat suppression, 0.54 (0.50, 0.59) for artifact, and 0.38 (0.32, 0.44) for SNR. The AUC of ADC calculated from DWI with adequate (high or medium at both time points) image quality was 0.61 (95% CI: 0.52, 0.702), while it was 0.68 (95% CI: 0.53, 0.83) from DWI with inadequate image quality at either T0 or T1. The p-value for the difference in AUCs was 0.45. Conclusions: The inter-reader agreement was moderate to fair across all three quality categories. When a manually delineated tumor ROI was possible, no statistically significant difference in ADC predictive performance was observed between the quality-adequate and quality-inadequate cohorts; still, both were predictive of pCR. Furthermore, no statistically significant differences were observed in inter-reader agreement or ADC predictive performance between 1.5T and 3T scanners. These findings are clinically relevant to the use of ADC as an imaging biomarker in real-world conditions. Full article