Citations

Abstract Background Evaluation of middle ear prostheses requires high-resolution imaging to assess position and structural integrity. Purpose To assess the diagnostic performance of photon-counting-detector (PCD)-CT for middle ear prosthesis imaging in cadaveric specimens. Materials and Methods Nine different middle ear prostheses were implanted in five fresh-frozen whole-head specimens, both in correct anatomic position and displaced to mimic malposition. Specimens were scanned on PCD-CT (NAEOTOM Alpha, Siemens) and energy-integrating-detector (EID)-CT (SOMATOM Force, Siemens), reconstructed at 0.2mm and 0.4mm, respectively. Eighteen cases (9 prostheses, 2 positions) per scanner were evaluated by two neuroradiologists (36 readings), blinded to scanner and position, who scored prosthesis position, reader confidence, and overall image quality. Agreement with the reference standard and between readers was compared. Results PCD-CT showed higher diagnostic confidence compared with EID-CT, while accuracy and inter-reader agreement did not demonstrate significant differences between scanners. Correct readings, PCD-CT versus EID-CT: lateral contact, 31/36 (86.1%; 95% CI: 58.3%-94.4%) vs. 29/36 (80.6%; 55.6%-91.7%), p = 0.500; medial contact, 30/36 (83.3%; 55.6%-94.4%) vs. 26/36 (72.2%; 50.0%-83.3%), p = 0.250. PCD-CT identified all cases of complete contact loss (score 3), while EID-CT missed one reading per contact. Inter-reader agreement may be higher with PCD-CT vs EID-CT (lateral: κw: 0.804 [0.578-0.941] vs. 0.571 [0.302-0.778], p = 0.250; medial: 0.684 [0.191-0.934] vs. 0.481 [0.024-0.835], p = 0.250). Reader confidence was significantly higher with PCD-CT; mean paired differences for Readers 1 and 2 were 9.4 (1.8-17.1; Holm-adjusted p = 0.019) and 23.1 points (13.4-32.7; p < 0.001) for lateral contact, and 16.4 (10.0-22.8; p < 0.001) and 17.8 points (6.8-28.8; p = 0.007) for medial contact. Readers preferred PCD-CT image quality in 16/18 (88.9%; 65.3%-98.6%) and 17/18 cases (94.4%; 72.7%-99.9%), respectively. Conclusion PCD-CT was associated with significantly higher reader confidence and preferred image quality, without significant differences in diagnostic accuracy or inter-reader agreement. These findings suggest PCD-CT is a promising modality for postoperative middle ear evaluations that warrants further investigations.

Abstract Objective. Four-dimensional CT (4DCT) enables visualization of dynamic wrist joint pathology but may be compromised by motion artifacts. Deep learning (DL)-assisted motion correction has the potential to enhance wrist 4DCT quality, but model development is hindered by scarce training data. To address this, we developed a model-based motion simulation framework that generated realistic 4DCT data with paired static ground truth images to demonstrate wrist motion correction using a physics-informed DL model trained with simulated data. Approach. Wrist motion was simulated by synchronizing rotational and translational movements of carpal bones and connective tissues. Elastic deformation guided by analytically-derived motion vector fields was applied to static wrist scans at neutral positions to create artifact-free images at multiple angular positions. Virtual motion-corrupted 4DCT images were produced via forward projection and reconstruction with synchronized gantry and wrist motion. Experimental data from 4DCT cadaver scans were collected to validate simulation quality. Low-intensity region score (LIRS), positivity, and entropy were used to assess simulated motion artifacts. A physics-informed motion correction network was adapted exclusively using simulated training data and subsequently deployed on simulated, real cadaveric, and in vivo 4DCT scans. LIRS and structural similarity index (SSIM) were used to evaluate motion correction quality. Wilcoxon signed-rank tests were conducted ( α = 0.05). Results. The simulation method created realistic motion artifacts compared to real scan reference ( p > 0.05). Motion artifacts were effectively mitigated across testing cases demonstrating generalizability. In simulated scans, LIRS improved from 0.55 ± 0.12–0.75 ± 0.04 and SSIM from 0.87 ± 0.08–0.95 ± 0.03 at bone regions (each p < 0.01). In experimental cadaveric scans, LIRS improved from 0.05 ± 0.01–0.31 ± 0.17 in marker regions and from 0.58 ± 0.19–0.77 ± 0.14 in bone regions (each p < 0.01). Significance. Our simulation method facilitates training DL-assisted motion correction algorithms by providing high-quality training data, effectively supporting 4DCT image quality enhancement.

Abstract Background Breast cancer is etiologically heterogeneous, but which risk factors differ in their associations across tumor subtypes remains unclear. We conducted a large, pooled analysis to evaluate independent, dose-response associations between breast cancer risk factors and quantitative tumor features. Methods Analyses of 15,731 invasive breast cancers from 24 studies evaluated associations (p-trend) between reproductive and hormonal factors, body mass index (BMI), alcohol, smoking, and family history in relation to quantitative immunohistochemistry measures on tissue microarrays (ER, PR, HER2, KI67, TP53) and tumor grade. Analyses in a subset of 10 population-based studies estimated subtype-specific odds ratios (ORs) comparing cases to controls. A Bayesian False Discovery Probability (BFDP) &lt;0.2 was used to identify associations with strong statistical evidence. Results Nulliparity and later age at menopause were associated with higher ER-positivity (p-trend=0.021 and 0.001, respectively), with corresponding OR[ER+] (95% CI) = 1.49 (1.16-1.90) for nulliparous vs. parous and 1.07 (1.03-1.11) per 5 years. Current combined menopausal hormone therapy (MHT) use was associated with lower grade (p-trend&lt;0.001), with OR [grade1] = 3.37 (2.69-4.21) for current vs. never users. Higher BMI was associated with lower ER-positivity and higher grade in premenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[ER+] = 0.80 (0.74-0.87) and OR[grade1] = 0.75 (0.63-0.88) per 5 units, and with higher PR-positivity and higher grade in postmenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[PR+] = 1.08 (1.03-1.14) and OR[grade 3] = 1.10 (1.04-1.17) per 5 units. Conclusion This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity. Associations with HER2, KI67 and TP53, or other risk factors did not meet our threshold for strong evidence.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (Aβ) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

Purpose of review Photon-counting detector computed tomography (PCD-CT) represents a major technological advancement in computed tomography, offering improved spatial resolution, reduced image noise, and increased dose efficiency compared with conventional energy-integrating detector CT (EID-CT). Because many clinically relevant structures within the temporal bone are submillimeter in size, the temporal bone represents an ideal region for determining whether these technical improvements translate into meaningful clinical benefits. This review evaluates the emerging literature on PCD-CT in temporal bone imaging and examines whether higher spatial resolution meaningfully impacts clinical decision-making. Recent findings Recent studies demonstrate improved visualization of middle ear anatomy, ossicular prostheses, cochlear implant electrode arrays, and superior semicircular canal dehiscence with PCD-CT compared with conventional CT. Improved spatial resolution and reduced metal artifact have been shown to enhance assessment of prosthesis positioning, cochlear duct measurements, and differentiation of true versus near semicircular canal dehiscence. However, given the recent availability of PCD-CT, most studies to date have focused on cadaveric studies, phantom studies, or patient studies evaluating image quality rather than diagnostic accuracy or clinical outcomes. Summary PCD-CT provides substantially improved visualization of submillimeter temporal bone structures and may improve diagnostic confidence in select clinical scenarios. Nevertheless, evidence demonstrating improved clinical outcomes remains limited, and further clinical studies are needed to define the role of PCD-CT in routine temporal bone imaging.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.