The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. Correlations between BCA data and demographic/clinical parameters were investigated. BCA-endotypes were identified and survival rates were compared between BCA-derived patient clusters. Patients with high-risk cytogenetics exhibited elevated cardiac marker index values. Across Revised-International Staging System (R-ISS) categories, BCA parameters did not show significant differences. However, both subcutaneous and total adipose tissue volumes were significantly lower in patients with progressive disease or death during follow-up compared to patients without progression. Cluster analysis revealed two distinct BCA-endotypes, with one group displaying significantly better survival. Furthermore, a combined model composed of clinical parameters and BCA data demonstrated a higher predictive capability for disease progression compared to models based solely on high-risk cytogenetics or R-ISS. These findings underscore the potential of BCA to improve patient stratification and refining prognostic models in MM.

To compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. Multi-reader study included 74 consecutive patients (median age = 66 years, [IQR = 57.25-71.75 years]; median PSA = 4.30 ng/mL [IQR = 1.33-7.75 ng/mL]) with suspected or confirmed PCa, who underwent mpMRI between October 2023 and January 2024. The study was conducted in two rounds, separated by a 4-week wash-out period. In each round, four readers evaluated T2W-MRI and standard or AI-generated ADC (AI-ADC) maps. Fleiss' kappa, quadratic-weighted Cohen's kappa statistics were used to assess inter-reader agreement. Linear mixed effect models were employed to compare the quality evaluation of standard versus AI-ADC maps. AI-ADC maps exhibited significantly enhanced imaging quality compared to standard ADC maps with higher ratings in windowing ease (β = 0.67 [95% CI 0.30-1.04], p < 0.05), prostate boundary delineation (β = 1.38 [95% CI 1.03-1.73], p < 0.001), reductions in distortion (β = 1.68 [95% CI 1.30-2.05], p < 0.001), noise (β = 0.56 [95% CI 0.24-0.88], p < 0.001). AI-ADC maps reduced reacquisition requirements for all readers (β = 2.23 [95% CI 1.69-2.76], p < 0.001), supporting potential workflow efficiency gains. No differences were observed between AI-ADC and standard ADC maps' inter-reader agreement. Our multi-reader study demonstrated that AI-ADC maps improved prostate boundary delineation, had lower image noise, fewer distortions, and higher overall image quality compared to ADC maps. Question Can we synthesize apparent diffusion coefficient (ADC) maps with AI to achieve higher quality maps? Findings On average, readers rated quality factors of AI-ADC maps higher than ADC maps in 34.80% of cases, compared to 5.07% for ADC (p < 0.01). Clinical relevance AI-ADC maps may serve as a reliable diagnostic support tool thanks to their high quality, particularly when the acquired ADC maps include artifacts.

To construct an artificial intelligence (AI)-assisted model for identifying the infraorbital posterior ethmoid cells (IPECs) based on deep learning using sagittal CT images. Sagittal CT images of 277 samples with and 142 samples without IPECs were retrospectively collected. An experienced radiologist engaged in the relevant aspects picked a sagittal CT image that best showed IPECs. The images were randomly assigned to the training and test sets, with 541 sides in the training set and 97 sides in the test set. The training set was used to perform a five-fold cross-validation, and the results of each fold were used to predict the test set. The model was built using nnUNet, and its performance was evaluated using Dice and standard classification metrics. The model achieved a Dice coefficient of 0.900 in the training set and 0.891 in the additional set. Precision was 0.965 for the training set and 1.000 for the additional set, while sensitivity was 0.981 and 0.967, respectively. A comparison of the diagnostic efficacy between manual outlining by a less-experienced radiologist and AI-assisted outlining showed a significant improvement in detection efficiency (P < 0.05). The AI model aided correctly in identifying and outlining all IPECs, including 12 sides that the radiologist should improve portraying. AI models can help radiologists identify the IPECs, which can further prompt relevant clinical interventions.

The prevalence of asymptomatic pancreatic cysts is increasing due to advances in imaging techniques. Among these, intraductal papillary mucinous neoplasms (IPMNs) are most common, with potential for malignant transformation, often necessitating close follow-up. This study evaluates novel MRI techniques for the assessment of IPMN. From May to December 2023, 59 patients undergoing abdominal MRI were retrospectively enrolled. Examinations were conducted on 3-Tesla scanners using a Deep-Learning Accelerated Half-Fourier Single-Shot Turbo Spin-Echo (HASTE_DL) and standard HASTE (HASTE_S) sequence. Two readers assessed minimum detectable lesion size and lesion-to-parenchyma contrast quantitatively, and qualitative assessments focused on image quality. Statistical analyses included the Wilcoxon signed-rank and chi-squared tests. HASTE_DL demonstrated superior overall image quality (p < 0.001), with higher sharpness and contrast ratings (p < 0.001, p = 0.112). HASTE_DL showed enhanced conspicuity of IPMN (p < 0.001) and lymph nodes (p < 0.001), with more frequent visualization of IPMN communication with the pancreatic duct (p < 0.001). Visualization of complex features (dilated pancreatic duct, septa, and mural nodules) was superior in HASTE_DL (p < 0.001). The minimum detectable cyst size was significantly smaller for HASTE_DL (4.17 mm ± 3.00 vs. 5.51 mm ± 4.75; p < 0.001). Inter-reader agreement was for (к 0.936) for HASTE_DL, slightly lower (к 0.885) for HASTE_S. HASTE_DL in IPMN imaging provides superior image quality and significantly reduced scan times. Given the increasing prevalence of IPMN and the ensuing clinical need for fast and precise imaging, HASTE_DL improves the availability and quality of patient care. Question Are there advantages of deep-learning-accelerated MRI in imaging and assessing intraductal papillary mucinous neoplasms (IPMN)? Findings Deep-Learning Accelerated Half-Fourier Single-Shot Turbo Spin-Echo (HASTE_DL) demonstrated superior image quality, improved conspicuity of "worrisome features" and detection of smaller cysts, with significantly reduced scan times. Clinical relevance HASTEDL provides faster, high-quality MRI imaging, enabling improved diagnostic accuracy and timely risk stratification for IPMN, potentially enhancing patient care and addressing the growing clinical demand for efficient imaging of IPMN.

PXseg, a novel approach for tooth segmentation, numbering and abnormal morphology detection in panoramic X-ray (PX), was designed and promoted through optimizing annotation and applying pre-training. Derived from multicenter, ctPXs generated from cone beam computed tomography (CBCT) with accurate 3D labels were utilized for pre-training, while conventional PXs (cPXs) with 2D labels were input for training. Visual and statistical analyses were conducted using the internal dataset to assess segmentation and numbering performances of PXseg and compared with the model without ctPX pre-training, while the accuracy of PXseg detecting abnormal teeth was evaluated using the external dataset consisting of cPXs with complex dental diseases. Besides, a diagnostic testing was performed to contrast diagnostic efficiency with and without PXseg's assistance. The DSC and F1-score of PXseg in tooth segmentation reached 0.882 and 0.902, which increased by 4.6% and 4.0% compared to the model without pre-training. For tooth numbering, the F1-score of PXseg reached 0.943 and increased by 2.2%. Based on the promotion in segmentation, the accuracy of abnormal tooth morphology detection exceeded 0.957 and was 4.3% higher. A website was constructed to assist in PX interpretation, and the diagnostic efficiency was greatly enhanced with the assistance of PXseg. The application of accurate labels in ctPX increased the pre-training weight of PXseg and improved the training effect, achieving promotions in tooth segmentation, numbering and abnormal morphology detection. Rapid and accurate results provided by PXseg streamlined the workflow of PX diagnosis, possessing significant clinical application prospect.

Spinal navigation systems require pre- and/or intra-operative 3-D imaging, which expose young patients to harmful radiation. We assessed a scoliosis-specific MRI-protocol that provides T2-weighted MRI and AI-generated synthetic-CT (sCT) scans, through deep learning algorithms. This study aims to compare MRI-based synthetic-CT spinal navigation to CT for safety and accuracy of pedicle screw planning and placement at thoracic and lumbar levels. Spines of 5 cadavers were scanned with thin-slice CT and the scoliosis-specific MRI-protocol (to create sCT). Preoperatively, on both CT and sCT screw trajectories were planned. Subsequently, four spine surgeons performed surface-matched, navigated placement of 2.5 mm k-wires in all pedicles from T3 to L5. Randomization for CT/sCT, surgeon and side was performed (1:1 ratio). On postoperative CT-scans, virtual screws were simulated over k-wires. Maximum angulation, distance between planned and postoperative screw positions and medial breach rate (Gertzbein-Robbins classification) were assessed. 140 k-wires were inserted, 3 were excluded. There were no pedicle breaches > 2 mm. Of sCT-guided screws, 59 were grade A and 10 grade B. For the CT-guided screws, 47 were grade A and 21 grade B (p = 0.022). Average distance (± SD) between intraoperative and postoperative screw positions was 2.3 ± 1.5 mm in sCT-guided screws, and 2.4 ± 1.8 mm for CT (p = 0.78), average maximum angulation (± SD) was 3.8 ± 2.5° for sCT and 3.9 ± 2.9° for CT (p = 0.75). MRI-based, AI-generated synthetic-CT spinal navigation allows for safe and accurate planning and placement of thoracic and lumbar pedicle screws in a cadaveric model, without significant differences in distance and angulation between planned and postoperative screw positions compared to CT.

Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype). Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points. Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender.

High-resolution volumetric computed tomography (CT) is essential for accurate diagnosis and treatment planning in thoracic diseases; however, it is limited by radiation dose and hardware costs. We present the Transformer Volumetric Super-Resolution Network (\textbf{TVSRN-V2}), a transformer-based super-resolution (SR) framework designed for practical deployment in clinical lung CT analysis. Built from scalable components, including Through-Plane Attention Blocks (TAB) and Swin Transformer V2 -- our model effectively reconstructs fine anatomical details in low-dose CT volumes and integrates seamlessly with downstream analysis pipelines. We evaluate its effectiveness on three critical lung cancer tasks -- lobe segmentation, radiomics, and prognosis -- across multiple clinical cohorts. To enhance robustness across variable acquisition protocols, we introduce pseudo-low-resolution augmentation, simulating scanner diversity without requiring private data. TVSRN-V2 demonstrates a significant improvement in segmentation accuracy (+4\% Dice), higher radiomic feature reproducibility, and enhanced predictive performance (+0.06 C-index and AUC). These results indicate that SR-driven recovery of structural detail significantly enhances clinical decision support, positioning TVSRN-V2 as a well-engineered, clinically viable system for dose-efficient imaging and quantitative analysis in real-world CT workflows.

Low-dose computed tomography plays a crucial role in reducing radiation exposure in clinical imaging, however, the resultant noise significantly impacts image quality and diagnostic precision. Recent transformer-based models have demonstrated strong denoising capabilities but are often constrained by high computational complexity. To overcome these limitations, we propose AMFA-Net, an adaptive multi-order feature aggregation network that provides a lightweight architecture for enhancing highresolution feature representation in low-dose CT imaging. AMFA-Net effectively integrates local and global contexts within high-resolution feature maps while learning discriminative representations through multi-order context aggregation. We introduce an agent-based self-attention crossshaped window transformer block that efficiently captures global context in high-resolution feature maps, which is subsequently fused with backbone features to preserve critical structural information. Our approach employs multiorder gated aggregation to adaptively guide the network in capturing expressive interactions that may be overlooked in fused features, thereby producing robust representations for denoised image reconstruction. Experiments on two challenging public datasets with 25% and 10% full-dose CT image quality demonstrate that our method surpasses state-of-the-art approaches in denoising performance with low computational cost, highlighting its potential for realtime medical applications.

To compare the trueness of artificial intelligence (AI)-based, manual, and global segmentation protocols by superimposing the resulting segmented 3D models onto reference gold standard surface scan models. Twelve dry human mandibles were used. A cone beam computed tomography (CBCT) scanner was used to scan the mandibles, and the acquired digital imaging and communications in medicine (DICOM) files were segmented using three protocols: global thresholding, manual, and AI-based segmentation (Diagnocat; Diagnocat, San Francisco, CA). The segmented files were exported as study 3D models. A structured light surface scanner (GoSCAN Spark; Creaform 3D, Levis, Canada) was used to scan all mandibles, and the resulting reference 3D models were exported. The study 3D models were compared with the respective reference 3D models by using a mesh comparison software (Geomagic Design X; 3D Systems Inc, Rock Hill, SC). Root mean square (RMS) error values were recorded to measure the magnitude of deviation (trueness), and color maps were obtained to visualize the differences. Comparisons of the trueness of three segmentation methods for differences in RMS were made using repeated measures analysis of variance (ANOVA). A two-sided 5% significance level was used for all tests in the software program. AI-based segmentations had significantly higher RMS values than manual segmentations for the entire mandible (p < 0.001), alveolar process (p < 0.001), and body of the mandible (p < 0.001). AI-based segmentations had significantly lower RMS values than manual segmentations for the condyles (p = 0.018) and ramus (p = 0.013). No significant differences were found between the AI-based and manual segmentations for the coronoid process (p = 0.275), symphysis (p = 0.346), and angle of the mandible (p = 0.344). Global thresholding had significantly higher RMS values than manual segmentations for the alveolus (p < 0.001), angle of the mandible (p < 0.001), body of the mandible (p < 0.001), condyles (p < 0.001), coronoid (p = 0.002), entire mandible (p < 0.001), ramus (p < 0.001), and symphysis (p < 0.001). Global thresholding had significantly higher RMS values than AI-based segmentation for the alveolar process (p = 0.002), angle of the mandible (p < 0.001), body of the mandible (p < 0.001), condyles (p < 0.001), coronoid (p = 0.017), mandible (p < 0.001), ramus (p < 0.001), and symphysis (p < 0.001). AI-based segmentations produced lower RMS values, indicating truer 3D models, compared to global thresholding, and showed no significant differences in some areas compared to manual segmentation. Thus, AI-based segmentation offers a level of segmentation trueness acceptable for use as an alternative to manual or global thresholding segmentation protocols.