There is no foundational classification that 3-dimensionally characterizes arthritic anatomy to preoperatively plan and postoperatively evaluate total knee arthroplasty (TKA). With the advent of computed tomography (CT) as a preoperative planning tool, the purpose of this study was to morphologically classify pre-TKA anatomy across coronal, axial, and sagittal planes to identify outlier phenotypes and establish a foundation for future philosophical, technical, and technological strategies. A cross-sectional analysis was conducted using 1,352 pre-TKA lower-extremity CT scans collected from a database at a single multicenter referral center. A validated deep learning and computer vision program acquired 27 lower-extremity measurements for each CT scan. An unsupervised spectral clustering algorithm morphometrically classified the cohort. The optimal number of clusters was determined through elbow-plot and eigen-gap analyses. Visualization was conducted through t-stochastic neighbor embedding, and each cluster was characterized. The analysis was repeated to assess how it was affected by severe deformity by removing impacted parameters and reassessing cluster separation. Spectral clustering revealed 4 distinct pre-TKA anatomic morphologies (18.5% Type 1, 39.6% Type 2, 7.5% Type 3, 34.5% Type 4). Types 1 and 3 embodied clear outliers. Key parameters distinguishing the 4 morphologies were hip rotation, medial posterior tibial slope, hip-knee-ankle angle, tibiofemoral angle, medial proximal tibial angle, and lateral distal femoral angle. After removing variables impacted by severe deformity, the secondary analysis again demonstrated 4 distinct clusters with the same distinguishing variables. CT-based phenotyping established a 3D classification of arthritic knee anatomy into 4 foundational morphologies, of which Types 1 and 3 represent outliers present in 26% of knees undergoing TKA. Unlike prior classifications emphasizing native coronal plane anatomy, 3D phenotyping of knees undergoing TKA enables recognition of outlier cases and a foundation for longitudinal evaluation in a morphologically diverse and growing surgical population. Longitudinal studies that control for implant selection, alignment technique, and applied technology are required to evaluate the impact of this classification in enabling rapid recovery and mitigating dissatisfaction after TKA. Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Accurate assessment of proximal humeral volumetric bone mineral density (vBMD) is essential for surgical planning in shoulder pathology. However, age-related changes in proximal humeral vBMD remain poorly characterized. This study developed a deep learning-based method to assess proximal humeral vBMD and identified sex-specific age-related changes. It also demonstrated that lumbar spine vBMD is not a valid substitute. This study aimed to develop a deep learning-based method for proximal humeral vBMD assessment and to investigate its age- and sex-related changes, as well as its correlation with lumbar spine vBMD. An nnU-Net-based deep learning pipeline was developed to automatically segment the proximal humerus on chest CT scans from 2,675 adults. Segmentation performance was assessed using the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), 95th-percentile Hausdorff Distance (95HD), and Average Symmetric Surface Distance (ASSD). Phantom-calibrated vBMD-total, trabecular, and BMAT-corrected trabecular-was quantified for each subject. Age-related distributions were modeled with generalized additive models for location, scale, and shape (GAMLSS) to generate sex-specific P3-P97 percentile curves. Lumbar spine vBMD was measured in 1460 individuals for correlation analysis. Segmentation was highly accurate (DSC 98.42 ± 0.20%; IoU 96.89 ± 0.42%; 95HD 1.12 ± 0.37 mm; ASSD 0.94 ± 0.31 mm). In males, total, trabecular, and BMAT-corrected trabecular vBMD declined approximately linearly from early adulthood. In females, a pronounced inflection occurred at ~ 40-45 years: values were stable or slightly rising beforehand, then all percentiles dropped steeply and synchronously, indicating accelerated menopause-related loss. In females, vBMD declined earlier in the lumbar spine than in the proximal humerus. Correlations between proximal humeral and lumbar spine vBMD were low to moderate overall and weakened after age 50. We present a novel, automated method for quantifying proximal humeral vBMD from chest CT, revealing distinct, sex-specific aging patterns. Males' humeral vBMD declines linearly, while females experience an earlier, accelerated loss. Moreover, the peak humeral vBMD in females occurs later than that of the lumbar spine, and spinal measurements cannot reliably substitute for humeral BMD in clinical assessment.

This study aims to construct a noninvasive preoperative prediction model for lymph node metastasis in adenocarcinoma of esophagogastric junction (AEG) using computed tomography (CT) texture characterization and machine learning. We analyzed clinical and imaging data from 57 patients with preoperative CT enhancement scans and pathologically confirmed AEG. Lesions were delineated, and texture features were extracted from arterial phase and venous phase CT images using 3D-Slicer software. Features were normalized, downscaled, and screened using correlation analysis and the least absolute shrinkage and selection operator algorithm. The lymph node metastasis prediction model employed machine learning algorithms (random forest, logistic regression, decision tree [DT], and support vector machine), with performance validated using receiver operating characteristic curves. In the arterial phase, the random forest model excelled in precision (0.86) and positive predictive value (0.86). The DT model exhibited the best negative predictive value (0.86), while the logistic regression model demonstrated the highest area under the curve (AUC; 0.78) and specificity (1.0). During the venous phase, the DT model excelled in precision (0.72), F1 score (0.76), and recall (0.80), whereas the support vector machine model had the highest AUC (0.75). Differences in AUCs between models in both phases were not statistically significant per DeLong's test, indicating comparable performance. Each model displayed strengths across various metrics, with the DT model showing consistent performance across arterial and venous phases, emphasizing accuracy and specificity. The CT texture-based machine learning model effectively predicts lymph node metastasis noninvasively in AEG patients, demonstrating robust predictive efficacy.

Deep learning reconstruction (DLR) offers a variety of advantages over the current standard iterative reconstruction techniques, including decreased image noise without changes in noise texture and less susceptibility to spatial resolution limitations at low dose. These advances may allow for more aggressive dose reduction in CT imaging while maintaining image quality and diagnostic accuracy. However, performance of DLRs is impacted by the type of framework and training data used. In addition, the patient size and clinical task being performed may impact the amount of dose reduction that can be reasonably employed. Multiple DLRs are currently FDA approved with a growing body of literature evaluating performance throughout this body; however, continued work is warranted to evaluate a variety of clinical scenarios to fully explore the evolving potential of DLR. Depending on the type and strength of DLR applied, blurring and occasionally other artifacts may be introduced. DLRs also show promise in artifact reduction, particularly metal artifact reduction. This commentary focuses primarily on current DLR data for abdominal applications, current challenges, and future areas of potential exploration.

Metastasis remains the major challenge in the clinical management of head and neck squamous cell carcinoma (HNSCC). Reliable pre-treatment prediction of metastatic risk is crucial for optimizing treatment strategies and prognosis. This study develops a deep learning-based multimodal framework to predict metastasis risk in HNSCC patients by integrating computed tomography (CT) images, radiomics, and clinical data. 1497 HNSCC patients were included. Tumor and organ masks were derived from pretreatment CT images. A 3D Swin Transformer extracted deep features from tumor regions. Meanwhile, 1562 radiomics features were obtained using PyRadiomics, followed by correlation filtering and random forest selection, leaving 36 features. Clinical variables including age, sex, smoking, and alcohol status were encoded and fused with imaging-derived features. Multimodal features were fed into a fully connected network to predict metastasis risk. Performance was evaluated using five-fold cross-validation with area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). The proposed fusion model outperformed single-modality models. The 3D deep learning module alone achieved an AUC of 0.715, and when combined with radiomics and clinical features, predictive performance improved (AUC = 0.803, ACC = 0.752, SEN = 0.730, SPE = 0.758). Stratified analysis showed generalizability across tumor subtypes. Ablation studies indicated complementary information from different modalities. Evaluation showed the 3D Swin Transformer provided more robust representation learning than conventional networks. This multimodal fusion model demonstrated high accuracy and robustness in predicting metastasis risk in HNSCC, offering a comprehensive representation of tumor biology. The interpretable model has potential as a clinical decision-support tool for personalized treatment planning.

A recent investigation into deep learning-based denoising for early Monte Carlo (MC) Path Tracing in computed tomography (CT) volume visualization yielded promising quantitative outcomes but inconsistent qualitative assessments. This research probes the underlying causes of this incongruity by deploying a web-based SurveyMonkey questionnaire distributed among healthcare professionals. The survey targeted radiologists, residents, orthopedic surgeons, and veterinarians, leveraging the authors' professional networks for dissemination. To evaluate perceptions, the questionnaire featured randomized sections gauging attitudes towards AI-enhanced image and video quality, confidence in reference images, and clinical applicability. Seventy-four participants took part, encompassing a spectrum of experience levels: <1 year (n=11), 1-3 years (n=27), 3-5 years (n=12), and >5 years (n=24). A substantial majority (77%) expressed a preference for AI-enhanced images over traditional MC estimates, a preference influenced by participant experience (adjusted OR 0.81, 95% CI 0.67-0.98, p=0.033). Experience correlates with confidence in AI-generated images (adjusted OR 0.98, 95% CI 0.95-1, p=0.018-0.047) and satisfaction with video previews, both with and without AI (adjusted OR 0.96-0.98, 95% CI 0.92-1, p = 0.033-0.048). Significant monotonic relationships emerged between experience, confidence (σ= 0.25-0.26, p = 0.025-0.029), and satisfaction (σ= 0.23-0.24, p = 0.037-0.046). The findings underscore the potential of AI post-processing to improve the rendering of biomedical volumes, noting enhanced confidence and satisfaction among experienced participants. The study reveals that participants' preferences may not align perfectly with quality metrics such as peak signal-to-noise ratio and structural similarity index, highlighting nuances in evaluating AI's qualitative impact on CT image denoising.

Inflammation plays important roles in the pathogenesis of vascular diseases. We here show the involvement of perivascular inflammation in aortic dilatation of Marfan syndrome (MFS). In the aorta of MFS patients and Fbn1C1041G/+ mice, macrophages markedly accumulated in periaortic tissues with increased inflammatory cytokine expression. Metabolic inflammatory stress induced by a high-fat diet (HFD) enhanced vascular inflammation predominantly in periaortic tissues and accelerated aortic dilatation in Fbn1C1041G/+ mice, both of which were inhibited by low-dose pitavastatin. HFD feeding also intensifies structural disorganization of the tunica media in Fbn1C1041G/+ mice, including elastic fiber fragmentation, fibrosis, and proteoglycan accumulation, along with increased activation of TGF-β downstream targets. Pitavastatin treatment mitigated these alterations. For non-invasive assessment of PVAT inflammation in a clinical setting, we developed an automated analysis program for CT images using machine learning techniques to calculate the perivascular fat attenuation index of the ascending aorta (AA-FAI), correlating with periaortic fat inflammation. The AA-FAI was significantly higher in patients with MFS compared to patients without hereditary connective tissue disorders. These results suggest that perivascular inflammation contributes to aneurysm formation in MFS and might be a potential target for preventing and treating vascular events in MFS.

Image-guided thermal ablation (IGTA) has been increasingly used in patients with stage IA non-small cell lung cancer (NSCLC) without surgical contraindications, but its long-term outcomes compared to lobectomy remain unknown. This study aims to evaluate the long-term outcomes of IGTA versus lobectomy and explore which patients may benefit most from ablation. After propensity score matching, a total of 290 patients with stage IA NSCLC between 2015 and 2023 were included. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. A Markov model was constructed to evaluate cost-effectiveness. Finally, a radiomics model based on preoperative computed tomography (CT) was developed to perform risk stratification. After matching, the median follow-up intervals were 34.8 months for the lobectomy group and 47.2 months for the ablation group. There were no significant differences between the groups in terms of 5-year PFS (hazard ratio [HR], 1.83; 95% CI, 0.86-3.92; p = 0.118) or OS (HR, 2.44; 95% CI, 0.87-6.63; p = 0.092). In low-income regions, lobectomy was not cost-effective in 99% of simulations. The CT-based radiomics model outperformed the traditional TNM model (AUC, 0.759 vs. 0.650; p < 0.01). Moreover, disease-free survival was significantly lower in the high-risk group than in the low-risk group (p = 0.009). This study comprehensively evaluated IGTA versus lobectomy in terms of survival outcomes, cost-effectiveness, and prognostic prediction. The findings suggest that IGTA may be a safe and feasible alternative to conventional surgery for carefully selected patients.

Body composition (BC) analysis is performed to quantify the relative amounts of different body tissues as a measure of physical fitness and tumor cachexia. We hypothesized that relative changes in body composition (BC) parameters, assessed by an artificial intelligence-based, PACS-integrated software, between baseline imaging before the start of radioligand therapy (RLT) and interim staging after two RLT cycles could predict overall survival (OS) in patients with metastatic castration-resistant prostate cancer. We conducted a single-center, retrospective analysis of 92 patients with mCRPC undergoing [¹⁷⁷Lu]Lu-PSMA RLT between September 2015 and December 2023. All patients had [⁶⁸ Ga]Ga-PSMA-11 PET/CT at baseline (≤ 6 weeks before the first RLT cycle) and at interim staging (6-8 weeks after the second RLT cycle) allowing for longitudinal BC assessment. During follow-up, 78 patients (85%) died. Median OS was 16.3 months. Median follow-up time in survivors was 25.6 months. The 1 year mortality rate was 32.6% (95%CI 23.0-42.2%) and the 5 year mortality rate was 92.9% (95%CI 85.8-100.0%). In multivariable regression, relative change in visceral adipose tissue (VAT) (HR: 0.26; p = 0.006), previous chemotherapy of any type (HR: 2.4; p = 0.003), the presence of liver metastases (HR: 2.4; p = 0.018) and a higher baseline De Ritis ratio (HR: 1.4; p < 0.001) remained independent predictors of OS. Patients with a higher decrease in VAT (< -20%) had a median OS of 10.2 months versus 18.5 months in patients with a lower VAT decrease or VAT increase (≥ -20%) (log-rank test: p = 0.008). In a separate Cox model, the change in VAT predicted OS (p = 0.005) independent of the best PSA response after 1-2 RLT cycles (p = 0.09), and there was no interaction between the two (p = 0.09). PACS-Integrated, AI-based BC monitoring detects relative changes in the VAT, Which was an independent predictor of shorter OS in our population of patients undergoing RLT.

To evaluate the precision of automated segmentation facilitated by deep learning (DL) and dose calculation in adaptive radiotherapy (ART) for nasopharyngeal cancer (NPC), leveraging synthetic CT (sCT) images derived from cone-beam CT (CBCT) scans on a conventional C-arm linac. Sixteen NPC patients undergoing a two-phase offline ART were analyzed retrospectively. The initial (pCT₁) and adaptive (pCT₂) CT scans served as gold standard alongside weekly acquired CBCT scans. Patient data, including manually delineated contours and dose information, were imported into ArcherQA. Using a cycle-consistent generative adversarial network (cycle-GAN) trained on an independent dataset, sCT images (sCT₁, sCT₄, sCT₄^*) were generated from weekly CBCT scans (CBCT₁, CBCT₄, CBCT₄) paired with corresponding planning CTs (pCT₁, pCT₁, pCT₂). Auto-segmentation was performed on sCTs, followed by GPU-accelerated Monte Carlo dose recalculation. Auto-segmentation accuracy was assessed via Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD₉₅). Dose calculation fidelity on sCTs was evaluated using dose-volume parameters. Dosimetric consistency between recalculated sCT and pCT plans was analyzed via Spearman's correlation, while volumetric changes were concurrently evaluated to quantify anatomical variations. Most anatomical structures demonstrated high pCT-sCT agreement, with mean values of DSC > 0.85 and HD₉₅ < 5.10 mm. Notable exceptions included the primary Gross Tumor Volume (GTVp) in the pCT₂-sCT₄ comparison (DSC: 0.75, HD₉₅: 6.03 mm), involved lymph node (GTVn) showing lower agreement (DSC: 0.43, HD₉₅: 16.42 mm), and submandibular glands with moderate agreement (DSC: 0.64-0.73, HD₉₅: 4.45-5.66 mm). Dosimetric analysis revealed the largest mean differences in GTVn D₉₉: -1.44 Gy (95% CI: [-3.01, 0.13] Gy) and right parotid mean dose: -1.94 Gy (95% CI: [-3.33, -0.55] Gy, p < 0.05). Anatomical variations, quantified via sCTs measurements, correlated significantly with offline adaptive plan adjustments in ART. This correlation was strong for parotid glands (ρ > 0.72, p < 0.001), a result that aligned with sCT-derived dose discrepancy analysis (ρ > 0.57, p < 0.05). The proposed method exhibited minor variations in volumetric and dosimetric parameters compared to prior treatment data, suggesting potential efficiency improvements for ART in NPC through reduced human dependency.