Bone marrow adipose tissue, as a distinct adipose subtype, has been implicated in the pathophysiology of skeletal, metabolic, and hematopoietic disorders. To identify its underlying genetic factors, we utilized a deep learning algorithm capable of quantifying bone marrow fat fraction (BMFF) in the vertebrae and proximal femur using magnetic resonance imaging data of over 38,000 UK Biobank participants. Genome-wide association analyses uncovered 373 significant BMFF-associated variants (P-value < 5 × 10^-9), with enrichment in bone remodeling, metabolism, and hematopoiesis pathway. Furthermore, genetic correlation highlighted a significant association between BMFF and skeletal disease. In about 300,000 individuals, polygenic risk scores derived from three proximal femur BMFF were significantly associated with increased osteoporosis risk. Notably, Mendelian randomization analyses revealed a causal link between proximal femur BMFF and osteoporosis. Here, we show critical insights into the genetic determinants of BMFF and offer perspectives on the biological mechanisms driving osteoporosis development.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT and MRI scans, aimed at enhancing the realism of surgical simulators for emergency scenarios such as trauma, where rapid detection of internal bleeding can be lifesaving. First, medical images are segmented and converted into graph representations of the vasculature, where nodes represent vessel branching points with spatial coordinates and edges encode vessel features such as length and radius. Due to no existing dataset directly labeling bleeding risks, we calculate the bleeding probability for each vessel node using a physics-based heuristic, peripheral vascular resistance via the Hagen-Poiseuille equation. A graph attention network is then trained to regress these probabilities, effectively learning to predict hemorrhage risk from the graph-structured imaging data. The model is trained using a tenfold cross-validation on a combined dataset of 1708 vessel graphs extracted from four public image datasets (MSD, KiTS, AbdomenCT, CT-ORG) with optimization via the Adam optimizer, mean squared error loss, early stopping, and L2 regularization. Our model achieves a mean R-squared of 0.86, reaching up to 0.9188 in optimal configurations and low mean training and validation losses of 0.0069 and 0.0074, respectively, in predicting bleeding risk, with higher performance on well-connected vascular graphs. Finally, we integrate the trained model into an immersive virtual reality environment to simulate intra-abdominal bleeding scenarios for immersive surgical training. The model demonstrates robust predictive performance despite the inherent sparsity of real-life datasets.

Accurate risk stratification is crucial for determining the optimal treatment plan for patients with colorectal cancer (CRC). However, existing deep learning models perform poorly in the preoperative diagnosis of CRC and exhibit limited generalizability, primarily due to insufficient annotated data. To address these issues, CRCFound, a self-supervised learning-based CT image foundation model for CRC is proposed. After pretraining on 5137 unlabeled CRC CT images, CRCFound can learn universal feature representations and provide efficient and reliable adaptability for various clinical applications. Comprehensive benchmark tests are conducted on six different diagnostic tasks and two prognosis tasks to validate the performance of the pretrained model. Experimental results demonstrate that CRCFound can easily transfer to most CRC tasks and exhibit outstanding performance and generalization ability. Overall, CRCFound can solve the problem of insufficient annotated data and perform well in a wide range of downstream tasks of CRC, making it a promising solution for accurate diagnosis and personalized treatment of CRC patients.

Generative artificial intelligence (AI) has been playing an important role in various domains. Leveraging its high capability to generate high-fidelity and diverse synthetic data, generative AI is widely applied in diagnostic tasks, such as lung cancer diagnosis using computed tomography (CT). However, existing generative models for lung cancer diagnosis suffer from low efficiency and anatomical imprecision, which limit their clinical applicability. To address these drawbacks, we propose Lung-DDPM+, an improved version of our previous model, Lung-DDPM. This novel approach is a denoising diffusion probabilistic model (DDPM) guided by nodule semantic layouts and accelerated by a pulmonary DPM-solver, enabling the method to focus on lesion areas while achieving a better trade-off between sampling efficiency and quality. Evaluation results on the public LIDC-IDRI dataset suggest that the proposed method achieves 8$\times$ fewer FLOPs (floating point operations per second), 6.8$\times$ lower GPU memory consumption, and 14$\times$ faster sampling compared to Lung-DDPM. Moreover, it maintains comparable sample quality to both Lung-DDPM and other state-of-the-art (SOTA) generative models in two downstream segmentation tasks. We also conducted a Visual Turing Test by an experienced radiologist, showing the advanced quality and fidelity of synthetic samples generated by the proposed method. These experimental results demonstrate that Lung-DDPM+ can effectively generate high-quality thoracic CT images with lung nodules, highlighting its potential for broader applications, such as general tumor synthesis and lesion generation in medical imaging. The code and pretrained models are available at https://github.com/Manem-Lab/Lung-DDPM-PLUS.

Cervical 2 pedicle screw (C2PS) fixation is widely used in posterior cervical surgery but carries risks of vertebral artery injury (VAI), a rare yet severe complication. This study aimed to identify risk factors for VAI during C2PS placement and develop a machine learning (ML)-based predictive model to enhance preoperative risk assessment. Clinical and radiological data from 280 patients undergoing head and neck CT angiography were retrospectively analyzed. Three-dimensional reconstructed images simulated C2PS placement, classifying patients into injury (n = 98) and non-injury (n = 182) groups. Fifteen variables, including characteristic of patients and anatomic variables were evaluated. Eight ML algorithms were trained (70% training cohort) and validated (30% validation cohort). Model performance was assessed using AUC, sensitivity, specificity, and SHAP (SHapley Additive exPlanations) for interpretability. Six key risk factors were identified: pedicle diameter, high-riding vertebral artery (HRVA), intra-axial vertebral artery (IAVA), vertebral artery diameter (VAD), distance between the transverse foramen and the posterior end of the vertebral body (TFPEVB) and distance between the vertebral artery and the vertebral body (VAVB). The neural network model (NNet) demonstrated optimal predictive performance, achieving AUCs of 0.929 (training) and 0.936 (validation). SHAP analysis confirmed these variables as primary contributors to VAI risk. This study established an ML-driven predictive model for VAI during C2PS placement, highlighting six critical anatomical and radiological risk factors. Integrating this model into clinical workflows may optimize preoperative planning, reduce complications, and improve surgical outcomes. External validation in multicenter cohorts is warranted to enhance generalizability.

Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in patients with locally advanced rectal tumors. However, the response to CRT in patients with locally advanced rectal cancer (LARC) varies significantly. The objective of this study was to compare the performance of models based on radiomics features of the tumor alone, the mesorectum alone, and a combination of both in predicting tumor response to neoadjuvant CRT in LARC. This retrospective study included 101 patients with LARC. Patients were categorized as responders (modified Ryan score 0-1) and non-responders (modified Ryan score 2-3). Pre-CRT magnetic resonance imaging evaluations included tumor-T2 weighted imaging (T2WI), tumor-diffusion weighted imaging (DWI), tumor-apparent diffusion coefficient (ADC) maps, and mesorectum-T2WI. The first radiologist segmented the tumor and mesorectum from T2-weighted images, and the second radiologist performed tumor segmentation using DWI and ADC maps. Feature reproducibility was assessed by calculating the intraclass correlation coefficient (ICC) using a two-way mixed-effects model with absolute agreement for single measurements [ICC(3,1)]. Radiomic features with ICC values <0.60 were excluded from further analysis. Subsequently, the least absolute shrinkage and selection operator method was applied to select the most relevant radiomic features. The top five features with the highest coefficients were selected for model training. To address class imbalance between groups, the synthetic minority over-sampling technique was applied exclusively to the training folds during cross-validation. Thereafter, classification learner models were developed using 10-fold cross-validation to achieve the highest performance. The performance metrics of the final models, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC), were calculated to evaluate the classification performance. Among the 101 patients, 36 were classified as responders and 65 as non-responders. A total of 25 radiomic features from the tumor and 20 from the mesorectum were found to be statistically significant (<i>P</i> < 0.05). The AUC values for predicting treatment response were 0.781 for the tumor-only model (random forest), 0.726 for the mesorectum-only model (logistic regression), and 0.837 for the combined model (logistic regression). Radiomic features derived from both the tumor and mesorectum demonstrated complementary prognostic value in predicting treatment response. The inclusion of mesorectal features substantially improved model performance, with the combined model achieving the highest AUC value. These findings highlight the added predictive contribution of the mesorectum as a key peritumoral structure in radiomics-based assessment. Currently, the response of locally advanced rectal tumors to neoadjuvant therapy cannot be reliably predicted using conventional methods. Recently, the significance of the mesorectum in predicting treatment response has gained attention, although the number of studies focusing on this area remains limited. In our study, we performed radiomics analyses of both the tumor tissue and the mesorectum to predict neoadjuvant treatment response.

We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and "model for end-stage liver disease-sodium" (MELD-Na) score) and fibrosis/portal hypertension (Fibrosis-4 (FIB-4) Score, liver stiffness measurement (LSM), hepatic venous pressure gradient (HVPG), platelet count (PLT), and spleen volume. We included 197 subjects, aged 54.9 ± 13.8 years (mean ± standard deviation), 111 males (56.3%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3; 1.0) (p ≤ 0.001). PVVR was reduced in both non-ACLD and ACLD patients (both 1.2) compared to controls (1.7) (p ≤ 0.001), but showed no difference between CLD groups (p = 0.999). HVVR significantly correlated indirectly with FIB-4, ALBI, MELD-Na, LSM, and spleen volume (ρ ranging from -0.27 to -0.40), and directly with PLT (ρ = 0.36). TVVR and PVVR showed similar but weaker correlations. Deep learning-based hepatic vessel volumetry demonstrated differences between healthy liver and chronic liver disease stages and shows correlations with established markers of disease severity. Hepatic vessel volumetry demonstrates differences between healthy liver and chronic liver disease stages, potentially serving as a non-invasive imaging biomarker. Deep learning-based vessel analysis can provide automated quantification of hepatic vascular changes across healthy liver and chronic liver disease stages. Automated quantification of hepatic vasculature shows significantly reduced hepatic vascular volume in advanced chronic liver disease compared to non-advanced disease and healthy liver. Decreased hepatic vascular volume, particularly in the hepatic venous system, correlates with markers of liver dysfunction, fibrosis, and portal hypertension.

Although surface-enhanced Raman scattering (SERS) spectroscopy is applied in biomedicine deeply, the design of new substrates for wider detection is still in demand. Crystalline-amorphous CoSe₂/CoS₂ heterojunction is synthesized, with high SERS performance and stability, composed of orthorhombic (o-CoSe₂) and amorphous CoS₂ (a-CoS₂). By adjusting feed ratio, the proportion of a-CoS₂ to o-CoSe₂ is regulated, where CoSe₂/CoS₂-S50 with a 1:1 ratio demonstrates the best SERS performance due to the balance of two components. It is confirmed through experimental and simulation methods that o-CoSe₂ and a-CoS₂ have unique contribution, respectively: a-CoS₂ has rich vacancies and a higher density of active sites, while o-CoSe₂ further enriches vacancies, enhances electron delocalization and charge transfer (CT) capabilities, and reduces bandgap. Besides, CoSe₂/CoS₂-S50 achieves not only SERS detection of two common esophageal tumor cells (KYSE and TE) and healthy oral epithelial cells (het-1A), but also the discrimination with high sensitivity, specificity, and accuracy via machine learning (ML) analysis.

Tumor-infiltrating lymphocytes (TILs) are key immune biomarkers associated with prognosis and treatment response in early-stage breast cancer (BC), particularly in the triple-negative subtype. This study aimed to evaluate whether [18F]FDG PET/CT imaging and routine cell blood count (CBC)-derived biomarkers can serve as non-invasive surrogates for TILs, using machine-learning models. We retrospectively analyzed 358 patients with biopsy-proven early-stage invasive BC who underwent pre-treatment [18F]FDG PET/CT imaging. PET-derived biomarkers were extracted from the primary tumor, lymph nodes, and lymphoid organs (spleen and bone marrow). CBC-derived biomarkers included neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). TILs were assessed histologically and categorized as low (0-10%), intermediate (11-59%), or high (≥ 60%). Correlations were assessed using Spearman's rank coefficient, and classification and regression models were built using several machine-learning algorithms. Tumor SUVmax and tumor SUVmean showed the highest correlation with TIL levels (ρ = 0.29 and 0.30 respectively, p < 0.001 for both), but overall associations between TILs and PET or CBC-derived biomarkers were weak. No CBC-derived biomarker showed significant correlation or discriminative performance. Machine-learning models failed to predict TIL levels with satisfactory accuracy (maximum balanced accuracy = 0.66). Lymphoid organ metrics (SLR, BLR) and CBC-derived parameters did not significantly enhance predictive value. In this study, neither [18F]FDG PET/CT nor routine CBC-derived biomarkers reliably predict TILs levels in early-stage BC. This observation was made in presence of potential scanner-related variability and for a restricted set of usual PET metrics. Future models should incorporate more targeted imaging approaches, such as immunoPET, to non-invasively assess immune infiltration with higher specificity and improve personalized treatment strategies.

Accurate segmentation of thyroid nodules in ultrasound images is critical for clinical diagnosis but remains challenging due to low contrast and complex anatomical structures. Existing deep learning methods often rely solely on local nodule features, lacking anatomical prior knowledge of the thyroid region, which can result in misclassification of non-thyroid tissues, especially in low-quality scans. To address these issues, we propose a Spatial Prior-Guided Dual-Path Network that integrates a prior-aware encoder to model thyroid anatomical structures and a low-cost heterogeneous encoder to preserve fine-grained multi-scale features, enhancing both spatial detail and contextual awareness. To capture the diverse and irregular appearances of nodules, we design a CrossBlock module, which combines an efficient cross-attention mechanism with mixed-scale convolutional operations to enable global context modeling and local feature extraction. The network further employs a dual-decoder architecture, where one decoder learns thyroid region priors and the other focuses on accurate nodule segmentation. Gland-specific features are hierarchically refined and injected into the nodule decoder to enhance boundary delineation through anatomical guidance. Extensive experiments on the TN3K and MTNS datasets demonstrate that our method consistently outperforms state-of-the-art approaches, particularly in boundary precision and localization accuracy, offering practical value for preoperative planning and clinical decision-making.