To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectively analyzed data from 351 gastric cancer patients, randomly splitting them into two cohorts (training cohort, n = 246; testing cohort, n = 105) in a 7:3 ratio. The tumor region of interest (ROI) was outlined on venous phase CT images as the input for the development of radiomics, 2D and 3D DL models (DL2D and DL3D). Of note, by centering the analysis on the tumor's maximum cross-section and incorporating seven adjacent 2D images, we generated stable 2.5D data to establish a multi-instance learning (MIL) model. Meanwhile, the clinical and feature-combined models which integrated traditional CT enhancement parameters (Ratio), radiomics, and MIL features were also constructed. Models' performance was evaluated by the area under the curve (AUC), confusion matrices, and detailed metrics, such as sensitivity and specificity. A nomogram based on the combined model was established and applied to clinical practice. The calibration curve was used to evaluate the consistency between the predicted LVI of each model and the actual LVI of gastric cancer, and the decision curve analysis (DCA) was used to evaluate the net benefit of each model. Among the developed models, 2.5D MIL and combined models exhibited the superior performance in comparison to the clinical model, the radiomics model, the DL2D model, and the DL3D model as evidenced by the AUC values of 0.820, 0.822, 0.748, 0.725, 0.786, and 0.711 on testing set, respectively. Additionally, the 2.5D MIL and combined models also showed good calibration for LVI prediction, and could provide a net clinical benefit when the threshold probability ranged from 0.31 to 0.98, and from 0.28 to 0.84, indicating their clinical usefulness. The MIL and combined models highlight their performance in predicting preoperative lymphovascular invasion in gastric cancer, offering valuable insights for clinicians in selecting appropriate treatment options for gastric cancer patients.

Computed Tomography (CT) is a widely utilized imaging modality in clinical settings. Using densely acquired rotational X-ray arrays, CT can capture 3D spatial features. However, it is confronted with challenged such as significant time consumption and high radiation exposure. CT reconstruction methods based on sparse-view X-ray images have garnered substantial attention from researchers as they present a means to mitigate costs and risks. In recent years, diffusion models, particularly the Latent Diffusion Model (LDM), have demonstrated promising potential in the domain of 3D CT reconstruction. Nonetheless, due to the substantial differences between the 2D latent representation of X-ray modalities and the 3D latent representation of CT modalities, the vanilla LDM is incapable of achieving effective alignment within the latent space. To address this issue, we propose the Consistent Latent Space Diffusion Model (CLS-DM), which incorporates cross-modal feature contrastive learning to efficiently extract latent 3D information from 2D X-ray images and achieve latent space alignment between modalities. Experimental results indicate that CLS-DM outperforms classical and state-of-the-art generative models in terms of standard voxel-level metrics (PSNR, SSIM) on the LIDC-IDRI and CTSpine1K datasets. This methodology not only aids in enhancing the effectiveness and economic viability of sparse X-ray reconstructed CT but can also be generalized to other cross-modal transformation tasks, such as text-to-image synthesis. We have made our code publicly available at https://anonymous.4open.science/r/CLS-DM-50D6/ to facilitate further research and applications in other domains.

We present a novel framework for CBCT-to-MDCT translation, grounded in the Schrodinger Bridge (SB) formulation, which integrates GAN-derived priors with human-guided conditional diffusion. Unlike conventional GANs or diffusion models, our approach explicitly enforces boundary consistency between CBCT inputs and pseudo targets, ensuring both anatomical fidelity and perceptual controllability. Binary human feedback is incorporated via classifier-free guidance (CFG), effectively steering the generative process toward clinically preferred outcomes. Through iterative refinement and tournament-based preference selection, the model internalizes human preferences without relying on a reward model. Subtraction image visualizations reveal that the proposed method selectively attenuates shade artifacts in key anatomical regions while preserving fine structural detail. Quantitative evaluations further demonstrate superior performance across RMSE, SSIM, LPIPS, and Dice metrics on clinical datasets -- outperforming prior GAN- and fine-tuning-based feedback methods -- while requiring only 10 sampling steps. These findings underscore the effectiveness and efficiency of our framework for real-time, preference-aligned medical image translation.

Survival of patients with high-risk hepatoblastoma remains low, and early identification of high-risk hepatoblastoma is critical. To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting high-risk hepatoblastoma. Clinical and CECT imaging data were retrospectively collected from 162 children who were treated at our hospital and pathologically diagnosed with hepatoblastoma. Patients were categorized into high-risk and non-high-risk groups according to the Children's Hepatic Tumors International Collaboration - Hepatoblastoma Study (CHIC-HS). Subsequently, these cases were randomized into training and test groups in a ratio of 7:3. The region of interest (ROI) was first outlined in the pre-treatment venous images, and subsequently the best features were extracted and filtered, and the radiomics model was built by three machine learning methods: namely, Bagging Decision Tree (BDT), Logistic Regression (LR), and Stochastic Gradient Descent (SGD). The AUC, 95 % CI, and accuracy of the model were calculated, and the model performance was evaluated by the DeLong test. The AUCs of the Bagging decision tree model were 0.966 (95 % CI: 0.938-0.994) and 0.875 (95 % CI: 0.77-0.98) for the training and test sets, respectively, with accuracies of 0.841 and 0.816,respectively. The logistic regression model has AUCs of 0.901 (95 % CI: 0.839-0.963) and 0.845 (95 % CI: 0.721-0.968) for the training and test sets, with accuracies of 0.788 and 0.735, respectively. The stochastic gradient descent model has AUCs of 0.788 (95 % CI: 0.712 -0.863) and 0.742 (95 % CI: 0.627-0.857) with accuracies of 0.735 and 0.653, respectively. CECT-based imaging histology identifies high-risk hepatoblastomas and may provide additional imaging biomarkers for identifying high-risk hepatoblastomas.

Immunotherapy has become a cornerstone in the treatment of advanced gastric cancer (GC). However, identifying reliable predictive biomarkers remains a considerable challenge. This study demonstrates the potential of integrating multimodal baseline data, including computed tomography scan images and digital H&E-stained pathology images, with biological interpretation to predict the response to immunotherapy-based combination therapy using a multicenter cohort of 298 GC patients. By employing seven machine learning approaches, we developed a radiopathomics signature (RPS) to predict treatment response and stratify prognostic risk in GC. The RPS demonstrated area under the receiver-operating-characteristic curves (AUCs) of 0.978 (95% CI, 0.950-1.000), 0.863 (95% CI, 0.744-0.982), and 0.822 (95% CI, 0.668-0.975) in the training, internal validation, and external validation cohorts, respectively, outperforming conventional biomarkers such as CPS, MSI-H, EBV, and HER-2. Kaplan-Meier analysis revealed significant differences of survival between high- and low-risk groups, especially in advanced-stage and non-surgical patients. Additionally, genetic analyses revealed that the RPS correlates with enhanced immune regulation pathways and increased infiltration of memory B cells. The interpretable RPS provides accurate predictions for treatment response and prognosis in GC and holds potential for guiding more precise, patient-specific treatment strategies while offering insights into immune-related mechanisms.

The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire. Although recent foundation models (e.g., segment anything model, SAM) can utilize sparse annotations to reduce annotation costs, segmentation tasks involving organs and tissues with blurred boundaries remain challenging. To address this issue, we propose a region uncertainty estimation framework for Computed Tomography (CT) image segmentation using noisy labels. Specifically, we propose a sample-stratified training strategy that stratifies samples according to their varying quality labels, prioritizing confident and fine-grained information at each training stage. This sample-to-voxel level processing enables more reliable supervision information to propagate to noisy label data, thus effectively mitigating the impact of noisy annotations. Moreover, we further design a boundary-guided regional uncertainty estimation module that adapts sample hierarchical training to assist in evaluating sample confidence. Experiments conducted across multiple CT datasets demonstrate the superiority of our proposed method over several competitive approaches under various noise conditions. Our proposed reliable label propagation strategy not only significantly reduces the cost of medical image annotation and robust model training but also improves the segmentation performance in scenarios with imperfect annotations, thus paving the way towards the application of medical segmentation foundation models under low-resource and remote scenarios. Code will be available at https://github.com/KHan-UJS/NoisyLabel.

Food insecurity (FI) is associated with increased adiposity and obesity-related medical conditions, and body composition can affect metabolic risk. Bariatric surgery effectively treats obesity and metabolic diseases. The association of FI with baseline computerized tomography (CT)-based body composition and bariatric surgery outcomes was investigated in this exploratory study. Fifty-four retrospectively identified adults had bariatric surgery, preoperative CT scan from 2017 to 2019, completed a six-item food security survey, and had body composition measured by bioelectrical impedance analysis (BIA). Skeletal muscle, visceral fat, and subcutaneous fat areas were determined from abdominal CT and normalized to published age, sex, and race reference values. Anthropometric data, related medical conditions, and medications were collected preoperatively, and at 6 months and at 12 months postoperatively. Patients were stratified into food security (FS) or FI based on survey responses. Fourteen (26%) patients were categorized as FI. Patients with FI had lower skeletal muscle area and higher subcutaneous fat area than patients with FS on baseline CT exam (p < 0.05). There was no difference in baseline BIA between patients with FS and FI. The two groups had similar weight loss, reduction in obesity-related medications, and healthcare utilization following bariatric surgery at 6 and 12 months postoperatively. Patients with FI had higher subcutaneous fat and lower skeletal muscle than patients with FS by baseline CT exam, findings which were not detected by BIA. CT analysis enabled by an artificial intelligence workflow offers more precise and detailed body composition data.

Accurate liver vessel segmentation is essential for effective liver surgery pre-planning, and reducing surgical risks since it enables the precise localization and extensive assessment of complex vessel structures. Manual liver vessel segmentation is a time-intensive process reliant on operator expertise and skill. The complex, tree-like architecture of hepatic and portal veins, which are interwoven and anatomically variable, further complicates this challenge. This study addresses these challenges by proposing the UNETR (U-Net Transformers) architecture for the multi-class segmentation of portal and hepatic veins in liver CT images. UNETR leverages a transformer-based encoder to effectively capture long-range dependencies, overcoming the limitations of convolutional neural networks (CNNs) in handling complex anatomical structures. The proposed method was evaluated on contrast-enhanced CT images from the IRCAD as well as a locally dataset developed from a hospital. On the local dataset, the UNETR model achieved Dice coefficients of 49.71% for portal veins, 69.39% for hepatic veins, and 76.74% for overall vessel segmentation, while reaching Dice coefficients of 62.54% for vessel segmentation on the IRCAD dataset. These results highlight the method's effectiveness in identifying complex vessel structures across diverse datasets. These findings underscore the critical role of advanced architectures and precise annotations in improving segmentation accuracy. This work provides a foundation for future advancements in automated liver surgery pre-planning, with the potential to enhance clinical outcomes significantly. The implementation code is available on GitHub: https://github.com/saharsarkar/Multiclass-Vessel-Segmentation .

Retrospective study. To develop a deep learning (DL) model to predict bone cement leakage (BCL) subtypes during percutaneous kyphoplasty (PKP) using preoperative computed tomography (CT) as well as employing multicenter data to evaluate the effectiveness and generalizability of the model. DL excels at automatically extracting features from medical images. However, there is a lack of models that can predict BCL subtypes based on preoperative images. This study included an internal dataset for DL model training, validation, and testing as well as an external dataset for additional model testing. Our model integrated a segment localization module based on vertebral segmentation via three-dimensional (3D) U-Net with a classification module based on 3D ResNet-50. Vertebral level mismatch rates were calculated, and confusion matrixes were used to compare the performance of the DL model with that of spine surgeons in predicting BCL subtypes. Furthermore, the simple Cohen's kappa coefficient was used to assess the reliability of spine surgeons and the DL model against the reference standard. A total of 901 patients containing 997 eligible segments were included in the internal dataset. The model demonstrated a vertebral segment identification accuracy of 96.9%. It also showed high area under the curve (AUC) values of 0.734-0.831 and sensitivities of 0.649-0.900 for BCL prediction in the internal dataset. Similar favorable AUC values of 0.709-0.818 and sensitivities of 0.706-0.857 were observed in the external dataset, indicating the stability and generalizability of the model. Moreover, the model outperformed nonexpert spine surgeons in predicting BCL subtypes, except for type II. The model achieved satisfactory accuracy, reliability, generalizability, and interpretability in predicting BCL subtypes, outperforming nonexpert spine surgeons. This study offers valuable insights for assessing osteoporotic vertebral compression fractures, thereby aiding preoperative surgical decision-making. 3.

This study investigates the effectiveness of U-Net architectures integrated with various convolutional neural network (CNN) backbones for automated lung cancer detection and segmentation in chest CT images, addressing the critical need for accurate diagnostic tools in clinical settings. A balanced dataset of 832 chest CT images (416 cancerous and 416 non-cancerous) was preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and resized to 128x128 pixels. U-Net models were developed with three CNN backbones: ResNet50, VGG16, and Xception, to segment lung regions. After segmentation, CNN-based classifiers and hybrid models combining CNN feature extraction with traditional machine learning classifiers (Support Vector Machine, Random Forest, and Gradient Boosting) were evaluated using 5-fold cross-validation. Metrics included accuracy, precision, recall, F1-score, Dice coefficient, and ROC-AUC. U-Net with ResNet50 achieved the best performance for cancerous lungs (Dice: 0.9495, Accuracy: 0.9735), while U-Net with VGG16 performed best for non-cancerous segmentation (Dice: 0.9532, Accuracy: 0.9513). For classification, the CNN model using U-Net with Xception achieved 99.1 percent accuracy, 99.74 percent recall, and 99.42 percent F1-score. The hybrid CNN-SVM-Xception model achieved 96.7 percent accuracy and 97.88 percent F1-score. Compared to prior methods, our framework consistently outperformed existing models. In conclusion, combining U-Net with advanced CNN backbones provides a powerful method for both segmentation and classification of lung cancer in CT scans, supporting early diagnosis and clinical decision-making.