Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these challenges, we present a large-scale dataset and introduce a novel framework for multi-modal feature fusion aimed at enhancing the accuracy of survival prediction. The dataset comprises 3D CT images and corresponding clinical records from NSCLC patients treated with immune checkpoint inhibitors (ICI), along with progression-free survival (PFS) and overall survival (OS) data. We further propose a cross-modality masked learning approach for medical feature fusion, consisting of two distinct branches, each tailored to its respective modality: a Slice-Depth Transformer for extracting 3D features from CT images and a graph-based Transformer for learning node features and relationships among clinical variables in tabular data. The fusion process is guided by a masked modality learning strategy, wherein the model utilizes the intact modality to reconstruct missing components. This mechanism improves the integration of modality-specific features, fostering more effective inter-modality relationships and feature interactions. Our approach demonstrates superior performance in multi-modal integration for NSCLC survival prediction, surpassing existing methods and setting a new benchmark for prognostic models in this context.

Functional brain lateralization, a key feature of the human brain that shows alterations in various mental disorders, remains poorly understood in panic disorder (PD), and its investigation may provide valuable insights into the neurobiological underpinnings of psychiatric conditions. This study investigates hemispheric lateralization in drug-naive patients with PD before and after treatment, explores its associations with gene expression and neurotransmitter profiles, and examines its utility for diagnosis and treatment outcome prediction. Fifty-eight patients and 85 healthy controls (HCs) were enrolled. Clinical assessments and resting-state functional magnetic resonance imaging scans were conducted before and after a 4-week paroxetine monotherapy. Intra-hemispheric functional connectivity strength (FCS), inter-hemispheric FCS, and parameter of asymmetry (PAS) were calculated. Imaging-transcriptomic and imaging-neurotransmitter correlation analyses were conducted. PAS was used in machine learning models for classification and treatment outcome prediction. Compared with HCs, patients exhibited enhanced intra-hemispheric FCS and decreased PAS in the caudate nucleus/pallidum and thalamus, with associated genes, dopamine and serotonin receptor densities, and vesicular acetylcholine transporter densities linking these lateralization alterations to neural signaling and synaptic function. FCS and PAS results were consistent across different correlation thresholds (0.15, 0.2, and 0.25). No significant changes in FCS or PAS were observed following treatment. PAS demonstrated excellent performance in classification (accuracy = 75.52 %) and treatment outcomes prediction (r = 0.763). Hemispheric lateralization in the cortico-basal ganglia-thalamic network was significantly altered in patients with PD, with these changes linked to disruptions in genes and neurotransmitter profiles which are associated with neural signal transduction and synaptic function. PAS shows promise as a biomarker for PD diagnosis and treatment outcome prediction.

In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In this research, we propose Deep Brain Net, a novel deep learning system designed to optimize performance in the detection of brain tumors. The model integrates the strengths of two advanced neural network architectures which are EfficientNetB0 and ResNet50, combined with transfer learning to improve generalization and reduce training time. The EfficientNetB0 architecture enhances model efficiency by utilizing mobile inverted bottleneck blocks, which incorporate depth wise separable convolutions. This design significantly reduces the number of parameters and computational cost while preserving the ability of models to learn complex feature representations. The ResNet50 architecture, pre trained on large scale datasets like ImageNet, is fine tuned for brain tumor classification. Its use of residual connections allows for training deeper networks by mitigating the vanishing gradient problem and avoiding performance degradation. The integration of these components ensures that the proposed system is both computationally efficient and highly accurate. Extensive experiments performed on publicly available MRI datasets demonstrate that Deep Brain Net consistently outperforms existing state of the art methods in terms of classification accuracy, precision, recall, and computational efficiency. The result is an accuracy of 88 percent, a weighted F1 score of 88.75 percent, and a macro AUC ROC score of 98.17 percent which demonstrates the robustness and clinical potential of Deep Brain Net in assisting radiologists with brain tumor diagnosis.

When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradients for differential privacy, work well on large datasets like MNIST and CIFAR-100, but fail on small, imbalanced medical datasets like HAM10000. This is because the imbalanced distribution causes gradients from minority classes to be clipped and lose crucial information, while majority classes dominate. This leads the model to fall into suboptimal solutions early. To address this, we propose SAD-DPSGD, which uses a linear decaying mechanism for noise and clipping thresholds. By allocating more privacy budget and using higher clipping thresholds in the initial training phases, the model avoids suboptimal solutions and enhances performance. Experiments show that SAD-DPSGD outperforms Auto-DPSGD on HAM10000, improving accuracy by 2.15% under $\epsilon = 3.0$ , $\delta = 10^{-3}$.

To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively. Total 350 patients with PMA (272 from Xinqiao Hospital of Army Medical University and 78 from Daping Hospital of Army Medical University) were stratified and randomly divided into training and test cohorts in a 7:3 ratio. The tumor consistency was classified as soft or firm. Clinicoradiological predictors were examined utilizing univariate and multivariate regression analyses. Radiomics features were selected employing the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. Logistic regression (LR) and random forest (RF) classifiers were applied to construct the models. Receiver operating characteristic (ROC) curves and decision curve analyses (DCA) were performed to compare and validate the predictive capacities of the models. A comparative study of the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) was performed. The Shapley additive explanation (SHAP) was applied to investigate the optimal model's interpretability. The combined model predicted the PMAs' consistency more effectively than the clinicoradiological and radiomics models. Specifically, the LR-combined model displayed optimal prediction performance (test cohort: AUC = 0.913; ACC = 0.840). The SHAP-based explanation of the LR-combined model suggests that the wavelet-transformed and Laplacian of Gaussian (LoG) filter features extracted from T₂WI and CE-T₁WI occupy a dominant position. Meanwhile, the skewness of the original first-order features extracted from T₂WI (T₂WI_original_first-order_Skewness) demonstrated the most substantial contribution. An interpretable machine learning model incorporating clinicoradiological predictors and multiparametric MRI (mpMRI)-based radiomics features may predict PMAs consistency, enabling tailored and precise therapies for patients with PMA.

In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However, the complex structure of the hip joint, high technical operation requirements and relatively long learning curve have hindered the popularization and development of hip arthroscopy in China. Therefore, on the one hand, it is necessary to promote the research and training of standardized techniques for the diagnosis of hip disease and the treatment of arthroscopic surgery, so as to improve the safety, effectiveness and popularization of the technology. On the other hand, our organization proactively leverages cutting-edge digitalization and intelligentization technologies, including medical image digitalization, medical big data analytics, artificial intelligence, surgical navigation and robotic control, virtual reality, telemedicine, and 5G communication technology. We conduct a range of innovative research and development initiatives such as intelligent-assisted diagnosis of hip diseases, digital preoperative planning, surgical intelligent navigation and robotic procedures, and smart rehabilitation solutions. These efforts aim to facilitate a digitalization and intelligentization leap in technology and continuously enhance the precision of diagnosis and treatment. In conclusion, standardization promotes the homogenization of diagnosis and treatment, while digitalization and intelligentization facilitate the precision of operations. The synergy of the two lays the foundation for personalized diagnosis and treatment and continuous innovation, ultimately driving the rapid development of hip arthroscopy technology.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound (US) for outcome prediction is not yet known. We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adverse liver-related outcomes in MASLD. Retrospective cohort study of adult MASLD patients biopsied between 2010-2021 at one of two Canadian tertiary care centers. Random forest was used to create predictive models for outcomes-hepatic decompensation, liver-related outcomes (decompensation, hepatocellular carcinoma (HCC), liver transplant, and liver-related mortality), HCC, liver-related mortality, F3-4, and fibrotic metabolic dysfunction-associated steatohepatitis (MASH). Diagnostic performance was assessed using area under the curve (AUC). 457 MASLD patients were included with 44.9% F3-4, diabetes prevalence 31.6%, 53.8% male, mean age 49.2 and BMI 32.8 kg/m². 6.3% had an adverse liver-related outcome over mean 43 months follow-up. AUC for ML predictive models were-hepatic decompensation 0.90(0.79-0.98), liver-related outcomes 0.87(0.76-0.96), HCC 0.72(0.29-0.96), liver-related mortality 0.79(0.31-0.98), F3-4 0.83(0.76-0.87), and fibrotic MASH 0.74(0.65-0.85). Biochemical and clinical variables had greatest feature importance overall, compared to US parameters. FIB-4 and AST:ALT ratio were highest ranked biochemical variables, while age was the highest ranked clinical variable. ML models based on clinical, biochemical, and US-based variables accurately predict adverse MASLD outcomes in this multi-centre cohort. Overall, biochemical variables had greatest feature importance. US-based features were not substantial predictors of outcomes in this study.

Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcomes across diverse medical disciplines. In recent years, the advent of deep learning methodologies has significantly transformed this domain by facilitating notable advancements in image analysis and classification endeavors. This paper provides a thorough overview of the application of deep learning techniques in biomedical image classification, encompassing various types of healthcare data, including medical images derived from modalities such as mammography, histopathology, and radiology. A detailed discourse on deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced models such as generative adversarial networks (GANs), is presented. Additionally, we delineate the distinctions between supervised, unsupervised, and reinforcement learning approaches, along with their respective roles within the context of biomedical imaging. This study systematically investigates 50 deep learning methodologies employed in the healthcare sector, elucidating their effectiveness in various tasks, including disease detection, image segmentation, and classification. It particularly emphasizes models that have been trained on publicly available datasets, thereby highlighting the significant role of open-access data in fostering advancements in AI-driven healthcare innovations. Furthermore, this review accentuates the transformative potential of deep learning in the realm of biomedical image analysis and delineates potential avenues for future research within this rapidly evolving field.

With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive method for identifying biomarkers relevant to cancer therapy. Liquid biopsy (LB) has further revolutionized cancer diagnostics by detecting circulating tumor DNA (ctDNA), enabling real-time molecular profiling. This study explores the integration of radiomics and LB to predict genomic alterations in solid tumors, including lung, colon, pancreatic, and prostate cancers. A retrospective study was conducted on 418 patients from the STING trial (NCT04932525), all of whom underwent both LB and CT imaging. Predictive models were developed using an XGBoost logistic classifier, with statistical analysis performed to compare tumor volumes, lesion counts, and affected organs across molecular subtypes. Performance was evaluated using area under the curve (AUC) values and cross-validation techniques. Radiomic models demonstrated moderate-to-good performance in predicting genomic alterations. KRAS mutations were best identified in pancreatic cancer (AUC=0.97), while moderate discrimination was noted in lung (AUC=0.66) and colon cancer (AUC=0.64). EGFR mutations in lung cancer were detected with an AUC of 0.74, while BRAF mutations showed good discriminatory ability in both lung (AUC=0.79) and colon cancer (AUC=0.76). In the radiomics predictive model, AR mutations in prostate cancer showed limited discrimination (AUC = 0.63). This study highlights the feasibility of integrating radiomics and LB for non-invasive genomic profiling in solid tumors, demonstrating significant potential in patient stratification and personalized oncology care. While promising, further prospective validation is required to enhance the generalizability of these models.

Medical self-supervised learning eliminates the reliance on labels, making feature extraction simple and efficient. The intricate design of pretext tasks in single-modal self-supervised analysis presents challenges, however, compounded by an excessive dependency on data augmentation, leading to a bottleneck in medical self-supervised learning research. Consequently, this paper reanalyzes the feature learnability introduced by data augmentation strategies in medical image self-supervised learning. We introduce an adaptive self-supervised learning data augmentation method from the perspective of batch fusion. Moreover, we propose a conv embedding block for learning the incremental representation between these batches. We tested 5 fused data tasks proposed by previous researchers and it achieved a linear classification protocol accuracy of 94.25% with only 150 self-supervised feature training in Vision Transformer(ViT), which is the best among the same methods. With a detailed ablation study on previous augmentation strategies, the results indicate that the proposed medical data augmentation strategy in this paper effectively represents ultrasound data features in the self-supervised learning process. The code and weights could be found at here.