Pneumothorax (PTX) is a common acute condition in thoracic surgery and respiratory medicine, and the accurate diagnosis and quantitative evaluation rely on high-quality three-dimensional (3D) chest imaging that involves high radiation exposure and costs. In this study, we propose PTX2CT-GAN, a generative adversarial learning-based framework that reconstructs 3D chest CT images from a single X-ray of pneumothorax. The framework integrates a Reconstruction Feature Enhancement Module (RFEM) to improve feature extraction and structural representation during CT reconstruction. Experiments on the MY-PTX-998 dataset demonstrate that the proposed PTX2CT-GAN outperforms related methods both on quantitative and qualitative evaluations. This work provides a low-radiation, low-cost, and high-quality alternative for CT imaging in pneumothorax diagnosis, especially in resource-limited clinical environments.

Aortic Dissection (AD) is a life-threatening disease that can be rapidly screened by using deep learning methods. However, deep learning model training requires a large amount of manual annotation of data. To improve the annotation efficiency and accuracy, we propose LABEL-SAM, a semi-automatic interactive segmentation algorithm designed to efficiently annotate AD in 3D computed tomography angiography (CTA) images. By requiring minimal user input—only points and bounding boxes on the first and last slices—LABEL-SAM automatically generates segmentation prompts for intermediate slices, reducing the manual workload. In addition, we propose a bidirectional prediction weighting method and a fine-tuning strategy tailored to AD data, further enhancing segmentation accuracy. Furthermore, LABEL-SAM is implemented as a plug-and-play plugin for the 3D Slicer software. Experimental results on both external and internal datasets demonstrate the method’s superior performance, improving annotation accuracy and efficiency. The code will be available at https://github.com/wenjiecai/LABEL-SAM. The demonstration video is now available at https://www.youtube.com/watch?v=R3Fzgl1b4JQ.

Current spatial transcriptomics (ST) technology can integrate stained pathological slides with RNA sequencing, providing precise information on gene expression and cell types. However, the unique handling of pathological slides required by ST technology can lead to image quality issues, impacting model performance. To address this challenge, we applied Structure Preserving Color Normalization (SPCN), based on the Beer-Lambert law. Here, we use a pancreatic ductal adenocarcinoma (PDAC) dataset with class-level and gene-level annotations, and introduced Gene Feature Align (GFA) Loss to align classification features with gene expression, resulting in clearer decision boundaries. Our approach significantly enhances model performance on this dataset, offering novel solutions for spatial transcriptomics and paired pathological images, while contributing to limited research on PDAC slide cell distribution prediction through deep neural networks.

Aortic Dissection (AD) is a life-threatening disease. As such, it’s vital to make clinical strategies by accurate segmentation of AD from Computed Tomography Angiography (CTA) images. Due to the heterogeneous morphology of AD in CTA images and the influence of image noise, inaccurate segmentation results are often produced, making it challenging to accurately identify the contours of AD. To meet these challenges, we propose a novel automatic AD segmentation model DETR-SAM based on the Segment Anything Model(SAM). Our model consists of a deformable DETR object detector module with multi-scale feature fusion and a fine-tuned SAM module for refining the segmentation contours. The object detector module effectively fuses the multi-scale image features extracted by the image encoder of SAM with the features obtained from the DETR backbone CNN network. The fused multi-scale features generate multiple prompts rich in geometric shape knowledge through multiple regression prediction heads to guide the SAM model to refine the segmentation boundaries and enhance the recognition of contours. Meanwhile, a fine-tuning strategy using these prompts is proposed to enhance the SAM model’s understanding of AD features. This strategy enables our DETR-SAM model with a 2D segmentation network architecture to surpass the performance of mainstream 3D segmentation algorithms. We have evaluated DETR-SAM on the ImageTBAD dataset and our internal dataset. The results showed that the accuracy of our model is better than the current state-of-the-art 2D and 3D segmentation methods in all four indicators. In addition, the effectiveness of the feature fusion module, hint generation method and fine-tuning strategy are further verified by ablation experiments. Our DETR-SAM method is helpful to a certain extent in the diagnosis and auxiliary treatment of AD in different clinical situations and lays a foundation for further research and clinical application.

MOTIVATION: Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS: Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION: The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.