Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance. METHODS: This study enrolled 311 patients from two centers with pathologically confirmed of GC. we employed a deep learning model, U-Mamba, to obtain fully automatic segmentation of the spleen CT images. Subsequently, radiomics features and deep learning features were extracted from the entire spleen CT images, and significant features were identified through dimensionality reduction. The clinical features, radiomic features, and deep learning features were organized and integrated, and five machine learning methods were employed to develop 15 predictive models. Ultimately, the model exhibiting superior performance was presented in the form of a nomogram. RESULTS: A total of 18 radiomics features, 30 deep learning features, and 1 clinical features were deemed valuable. The DLRA model demonstrated superior discriminative capacity relative to other models. A nomogram was constructed based on the logistic clinical model to facilitate the usage and verification of the clinical model. CONCLUSION: Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting serosal invasion in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.