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.

This study aimed to develop and validate a positron emission tomography/computed tomography (PET/CT)-based nomogram for individualized prediction of 3-year progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC). A total of 128 patients with NPC who underwent pretreatment PET/CT imaging were retrospectively enrolled. Radiomic features were extracted from PET and CT images, and clinical variables were collected. Feature selection was performed using univariate Cox analysis, multivariable Cox analysis, and the least absolute shrinkage and selection operator regression to identify independent predictors. A nomogram was constructed by integrating CT-Radscore, PET-Radscore, and key clinical variables. Model performance in predicting 3-year PFS was evaluated using the concordance index, time-dependent area under the receiver operating characteristic curve, Kaplan–Meier survival curves, calibration curves, and decision curve analysis in both training and validation cohorts. The nomogram incorporating CT-Radscore, PET-Radscore, and lactate dehydrogenase demonstrated robust predictive ability for PFS in NPC. The area under the receiver operating characteristic curve for predicting 3-year PFS was 0.813 in the training cohort and 0.739 in the validation cohort. The corresponding concordance index values were 0.705 and 0.635, respectively. Calibration plots and decision curve analysis confirmed the nomogram’s reliability and clinical utility. A PET/CT-based radiomics nomogram (CT-Radscore, PET-Radscore, and lactate dehydrogenase) achieved robust prediction of 3-year PFS and enhanced prognostic stratification in NPC. External validation in larger multi-center cohorts is needed due to the single-center retrospective design and moderate sample size.