# Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions

## TL;DR

This study uses radiomics to analyze both tumor and surrounding tissue features to better predict risk in thymic tumors, showing that including peri-tumoral data improves accuracy.

## Contribution

The study introduces a novel multimodal radiomics approach integrating intra- and peri-tumoral features to enhance tumor risk prediction.

## Key findings

- The IntraPeri1mm model achieved the highest AUC of 0.837 with strong sensitivity and specificity.
- SHAP analysis identified key predictive features like peri_log_sigma_2_0_mm 3D_firstorder RootMeanSquared.
- DeLong’s test and DCA confirmed the model's statistical significance and clinical applicability.

## Abstract

This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.

A total of 133 patients, including 128 with thymomas and 5 with thymic carcinomas, were ultimately enrolled in this study. Based on the high- and low-risk classification, the cohort was divided into a training set (n = 93) and a testing set (n = 40) for subsequent analysis.Based on imaging data from these 133 patients, multiple radiomics prediction models integrating intra-tumoral and peritumoral features were developed. The data were sourced from patients treated at the Affiliated Hospital of Guangdong Medical University between 2015 and 2023, with all imaging obtained through preoperative CT scans. Radiomics feature extraction involved three primary categories: first-order features, shape features, and high-order features. Initially, the tumor’s region of interest (ROI) was manually delineated using ITK-SNAP software. A custom Python algorithm was then used to automatically expand the peri-tumoral area, extracting features within 1 mm, 2 mm, and 3 mm zones surrounding the tumor. Additionally, considering the multimodal nature of the imaging data, image fusion techniques were incorporated to further enhance the model’s ability to capture the tumor microenvironment. To build the radiomics models, selected features were first standardized using z-scores. Initial feature selection was performed using a t-test (p < 0.05), followed by Spearman correlation analysis to remove redundancy by retaining only one feature from each pair with a correlation coefficient ≥ 0.90. Subsequently, hierarchical clustering and the LASSO algorithm were applied to identify the most predictive features. These selected features were then used to train machine learning models, which were optimized on the training dataset and assessed for predictive performance. To further evaluate the effectiveness of these models, various statistical methods were applied, including DeLong’s test, NRI, and IDI, to compare predictive differences among models. Decision curve analysis (DCA) was also conducted to assess the clinical applicability of the models.

The results indicate that the IntraPeri1mm model performed the best, achieving an AUC of 0.837, with sensitivity and specificity at 0.846 and 0.84, respectively, significantly outperforming other models. SHAP value analysis identified several key features, such as peri_log_sigma_2_0_mm 3D_firstorder RootMeanSquared and intra_wavelet_LLL_firstorder Skewness, which made substantial contributions to the model’s predictive accuracy. NRI and IDI analyses further confirmed the model’s superior clinical applicability, and the DCA curve demonstrated robust performance across different thresholds. DeLong’s test highlighted the statistical significance of the IntraPeri1mm model, underscoring its potential utility in radiomics research.

Overall, this study provides a new perspective on tumor risk assessment, highlighting the importance of peri-tumoral features in the analysis of the tumor microenvironment. It aims to offer valuable insights for the development of personalized treatment plans.

Not applicable.

The online version contains supplementary material available at 10.1186/s12880-025-01790-2.

• By using multimodal fusion and integrating both intra- and peri-tumoral features across different peri-tumoral ranges (1 mm, 2 mm, 3 mm), the study aims to improve the predictive accuracy of the model.

• SHAP value analysis was performed to assess the contribution of each feature to the model’s predictions, providing interpretability for individual features and enhancing the clinical applicability of the model.

The online version contains supplementary material available at 10.1186/s12880-025-01790-2.