ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 cases as a train cohort and 20 cases as a test cohort). The regions of interest (ROIs) were annotated accordingly. In MRI processing, the ROI was further divided into clusters to extract habitat radiomics features. For whole slide imaging (WSI), the ROI was cropped into equal‐sized image patches for weakly supervised learning and deep learning using various network architectures. The optimal model architecture was selected, and pathological features were extracted. After feature selection, four independent models were constructed: habitat radiomics model, pathomics‐based model, clinical model, and combined model integrating all information. Model performance was evaluated using the concordance index (C‐index) and the area under the receiver operating characteristic curve (AUC). Results The combined model demonstrated the best predictive performance, with a C‐index of 0.883 and an AUC of 0.965 in the train cohort. In the test cohort, the C‐index was 0.840, and the AUC was 0.927. Based on the combined model, patients with high‐grade gliomas were divided into high‐risk and low‐risk groups, with median progression‐free survival (mPFS) of 9 months and 77 months, respectively ( p < 0.001). Conclusion Compared with the habitat radiomics model or the pathomics‐based model alone, the combined model can better predict the risk of progression in high‐grade gliomas and provides valuable guidance for personalized treatment of high‐grade gliomas.
Zhu et al. (Sun,) studied this question.