A Spatial-Transformation-Based Causality-Enhanced Model for Glioblastoma Progression Diagnosis

Abstract

Differentiation between pseudoprogression and true tumor progression of glioblastoma (GBM) is crucial for choosing appropriate management strategies and increasing the chances of patient survival. Currently, there is a lack of noninvasive and effective methods in clinic for GBM progression diagnosis. Here, we propose an automated early diagnosis method based on diffusion tensor imaging (DTI) with a high potential for this diagnosis. A primary challenge for intelligent diagnostic methods lies in the limited accuracy and stability caused by data insufficiency and the fine-grained nature of diagnostic tasks. To address this challenge, we develop a spatial-transformation-based causality-enhanced model (ST-CEM). This model jointly improves data diversity and the effective utilization of clinically significant discriminative information. Specifically, first, a texture diverse augmentation scheme is designed based on a spatial transformation, which allows for greater texture diversification in the augmented data. Subsequently, an interference information contrastive strategy is developed, where nonlesion features that may introduce interference are actively extracted and decoupled with lesion features. Finally, a causality-enhanced mechanism is introduced to highlight the decoupled lesion features, thereby improving the diagnostic stability of the model. Extensive experiments verified the effectiveness of our model in diagnosis of GBM progression under small-sample conditions. The proposed model achieved an accuracy of 84.1%, precision of 85.8%, and recall of 90.3%, all of which outperform the existing works. Moreover, it demonstrated competitive performance on an additional lung nodule classification dataset.