Prediction of IDH and 1p/19q Status in Gliomas Based on Dual Structural Feature Exploration and Alignment Network

Noninvasively predicting the status of isocitrate dehydrogenase (IDH) and chromosome arms 1p/19q preoperatively on multisequence magnetic resonance imaging (MRI) images is helpful for prognosis and optimal therapy planning of patients with gliomas. However, effectively learning discriminative features from MRI images for predicting IDH mutation and 1p/19q codeletion status remains challenging due to the high heterogeneity of gliomas. A dual structural feature exploration and alignment network (DSFEAnet) was proposed to effectively explore representative features associated with the intratumoral and marginal heterogeneity of gliomas for accurate prediction. First, a match and mismatch feature extraction (MMFE) module was introduced to extract image structural features related to intratumoral heterogeneity, such as information associated with tumor core localization and T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign. Second, a graph-based geometry exploration (GGE) module was developed to explore graph structural features related to marginal heterogeneity. In this module, the vertex associations and variations perpendicularly along a 3-D tumor surface were integrated as a graph, which can effectively perceive changes in locations, sizes, and marginal textures of gliomas, thus enhancing the feature representational ability to describe glioma heterogeneity. Finally, a dual structural feature alignment (DSFA) module was incorporated to narrow the gaps among intra- and interstructural features. It can adaptively align and fuse different features and thus further improve the overall prediction performance. The proposed DSFEAnet was evaluated using a multicenter dataset, and its robustness was demonstrated on an independent clinical dataset. Specifically, preoperative MRI images of 560 glioma samples were collected from publicly available The Cancer Imaging Archive (TCIA) ( $n =203$ , age: 51.82 (15.21) years, male/female: 109/94, IDH-mutant/wild-type: 90/113, 1p/19q-codeleted/noncodeleted: 27/176), Nanfang hospital ( $n =136$ , age: 41.96 (12.29) years, male/female: 80/56, IDH-mutant/wild-type: 53/83, 1p/19q-codeleted/noncodeleted: 31/105), and Zhujiang hospital ( $n =221$ , age: 43.82 (17.36) years, male/female: 134/87, IDH-mutant/wild-type: 94/127, 1p/19q-codeleted/noncodeleted: 34/187). Our DSFEAnet achieved an AUC of 87.72% for IDH mutation status prediction and an AUC of 80.52% for 1p/19q codeletion status prediction in the Nanfang hospital dataset. Finally, the interpretability of the proposed modules was assessed to highlight the effectiveness of our method. Overall, the DSFEAnet exhibits great potential for predicting IDH mutation and 1p/19q codeletion status.