Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas

Low-grade gliomas (LGGs) are invasive neoplasms that arise in the cerebral hemispheres of adults including diffuse low-grade and intermediate-grade gliomas (World Health Organization [WHO] grades II and III) [1]. The fifth edition of the WHO Classification of Tumors of the Central Nervous System divides adult-type gliomas into three subtypes based on molecular markers: (1) astrocytoma, isocitrate dehydrogenase (IDH) -mutant, (2) oligodendroglioma, IDH-mutant, and 1p/19q-codeleted, and (3) glioblastoma, IDH-wildtype [2].

Studies have shown that oligodendroglioma has the best prognosis between these three categories [3]. In addition, studies have illustrated that even small residual tumor has a negative impact on overall survival in 1p/19q intact astrocytoma than on 1p/19q co-deleted oligodendroglioma [4]. Therefore, the noninvasive assessment of the molecular subtype of 1p/19q is particularly valuable in guiding clinical decision making.

The radiological features of 1p/19q co-deleted tumors frequently display calcifications, and they predominantly occur in the frontal lobe, with a tendency to invade the gray matter. These tumors typically exhibit heterogeneous signal intensities on both T1- and T2-weighted MR imaging, often lacking a distinct tumor margin [5, 6].

With artificial intelligence technique applied to MRI, radiogenomics becoming a promising tool for discriminating genotype of gliomas in a non-invasive fashion. The largest amount of literature researches focused on conventional MRI, such as T1-weighted imaging (T1WI), T2 weighted imaging (T2WI) and fluid-attenuated inversion recovery (FLAIR) [7‐9]. Advanced MRI techniques such as amide proton transfer weighted (APTw) or diffusion weighted imaging (DWI) remain less studied.

APTw imaging is one of the most developed branch of chemical exchange saturation transfer (CEST) imaging [10, 11]. Previous studies have demonstrated that APTw imaging has important value in detecting molecular biomarkers in gliomas, such as IDH mutation, O6-methylguanine methyltransferase (MGMT), and Lys-27-Met mutations in histone 3 genes (H3K27M) [12‐16]. In the study by Su et al. [17], CEST imaging was used to identify 1p/19q co-deletion, and statistically significant indices included direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), and MTRasym (2.0 ppm). However, the APT value was not statistically significant. In addition, a growing number of researches have shown that features extracted from DWI have predictive values in predicting of glioma molecular subtypes [18‐20].

The aim of this retrospective study was to develop a radiogenomics method to predict 1p/19q co-deletion of LGG based on advanced and conventional MRI features.

In this study, we developed a radiogenomics method that predicts 1p/19q co-deletion status in LGG based on APTw, DWI, conventional sequences, as well as additional MRI features. Our results showed that the radiomics model and the combined model exhibited excellent performance in distinguishing 1p/19q co-deletion status in both the training and test sets, with AUCs of 0.948 and 0.966 for the training set, and 0.909 and 0.896 for the test set, respectively. Furthermore, the predictive performance of our model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians. 1p/19q co-deletion is associated with longer progression-free and overall survival, and better response to radiotherapy and chemotherapy [1, 25, 26]. For the suspected LGG patients, maximal safe surgical resection is advocated as the standard of care [27]. However, recent studies demonstrated that gross total resection was not related with prolonged survival of patients with oligodendroglioma [28, 29]. Therefore, preoperative identification of 1p/19q genotype could help with surgical planning.

There are growing number of studies using machine learning algorithms to predict molecular subtypes such as 1p/19q co-deletion and IDH mutations [7, 21, 30‐33]. However, most of these studies only used conventional anatomical MR sequences because of the widespread usage. The 3D APTw and DWI in our datasets enabled us to extract molecular information from the tumor. Recently, two pioneering APTw-based radiomics studies have been undertaken, aiming to distinguish glioblastomas from gliomas and brain metastases, as well as to discriminate treatment response from tumor progression [34, 35]. Our research findings indicate that APTw-based radiomics also holds value in predicting the 1p/19q co-deletion in LGG.

In the training set, our findings indicate statistically significant differences in calcifications, tumor margin clarity, and gray matter involvement between gliomas with 1p/19q co-deletion and those without, which is consistent with prior studies. Notably, among these features, only calcifications and tumor margin clarity independently predict 1p/19q co-deletion. This may be related to the relatively small sample size of our training dataset. In the radiomics model, our results demonstrate that among the three most contributing features two are texture features from T2WI and DWI while the third is a histogram feature from APTw (Fig. 3, c). This finding could partly be explained by the fact that 1p/19q-codeleted glioma frequently had heterogeneous signal intensity on T2WI [6, 36], mixed/restricted diffusion characteristics [37], and concentration of endogenous cellular proteins in tissue, which can be reflected by radiomic features from APTw, varies across different molecular subtypes of gliomas [38].

Of all these models, the radiomics model achieved the best performance in the testing set, which means the radiomics model had strong predictive power for 1p/19q co-deletion. The clinical model demonstrates similar performance in both the training and testing sets with an AUC of 0.760 and 0.766. However, in the training set, the AUC value of the radiomics model is lower than that of the combined model. Contrastingly, in the testing set, the AUC value of the radiomics model is higher than that of the combined model. This observed difference could be attributed to the relatively smaller sample size of the testing set, indicating the need for a larger dataset evaluation to draw more conclusive results.

Our study has several limitations. First, it was based on a single-center, retrospectively collected dataset, multi-center data will be needed to allow external validation. Secondly, other medical images including perfusion-weighted imaging (PWI) and computed tomography (CT) may provide extra functional and calcified information; accordingly, we suggest that further work includes more imaging modalities to explore the performance of radiomic models. Finally, our work only focused on the prediction of the 1p/19q co-deletion genotype, the analysis of other molecular subtype, including IDH1/2, and CDKN2A/B will permit additional comprehensive understanding of the diffuse gliomas.

Radiomic features from APTw, DWI and conventional MRI sequences can preoperatively and non-invasively distinguish the 1p/19q co-deletion genotype in patients with LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians. These findings suggest that our radiogenomics approach has the potential to become a valuable tool in clinical decision making for LGG patients.