Patients
The study was approved by the Institutional Review Board of Beijing Tiantan Hospital, Capital Medical University, Beijing, China (KY2020-048-01). The study was also registered at Chinese ClinicalTrial Registry (ChiCTR2000031805). All study procedures were in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients. Fifty-two glioma patients and one hundred seventeen healthy controls participated this study, which were listed in Table 1. All gliomas in the cohort were diagnosed according to the criteria of the WHO classification system in the revised version of 2016. Inclusion criteria were suspected, newly diagnosed glioma with only one cancer region in the brain and age over 18 years. Exclusion criteria were previous cranial surgery, neuropsychiatric comorbidities, and any contraindications to MR scanning such as metal implants. Neuropsychological testing was conducted prior to the first MRI scan using the Montreal Cognitive Assessment (MOCA) test. Histological confirmation of the diagnosis was obtained by surgical resection. Molecular markers, including 1p/19q codeletion, IDH1/2 mutation, TERT promoter mutation and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation were all collected. Chromosomes 1 and 19 were analyzed by the fluorescence in situ hybridization method, and the IDH1/2 mutation and TERT promoter mutation were detected by sequence analysis, both following a previously described protocol[12]. MGMT promoter methylation was assessed by methylation-specific PCR as described previously by our team[13]. Patients were followed with routine clinical visits after initiation of therapy. All healthy controls were recruited from the local community and university students.
Structural MRI acquirement
All subjects were scanned with a Philips Ingenia 3.0T MRI scanner at Beijing Tiantan Hospital. For both glioma patients and healthy controls, T1 sequence was collected with the following parameters: TR: 6.5 ms, TE: 3.0 ms, Flip angle: 8°, voxel size: 1×1×1 mm3, image dimension: 256×256×196. In addition, T2-flair was also scanned for glioma patients with the listed parameters: TR: 4.8 s, TE: 0.34 s, Flip angle: 90°, voxel size: 0.625×0.625×0.55 mm3, image dimension: 400×400×300.
Image processing
The tumor region of every patient was extracted from T2-flair images with two sequential steps: 1. launch an automatic segmentation by ITK-SNAP software; 2. manually correct the segmentation by experienced neurosurgeons. After the segmentation, individual T2-flair image was co-registered to its corresponding T1 image by SPM software, and the transformation matrix was used on the segmented tumor to obtain the matched tumor region in T1 image space.
Individual T1 images were processed with CAT12 software to calculate the brain tissue volume. The skull stripping and correction for bias-field inhomogeneities were subsequently conducted. After that, the whole brain was segmented into different tissue types, e.g. gray matter and white matter. Then, the segmented GM and WM images were normalized to the MNI standard space with a modulation manner by DARTEL algorithm. Finally, the normalized GM and WM images were smoothed with 4-mm FWHM Gaussian kernel.
Individual structural abnormality index
Figure 1 illustrated the flowchart of the proposed individual structural abnormality map. In order to calculate individual structural abnormality maps for each glioma patient, all healthy controls were firstly used to construct the normative brain volume model. In this model, general linear model (GLM) was adopted to discover the relationship between voxel-wise volume and variables including age, sex, and total intracranial volume (TIV) as the following equation.

Here, β1, β2, β3 were weights for age, sex and TIV on the voxel-wise volume, and GM and WM volume models were respectively constructed.
Once the models had been created, the individual structural abnormality map for each glioma patient was calculated based on W score, which was calculated as following:

After the calculation of W score for each patient, a cutoff threshold (|W|>3) was set to the individual W score map to generate the individual structural abnormality map. Notably, an individualized explicit mask was generated for every patient which combined the corresponding tissue (GM/WM) prior probability template (threshold: 0.2) with individual tumor mask. Through the individual structural abnormality map, we proposed a series of abnormality indexes (8 GM indexes and 8 WM indexes, 16 in total) to reflect the characteristics of structural damages induced by glioma, which were listed in detail in Table 2. For GM/WM, there are respectively 8 individual structural abnormality indexes: GM atrophy ratio in tumor hemisphere (GM-ART), WM atrophy ratio in tumor hemisphere (WM-ART), GM atrophy ratio in contralateral hemisphere (GM-ARC), WM atrophy ratio in contralateral hemisphere (WM-ARC), GM enlargement ratio in tumor hemisphere (GM-ERT), WM enlargement ratio in tumor hemisphere (WM-ERT), GM enlargement ratio in contralateral hemisphere (GM-ERC), WM enlargement ratio in contralateral hemisphere (WM-ERC), GM abnormality ratio in tumor hemisphere (GM-ABRT), WM abnormality ratio in tumor hemisphere (WM-ABRT), GM abnormality ratio in contralateral hemisphere (GM-ABRC), WM abnormality ratio in contralateral hemisphere (WM-ABRC), GM abnormality in non-cancer region in comparison to cancer region (GM-ABNC), WM abnormality in non-cancer region in comparison to cancer region (WM-ABNC), GM abnormality in non-cancer region in comparison to whole brain (GM-ABNW) and WM abnormality in non-cancer region in comparison to whole brain (WM-ABNW).
Associations with molecular, histological, and cognitive indicators
To explore the relationship between each of 16 abnormality indexes and IDH1, TERT mutation, 1p/19q deletion, MGMT methylation, histological grade and MOCA score, Spearman correlation was used and a P <0.05 was thought as significance. Moreover, concerning the combinations of the proposed 16 individual structural abnormal indexes may further boost the correlation with these clinically concerned indicators, Canonical Correlation Analysis (CCA) was used to merge all indexes into a new canonical variable for each clinical indicator. CCA is in fact to seek the optimal linear combinations of these indexes that display the largest correlational relationship with these clinical indicators. Clearly, the weights (coefficients) of 16 abnormal indexes were different for each clinical indicator.
The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.