To Determine Whether the Habitats Created by the Preoperative or Postoperative Multiparametric MRI Can Predict IDH Mutation Status, Prognosis, Recurrence or Intracranial Distant Recurrence of the Patients With High-grade Gliomas
Conditions: High-grade Glioma
Sponsor: Weiguo Zhang

Jiachen Liu, MD 
E-mail: jcliu0430@163.com 
Chongqing, September 1, 2022 
 
Title: How the habitats created by MRI can predict the IDH 
mutation status and prognosis of the patients with high-grade 
gliomas 
 
1.  Study direction 
Prediction of IDH mutation status and prognosis of high-grade glioma patients 
based on multi-parametric MRI habitat imaging.  
The multi-parametric MRI involved in this study scheme generally includes the 
following types in ideal state: conventional MRI sequences, Dynamic Susceptibility 
Weighted Contrast Enhanced Magnetic Resonance Imaging (DSC-MRI), Dynamic 
Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), Diffusion Weighted 
Magnetic Resonance Imaging (DWI), Vessel Size Imaging (VSI), or Magnetic 
Resonance Spectroscopy (MRS), and multiple combinations of indefinite items are 
carried out according to different research purposes. 
 
2.  Study background and objective 
2.1  Background 
High-grade glioma (HGG) is the most common primary brain tumor in the central 
nervous system. It has a highly malignant biological behavior, and is also a disease with 
complex initial mechanism and in constant dynamic change. Despite the emergence of 
multi-modal therapy such as immune and molecular targeting in recent years, the 
overall prognosis of HGG patients has not been significantly improved, and the median 
survival time has not been significantly improved. The high invasiveness and 
recurrence rate of HGG can be attributed in part to the significant tumor heterogeneity. 
Studies have shown that tumors are not homogenous individuals but uneven 
distribution of tumor cell subsets with different gene mutations or dominant clonal 
populations formed by tumor stem cells establishing differentiation levels. Genetic 

variation among these tumor subclones causes them to compete with each other and 
change dynamically driven by environmental selection pressures based on local action, 
in a pattern similar to the occupation of specific habitats by different species in an 
ecosystem. These tumor cell clusters with the same ability of environmental selection 
and cell evolution are called habitats. 
In terms of imaging, it is not difficult to find significant heterogeneity in the tumor 
based on differences in signals and enhancement modes. However, there are inevitably 
subjective differences between observers in the description of medical images such as 
enhancement, necrosis, edema, and tumor boundaries. Therefore, there is an urgent 
need for an accurate, objective, quantitative and spatial imaging method to express 
tumor heterogeneity. Habitat imaging technique is based on this need. Habitat imaging 
is an image segmentation concept and quantitative analysis technology based on 
differences in tumor pathology, blood perfusion, molecular characteristics, etc., which 
can combine various imaging methods to reveal the pathophysiological significance 
behind tumor imaging heterogeneity and the relationship between regional differences 
in the microenvironment that drive tumor evolution and adaptive strategies of tumor 
cells, to evaluate and monitor the temporal and spatial evolution of tumor habitat and 
predict potential genotypes and phenotypes. In addition, habitat imaging has been 
proved by many studies that the quantitative parameters obtained from habitat or the 
radiomics features extracted from habitat can be used as a robust biomarker for the 
prediction of overall survival, molecular mutation status and clinical treatment effect of 
patients. 
Above all, we have sufficient and firm reasons to deem that habitat imaging based 
on multiparametric MRI is more conducive to reflect the potential biological 
information inside the tumor and realize individualized diagnosis and treatment. 
 
2.2  Study objective 
The purpose of this study was to construct tumor habitat by combining 
preoperative or postoperative multi-parametric MRI imaging, so as  
(1) to reveal the spatial and/or temporal heterogeneity within tumors;  
(2) to explore the value of habitat imaging in the classification and molecular typing of 
HGG; 
(3) to investigate the value of parameters quantitatively obtained from habitat in 
predicting the overall survival time and risk factors of HGG patients; 
(4) to investigate the differential and predictive ability of HGG local recurrence and 

intracranial long-distance recurrence;  
(5) to verify whether the habitat imaging analysis was more accurate than the traditional 
holistic tumor analysis in the above studies. 
 
3.  Study program 
3.1  Study center 
The study was a single-center observational retrospective study with data from the 
hospital information system. The subjects of the study were HGG patients who 
underwent MRI at the Army Medical Center of PLA (also known as Daping Hospital 
and the Research Institute of Surgery of the Third Military Medical University) from 
January 1, 2008 to December 31, 2021 (or at some interval within this period). 
 
3.2  Selection criteria 
Inclusion Criteria (if we will predict the molecular status and overall survival): 
(1) the patient was over 18 years old;  
(2) the lesion was located in the supratentorial space; 
(3) a histopathologic diagnosis of HGGs according to the WHO CNS4/5;  
(4) all subjects were the first diagnosed cases without any invasive or non-invasive 
treatment;  
(5) access to the complete preoperative MR imaging examinations, at least including 
four conventional sequences.  
 
Inclusion Criteria (if we will differentiate recurrence from distant intracranial 
recurrence): 
(1) the patient was over 18 years old;  
(2) the lesion was located in the supratentorial space; 
(3) a histopathologic diagnosis of HGGs according to the WHO CNS4/5;  
(4) underwent concurrent chemoradiotherapy with temozolomide after surgical 
resection or biopsy;  
(5) underwent preoperative and postoperative MRI, at least including four 
conventional sequences; 
(6) had newly appeared or enlarging, measurable, contrast-enhancing mass which 
raises clinical suspicion of tumor recurrence and distant intracranial recurrence;  
(7) adequate follow-up examinations to determine treatment response on clinic-
radiological consensus or pathologic confirmation. 

 
Exclusion Criteria: 
(1) patient with other brain tumors or other grade gliomas at the same time; 
(2) patient with severe basic diseases at the same time;  
(3) patient with a survival time of less than 30 days, which can be caused by severe 
surgical trauma stress;  
(4) poor image quality and heavy artifact affect the subsequent image processing. 
 
4.  Study data and variable collection 
(1) Basic clinical information:  
age, sex, height, weight and previous underlying diseases of the patient;  
(2) Image and date collection:  
the original MR image and the date of each MRI from preoperative to 
postoperative to recurrent to follow-up to the last examination, and records tumor 
location, volume, boundaries, and enhancement patterns; 
(3) Pathological and immunohistochemical data collection:  
the specific diagnosis of high-grade glioma and specific molecular 
characteristics, including IDH, MGMT, TERT, 1p/19q, etc. 
(4) Methods of operation and postoperative treatment: 
the extent of resection includes gross total resection, subtotal resection and 
partial resection, and the adjuvant therapy includes radiation, chemotherapy, 
chemoradiotherapy, untreated and unknow. 
(5) Prognostic data collection:  
specific overall survival time, recurrence time after treatment, whether there is 
an endpoint time.  
(6) Quantitative parameter collection: 
the corresponding characteristic quantitative parameters were extracted 
according to different magnetic resonance sequences. 
 
* The data will be collected from the Hospital Information System (HIS), Picture 
Archiving and Communication System (PACS) and telephone follow-up.  
 
5.  Advanced image processing and habitat construction 
The multi-parametric MRI images of all patients will be exported from the PACS 

system in DICOM format. First of all, all DICOM data will be converted to Nifti format. 
Subsequently, skull stripping will be performed. Next, the image will be resampled and 
registered. A deep learning model of nnU-Net will be employed to segment preliminary 
subregions of enhancing tumor, edema and necrosis on the four conventional structure 
sequences. Finally, the habitat construction will be carried out using a K-means 
clustering. Parameter values can be extracted quantitatively from habitats and radiomics 
features can be extracted quantitatively too. 
 
6.  Statistical analysis 
Independent-samples T-test or Mann-Whiney U-test will be used to analyze the 
differences in quantitative parameter values between IDH-mutant HGGs and their wild-
type counterpart in each habitat, using Benjamini-Hochberg’s procedure for controlling 
the false discovery rate in multiple comparisons. The Chi-square testing will be used to 
analyze the constituent ratio of variables. ROC will be applied to evaluate the value of 
differentiating IDH mutation status and verify the differential efficacy of precise tumor 
habitats constructed by multi-parametric MRI and traditional tumor habitats. The cutoff 
values for each habitat were determined by using Youden index. The OS of HGG 
patients were analyzed using Kaplan-Meier survival analysis and Log-rank test. 
Specific statistical methods shall be formulated according to specific circumstances 
 
7.  Outcomes 
Primary outcomes: multi-model habitats constructed by multiparametric MRI predict 
IDH mutation status and the prognosis in high-grade gliomas (in progress); 
Secondary outcomes: multi-model habitats constructed by multiparametric MRI 
differentiate recurrence from distant intracranial recurrence (in the stage of preparation); 
Exploratory outcomes: validate the advantages of habitat imaging and potential links 
to pathophysiology. 
 
8.  Ethical issues 
(1) Documents and procedures to apply for and obtain approval from the Ethics 
committee in accordance with the Declaration of Helsinki and the code of ethics of 
the country in which the research is conducted; 
(2) Before each patient is enrolled in the study, the study physician is responsible for 

providing a complete and comprehensive written description of the purpose, 
procedures, and possible risks of the study to him or her or her designated 
representative. Patients should be made aware of their right to withdraw from the 
study at any time. A written patient informed consent should be given to each 
patient before enrollment. The research physician is responsible for obtaining 
informed consent before each patient is enrolled in the study, and informed consent 
should be kept as a clinical research document for future reference; 
(3) Follow-up of subjects during and after the study;  
(4) Confidentiality regulations for subjects' privacy. 
 
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