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Survival-relevant high-risk subregion identification for glioblastoma patients: the MRI-based multiple instance learning approach

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Given the glioblastoma (GBM) heterogeneity, survival-relevant high-risk subregions may exist and facilitate prognosis. The study aimed to identify the high-risk subregions on MRI, and to evaluate their survival stratification performance.


The gross tumor regions (GTRs) were delineated on the normalized MRI of 104 GBM patients. The signal intensity of voxels from 104 GTRs was pooled as global intensity vector, and K-means clustering was performed on it to find the optimal global clusters. Subregions were generated by assigning back voxels that belonged to each global cluster. Finally, a multiple instance learning (MIL) model was built and validated using radiomics features from each subregion. In this process, subregions predicted as positive would be treated as high-risk subregions, and patients with high-risk subregions inside the GTR would be predicted as having short-term survival.


After K-means clustering, three global clusters were fixed and 294 subregions of 104 patients were generated. Then, the subregion-level MIL model was trained and tested by 200 (71 patients) and 94 subregions (33 patients). The accuracy, sensitivity, and specificity for survival stratification were 87.88%, 85.71%, and 89.47%. Furthermore, 41 high-risk subregions were correctly predicted from patients with short-term survival, in which the median overlap rate of non-enhancing component was 60%.


The stratification performance of high-risk subregions identified by the MIL model was higher than the GTR. The non-enhancing area on MRI was the most important component in high-risk subregions. The MIL approach provides a new perspective on the clinical challenges of glioma with coarse-grained labeling.

Key Points

• The performance of high-risk subregions was more promising than the GTR for OS stratification.

• The non-enhancing component was the most important in the high-risk subregions.

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The contrast-enhanced tumor


Fluid-attenuated inversion recovery




The co-occurrence matrix


The gray-level run-length texture matrix


The gray-level size-zone matrix


The gross tumor regions


Intraclass correlation coefficient


Karnofsky Performance Status


Multiple instance learning


Magnetic resonance imaging


The non-contrast-enhanced tumor


The necrosis tumor


Overall survival


The Cancer Genome Atlas


The Cancer Imaging Archive


Echo time


Repetition time


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This study has received funding by the National Nature Science Foundation of China (No. 81701658 to Zhang Xi, 81871424 to Liu Yang, and 81801655 to Tian Qiang) and the Major Project of Shaanxi Province (No. 2020JZ-28 to Liu Yang).

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Corresponding authors

Correspondence to Xiaowei He or Yang Liu.

Ethics declarations


The scientific guarantor of this publication is Yang Liu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors, Xiaopan Xu, has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because all the patient data in TCGA was deidentified.

Ethical approval

Institutional Review Board approval was not required because all the data used in this study were selected from the Cancer Genome Atlas (TCIA). After ethical review by NIH, the TCIA is freely available for the scientific research. Followed by the instructions of TCIA, we have referred related articles about TCIA.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in AJNR (Am J Neuroradiol. 2017.


• retrospective

• diagnostic or prognostic study

• multicenter study

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Zhang, X., Lu, D., Gao, P. et al. Survival-relevant high-risk subregion identification for glioblastoma patients: the MRI-based multiple instance learning approach. Eur Radiol 30, 5602–5610 (2020).

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