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European Radiology

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01-10-2020 | Glioblastoma | Neuro

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

Authors: Xi Zhang, Di Lu, Peng Gao, Qiang Tian, Hongbing Lu, Xiaopan Xu, Xiaowei He, Yang Liu

Published in: European Radiology | Issue 10/2020

Abstract

Objectives

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.

Methods

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.

Results

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%.

Conclusion

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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Title: Survival-relevant high-risk subregion identification for glioblastoma patients: the MRI-based multiple instance learning approach
Authors: Xi Zhang
Di Lu
Peng Gao
Qiang Tian
Hongbing Lu
Xiaopan Xu
Xiaowei He
Yang Liu
Publication date: 01-10-2020
Publisher: Springer Berlin Heidelberg
Keywords: Glioblastoma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Glioblastoma
Glioblastoma
Published in: European Radiology / Issue 10/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06912-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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