A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI
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Published:2024-02-02
Issue:2
Volume:150
Page:
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ISSN:1432-1335
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Container-title:Journal of Cancer Research and Clinical Oncology
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language:en
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Short-container-title:J Cancer Res Clin Oncol
Author:
Wei Ruili,Lu Songlin,Lai Shengsheng,Liang Fangrong,Zhang Wanli,Jiang Xinqing,Zhen Xin,Yang Ruimeng
Abstract
Abstract
Purpose
To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification.
Methods
329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified.
Results
The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ($${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$
F
seq
1
,
2
,
3
,
4
) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features $${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$
F
seq
1
,
2
,
3
,
4
achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402).
Conclusion
The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
Funder
Natural Science Foundation of Guangdong Province Science and Technology Project of Guangzhou National Natural Science Foundation of China Basic and Applied Basic Research Foundation of Guangdong Province Guangzhou Key Laboratory of Molecular Imaging and Clinical Translational Medicine Special Fund for the Construction of High-level Key Clinical Specialty (Medical Imaging) in Guangzhou
Publisher
Springer Science and Business Media LLC
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