A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

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

Reference37 articles.

1. Aldape K, Zadeh G, Mansouri S, Reifenberger G, von Deimling A (2015) Glioblastoma: pathology, molecular mechanisms and markers. Acta Neuropathol 129(6):829–848. https://doi.org/10.1007/s00401-015-1432-1

2. Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, Zhang B, Capellini A, Liao W, Shen Q, Li X, Xiao B, Cryan J, Ramkissoon S, Ramkissoon L, Ligon K, Wen PY, Bindra RS, Woo J, Arnaout O, Gerstner ER, Zhang PJ, Rosen BR, Yang L, Huang RY, Kalpathy-Cramer J (2018a) Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging [Journal Article; Research Support, N.I.H, Extramural; Research Support, Non-U.S. Gov’t; Validation Study]. Clin Cancer Res 24(5):1073–1081. https://doi.org/10.1158/1078-0432.CCR-17-2236

3. Chen R, Smith-Cohn M, Cohen AL, Colman H (2017) Glioma subclassifications and their clinical significance [Journal Article; Review]. Neurotherapeutics 14(2):284–297. https://doi.org/10.1007/s13311-017-0519-x

4. Chen L, Zhang H, Lu J, Thung K, Aibaidula A, Liu L, Chen S, Jin L, Wu J, Wang Q, Zhou L, Shen D (2018) Multi-label nonlinear matrix completion with transductive multi-task feature selection for joint MGMT and IDH1 status prediction of patient with high-grade gliomas. IEEE Trans Med Imaging 37(8):1775–1787. https://doi.org/10.1109/TMI.2018.2807590

5. Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z (2019) Development and validation of a MRI-based radiomics prognostic classifier in patients with primary glioblastoma multiforme [Journal Article; Research Support, Non-U.S.Gov’t; Validation Study]. Acad Radiol 26(10):1292–1300. https://doi.org/10.1016/j.acra.2018.12.016

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