Diffusion tensor imaging‐based machine learning for <scp>IDH</scp> wild‐type glioblastoma stratification to reveal the biological underpinning of radiomic features

Wang, Zilong; Guan, Fangzhan; Duan, Wenchao; Guo, Yu; Pei, Dongling; Qiu, Yuning; Wang, Minkai; Xing, Aoqi; Liu, Zhongyi; Yu, Bin; Liu, Xianzhi; Yan, Dongming; Ji, Yuchen; Cheng, Jingliang; Yan, Jing; Zhang, Z.

doi:10.1111/cns.14263

Cited by 3 publications

(1 citation statement)

References 44 publications

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“… 7 , 8 The advancements in radiogenomics have uncovered associations between radiomic features from multimodal magnetic resonance imaging (MRI), such as conventional MRI, perfusion‐weighted imaging (PWI), and diffusion tensor imaging (DTI), and the underlying biological processes in gliomas. 9 , 10 , 11 , 12 Improved understanding of the biological interpretation of radiomic features at the molecular level could potentially accelerate the development of personalized medicine. 13 However, radiogenomics studies on prognosis assessment of insular gliomas have not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Duan,

Wang,

et al. 2024

Cancer Science

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Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA‐seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18‐feature‐based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log‐rank p = 0.00058; HR = 3.595, 95% CI:1.636–7.898) in the validation set. The radiomic‐pathological‐clinical model (R‐P‐CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function‐related pathways.

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Section: Introductionmentioning

confidence: 99%

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Duan,

Wang,

et al. 2024

Cancer Science

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Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma

Guan,

Wang,

Qiu

et al. 2023

J Transl Med

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Background To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. Methods A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. Results Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. Conclusion Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

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Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

Luan,

Zhang,

Liu

et al. 2024

J Transl Med

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Background Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. Methods We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. Results We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan–Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy’s clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. Conclusions We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

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Diffusion tensor imaging‐based machine learning for IDH wild‐type glioblastoma stratification to reveal the biological underpinning of radiomic features

Cited by 3 publications

References 44 publications

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

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