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BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models

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Abstract

We aimed to study whether the Bruton’s tyrosine kinase (BTK) expression is correlated with the prognosis of patients with high-grade gliomas (HGGs) and predict its expression level prior to surgery, by constructing radiomic models. Clinical and gene expression data of 310 patients from The Cancer Genome Atlas (TCGA) were included for gene-based prognostic analysis. Among them, contrast-enhanced T1-weighted imaging (T1WI + C) from The Cancer Imaging Archive (TCIA) with genomic data was selected from 82 patients for radiomic models, including support vector machine (SVM) and logistic regression (LR) models. Furthermore, the nomogram incorporating radiomic signatures was constructed to evaluate its clinical efficacy. BTK was identified as an independent risk factor for HGGs through univariate and multivariate Cox regression analyses. Three radiomic features were selected to construct the SVM and LR models, and the validation set showed area under curve (AUCs) values of 0.711 (95% CI, 0.598–0.824) and 0.736 (95% CI, 0.627–0.844), respectively. The median survival times of the high Rad_score and low-Rad_score groups based on LR model were 15.53 and 23.03 months, respectively. In addition, the total risk score of each patient was used to construct a predictive nomogram, and the AUCs calculated from the corresponding time-dependent ROC curves were 0.533, 0.659, and 0.767 for 1, 3, and 5 years, respectively. BTK is an independent risk factor associated with poor prognosis in patients, and the radiomic model constructed in this study can effectively and non-invasively predict preoperative BTK expression levels and patient prognosis based on T1WI + C.

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The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author/s.

Abbreviations

HGG:

High-Grade Glioma

BTK:

Bruton’s Tyrosine Kinase

MRI:

Magnetic Resonance Imaging

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

T1WI + C:

Contrast-Enhanced T1-Weighted Imaging

CGGA:

Chinese Glioma Genome Atlas

OS:

Overall survival

GLRLM:

Gray-Level Run-Length Matrix

GLSZM:

Gray-Level Size Zone Matrix

GLDM :

Gray-Level Dependence Matrix

GLCM:

Gray-Level Co-Occurrence Matrix

NGTDM:

Neighboring Gray Tone Difference Matrix

mRMR:

Maximum Relevance Minimum Redundancy

RFE:

Recursive Feature Elimination

SVM:

Support Vector Machine

LR:

Logistic Regression

ROC:

Receiver Operating Characteristic

AUC:

Area Under the Curve

DCA:

Decision Curve Analysis

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

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Funding

This paper was funded by the National Key R&D Program of China, No. 2022YFF0608404.

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Authors and Affiliations

  1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road, South Fourth Ring Road, Beijing, China

    Chenggang Jiang, Chen Sun, Xi Wang, Shunchang Ma, Wang Jia & Dainan Zhang

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Contributions

JW, ZDN, and MSC formulated the overall research design and conceived the concept. JCG made significant contributions to data collection and part of data processing. WX and SC completed the majority of data processing. JCG and ZDN drafted the manuscript. All authors made critical revision of the manuscript. All authors contributed equally to this paper. All authors read and approved the final manuscript.

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Correspondence to Dainan Zhang.

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Jiang, C., Sun, C., Wang, X. et al. BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01026-9

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