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

Chenggang Jiang1, Chen Sun1, Xi Wang1

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

Journal of Imaging Informatics in Medicine
|February 21, 2024
PubMed
Summary
This summary is machine-generated.

Bruton's tyrosine kinase (BTK) is linked to poor prognosis in high-grade gliomas (HGGs). Radiomic models using MRI scans can predict BTK levels and patient outcomes before surgery.

Keywords:
Bruton’s tyrosine kinase (BTK)High-grade glioma (HGG)Machine learningPrognosisRadiomics

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Area of Science:

  • Neuro-oncology
  • Radiomics
  • Genomics

Background:

  • High-grade gliomas (HGGs) are aggressive brain tumors with poor prognoses.
  • Bruton's tyrosine kinase (BTK) is implicated in various cancers, but its role in HGGs requires further elucidation.
  • Non-invasive methods to predict tumor characteristics and prognosis are crucial for treatment planning.

Purpose of the Study:

  • To investigate the correlation between Bruton's tyrosine kinase (BTK) expression and prognosis in high-grade gliomas (HGGs).
  • To develop radiomic models for predicting preoperative BTK expression levels in HGG patients.
  • To assess the clinical utility of a nomogram incorporating radiomic signatures for predicting patient outcomes.

Main Methods:

  • Analysis of clinical and gene expression data from 310 HGG patients in The Cancer Genome Atlas (TCGA).
  • Construction of radiomic models (SVM, LR) using contrast-enhanced T1-weighted imaging (T1WI+C) from 82 patients.
  • Development of a predictive nomogram based on radiomic features and patient survival data.

Main Results:

  • BTK was identified as an independent risk factor for poor prognosis in HGGs.
  • Radiomic models achieved AUCs of 0.711 (SVM) and 0.736 (LR) for predicting BTK expression.
  • The nomogram demonstrated predictive capabilities for 1, 3, and 5-year survival with AUCs of 0.533, 0.659, and 0.767, respectively.

Conclusions:

  • BTK expression is a significant independent predictor of poor prognosis in HGG patients.
  • Preoperative radiomic models based on T1WI+C can effectively predict BTK expression levels.
  • The developed radiomic approach offers a non-invasive tool for assessing patient prognosis in HGGs.