BMC Cancer (Jul 2024)

Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients

  • Xian Xie,
  • Chen Luo,
  • Shuai Wu,
  • Wanyu Qiao,
  • Wei Deng,
  • Lei Jin,
  • Junfeng Lu,
  • Linghao Bu,
  • Hugues Duffau,
  • Jie Zhang,
  • Ye Yao

DOI
https://doi.org/10.1186/s12885-024-12542-w
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 13

Abstract

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Abstract Background Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. Methods A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. Results The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. Conclusions The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.

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