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Promoting Prognostic Model Application: A Review Based on Gliomas.

Xisong Liang1, Zeyu Wang1, Ziyu Dai1

  • 1Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, China.

Journal of Oncology
|August 16, 2021
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Summary
This summary is machine-generated.

This study reviews 138 machine learning-based glioma prognostic models, identifying high-quality genetic panels and markers. These findings aim to improve glioma patient management and guide genetic models toward clinical application.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Malignant neoplasms, particularly gliomas, exhibit poor prognosis due to limited therapeutic efficacy and high recurrence rates.
  • Traditional prognostic models based on clinical and pathological features have lower distinguishing power compared to genetic signatures.
  • Advancements in sequencing and machine learning have spurred the development of genetic panel-based prognostic models, especially RNA-panel models.

Purpose of the Study:

  • To systematically review and assess the quality of existing machine learning-based genetic prognostic models for glioma.
  • To identify high-quality models and key genetic markers with strong prognostic potential in gliomas.
  • To propose novel criteria for developing and evaluating clinically relevant prognostic models for glioma.

Main Methods:

  • Systematic review of 138 machine learning-based genetic models for glioma prognosis.
  • Development of novel criteria for assessing the quality of these prognostic models.
  • Analysis of biological and clinical significance of overlapping glioma markers within the reviewed models.

Main Results:

  • Identified 27 high-quality prognostic models among the 138 reviewed.
  • Screened genetic markers demonstrating strong prognostic potential for gliomas.
  • Established novel criteria for evaluating the quality and clinical applicability of genetic prognostic models.

Conclusions:

  • Machine learning-based genetic panels offer superior prognostic capability for gliomas compared to traditional methods.
  • The developed criteria can guide the assessment and selection of high-quality prognostic models for clinical use.
  • This work facilitates the translation of genetic models from research to clinical practice, enhancing glioma patient management.