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Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review.

Kaitlyn Alleman1, Erik Knecht1, Jonathan Huang2

  • 1Chicago Medical School, Rosalind Franklin University of Science and Medicine, Chicago, IL 60064, USA.

Cancers
|January 21, 2023
PubMed
Summary

Deep learning (DL) shows promise for predicting glioma survival by integrating multimodal data. Combining various data types, like MRI and clinical information, improves prediction accuracy, though transparency issues remain.

Keywords:
brain tumordeep learninggenomicsgliomamachine learningmultimodalprognosticationradiomics

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Area of Science:

  • Neuro-oncology
  • Medical imaging analysis
  • Artificial intelligence in medicine

Background:

  • Malignant brain tumors, particularly gliomas, significantly impact patient morbidity and mortality.
  • Advancements in clinical data collection and analysis offer opportunities for improved prognostic tools.
  • Deep learning (DL) presents a promising approach for integrating diverse clinical data modalities.

Purpose of the Study:

  • To systematically review the application of DL in predicting glioma prognosis.
  • To assess the impact of multimodal data integration on DL model performance for glioma prognostication.

Main Methods:

  • A systematic review was conducted following PRISMA guidelines.
  • Databases searched included Embase, PubMed MEDLINE, and Scopus.
  • Studies focused on DL-based prognostication of gliomas, analyzing predicted outcomes like overall survival and treatment response.

Main Results:

  • The review identified studies predicting overall survival (81%), survival and genotype (12.5%), and immunotherapy response (6.2%).
  • Multimodal data integration, combining MRI with clinical, histologic, biomarker, or genomic data, showed improved predictive performance compared to unimodal models.
  • Risk of bias was noted, primarily due to inconsistent methodological reporting.

Conclusions:

  • Multimodal data significantly enhances the accuracy of DL-based overall survival prediction in gliomas.
  • Challenges such as data limitations and lack of transparency in reporting hinder the full realization of multimodal DL for brain tumor patients.
  • Further research emphasizing transparent reporting is needed to optimize DL applications in neuro-oncology.