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

Kaitlyn Alleman, Erik Knecht, Jonathan Huang, Lu Zhang, Sandi Lam, Michael DeCuypere*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized.

Original languageEnglish (US)
Article number545
JournalCancers
Volume15
Issue number2
DOIs
StatePublished - Jan 2023

Keywords

  • brain tumor
  • deep learning
  • genomics
  • glioma
  • machine learning
  • multimodal
  • prognostication
  • radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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