Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis

Wanyi Sun, Cheng Song, Chao Tang, Chenghao Pan, Peng Xue*, Jinhu Fan*, Youlin Qiao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I2). 33 were ultimately included in the meta-analysis. The overall pooled SE and SP were 94% and 93%, with an AUC of 0.98. There was great heterogeneity in this field. Our evidence-based study shows DL achieves high accuracy in glioma grading. Subgroup analysis reveals several limitations in this field: 1) Diagnostic trials require standard method for data merging for AI; 2) small sample size; 3) poor-quality image preprocessing; 4) not standard algorithm development; 5) not standard data report; 6) different definition of HGG and LGG; and 7) poor extrapolation.

Original languageEnglish
Article number106815
JournaliScience
Volume26
Issue number6
DOIs
Publication statusPublished - 16 Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • Machine learning
  • Oncology

Fingerprint

Dive into the research topics of 'Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis'. Together they form a unique fingerprint.

Cite this