Convolutional neural network and glioma genotyping: A systematic review.

Abstract

e14038 Background: Gliomas are the most common primary intracranial tumors. Overall, there are 23 different glioma types, and the grading and prognosis of each type are determined by their specific mutations. Several identified mutations are IDH gene status, CDKN2A/B homozygote delegation, PTEN mutation, p53 mutation, TERT promotor mutation, H3K27M mutation, MGMT promotor methylation, trisomy 7/monozomy10, EGFR amplification, ATRX mutation, 1p/19q codeletion, and BRAF mutation. These specific mutations and genomic features can have their distinctive radiologic features seen in different magnetic resonance imaging (MRI) sequences. By using a Convolutional Neural Network (CNN) for feature extraction, the genotyping power of MRI for glioma could be increased. To address this, we systematically reviewed the use of different CNN-based models to detect the genetic context of glioma based on the MRI images. Methods: We conducted a systematic search in PubMed, Embase, Scopus, Web of Science, and Google Scholar. We included original peer-reviewed human studies (retrospective or prospective), focusing on the use of CNN in individuals with glioma. We excluded animal studies, studies with no CNN-based models, reviews, book chapters, conference abstracts, and case reports. JBI checklists were utilized for quality assessment. Results: The initial search resulted in 973 articles. after removing duplicates, 563 studies entered the screening process by title and abstract. 59 studies met our inclusion criteria (56 retrospective and 3 prospective cohort studies) with a total of 26434 glioma patients included (studies ranged from 21 – 2648 individuals). 31 studies used CNN methods only in different stages of image processing (tumor segmentation, feature extraction, image encoder) and 28 studies used CNN as a classifier and mutation predictor. ResNet was the most commonly used CNN-based model (n = 14). U-net (n = 7) and DenseNet models (n = 4) were other commonly used CNN-based models. Included studies targeted IDH status (n=44), 1p/19q codeletion (n=18), MGMT promotor methylation (n=11), ATRX mutation (n=5), TERT promotor mutation (n=5), CDKN2A/B homozygote deletion (n=3), PTEN mutation (n=3), p53 mutation (n=3), H3K27M mutation (n=2), trisomy 7/monozomy10 (n=2), EGFR amplification (n=2), and BRAF mutation (n=1). Highly severe methodological heterogeneity among these studies excluded the possibility of meta-analysis. Conclusions: Current studies have shown that, as a non-invasive method, using CNN-based models on multiple MRI sequences increases the power of early detection and prediction of glioma genetic types before pathological confirmation and has promising results. However, DL performance varies among mutations. Combining multiple DL models with clinical and radiomic features in different and larger databases can further increase the final results.