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Deep Learning Approaches for Automated Classification and Segmentation of Head and Neck Cancers and Brain Tumors in Magnetic Resonance Images: A Meta-Analysis Study Publisher Pubmed



Badrigilan S1 ; Nabavi S2 ; Abin AA2 ; Rostampour N1 ; Abedi I3 ; Shirvani A3 ; Ebrahimi Moghaddam M2
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
  2. 2. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
  3. 3. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: International Journal of Computer Assisted Radiology and Surgery Published:2021


Abstract

Purpose: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images. Methods: PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics. Results: The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76–0.9994), 0.9132 (95% CI: 0.71–0.994) and 0.9164 (95% CI: 0.78–1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively. Conclusion: Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics. © 2021, CARS.
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