Mohsen Tabatabaei1, Ali Razaei2,3, Amir Hossein Sarrami4, Zahra Saadatpour2,3, Aparna Singhal2,3, Houman Sotoudeh2,3. 1. Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran. 2. Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA. 3. Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA. 4. University of Semnan, Semnan, Iran.
Abstract
INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS: Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS: By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION: The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.
INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS: Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS: By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION: The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.
Authors: Ryan C Bahar; Sara Merkaj; Gabriel I Cassinelli Petersen; Niklas Tillmanns; Harry Subramanian; Waverly Rose Brim; Tal Zeevi; Lawrence Staib; Eve Kazarian; MingDe Lin; Khaled Bousabarah; Anita J Huttner; Andrej Pala; Seyedmehdi Payabvash; Jana Ivanidze; Jin Cui; Ajay Malhotra; Mariam S Aboian Journal: Front Oncol Date: 2022-04-22 Impact factor: 5.738
Authors: Sara Merkaj; Ryan C Bahar; Tal Zeevi; MingDe Lin; Ichiro Ikuta; Khaled Bousabarah; Gabriel I Cassinelli Petersen; Lawrence Staib; Seyedmehdi Payabvash; John T Mongan; Soonmee Cha; Mariam S Aboian Journal: Cancers (Basel) Date: 2022-05-25 Impact factor: 6.575