Supplementary Material for: Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review

<b><i>Introduction:</i></b> 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. <b><i>Material and Methods:</i></b> 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. <b><i>Results:</i></b> 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). <b><i>Conclusion:</i></b> 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 data­sets.

**引言：** 精准医学时代，放射组学（Radiomics）正迎来迅猛发展的态势。胶质瘤是放射组学广泛评估的病理类型之一，但该技术尚未真正落地临床应用。本系统综述选取并梳理了已发表的基于放射组学开展胶质瘤分级的相关研究，旨在评估该技术的应用可行性与现存挑战。
**材料与方法：** 本研究采用7条独立检索式，检索了PubMed、Embase及Scopus三大数据库中2015年至2020年9月期间发表的全部英文文献。经过纳入与排除标准筛选后，最终选取符合要求的文献，基于我们自主研发的改良放射组学标准评分量表（Modified Radiomics Standard Scoring，RQS）进行方法学质量评估，该量表共包含43个条目，评分范围为0~44分（最低0分，最高44分）。最后，本研究针对入选文献存在的挑战与不足提出了相关见解。
**结果：** 本次检索共获文献1177篇，其中PubMed收录485篇、Embase收录343篇、Scopus收录349篇。经过纳入排除标准筛选后，最终有18篇文献符合要求，纳入RQS质量评估分析。上述18篇文献的RQS总分为26分（占满分的59%）至43分（占满分的97%），平均分为33.5分（占满分的76%）。
**结论：** 现有相关研究虽具应用前景，但研究设计异质性极强，在放射组学软件选用、特征提取数量、特征筛选数目及机器学习模型选型等方面均存在显著差异。所有研究均为回顾性设计，其中多项研究基于小样本数据集，且存在类别不平衡问题，同时缺乏外部验证数据集。