期刊
FRONTIERS IN ONCOLOGY
卷 11, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.633176
关键词
radiomics; lung cancer; reproducibility; variability; CT acquisition; tumor segmentation; feature extraction; quality control
类别
资金
- National Institute of Health [U01 CA225431]
Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype, and clinical outcome prediction in the era of precision medicine, but its clinical use faces challenges due to reasons such as reproducibility and generalizability issues. This article discusses sources of variation in radiomics signatures, reviews studies on feature reproducibility and model performance, and explores strategies to reduce feature variability and improve the quality of radiomics studies.
Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.
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