Litcius/Paper detail

Impact of image quality on radiomics applications

Yunfeng Cui, F Yin

2022Physics in Medicine and Biology30 citationsDOI

Abstract

Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.

Topics & Concepts

RadiomicsImage qualityWorkflowRobustness (evolution)StandardizationComputer scienceMedical imagingArtificial intelligenceMedical physicsScannerQuality (philosophy)Quality assuranceData miningComputer visionMedicineImage (mathematics)PathologyEpistemologyChemistryBiochemistryExternal quality assessmentOperating systemDatabaseGenePhilosophyRadiomics and Machine Learning in Medical ImagingAdvanced X-ray and CT ImagingPancreatic and Hepatic Oncology Research