Radiomic analysis: Study design, statistical analysis, and other bias mitigation strategies Review
| Authors: | Moskowitz, C. S.; Welch, M. L.; Jacobs, M. A.; Kurland, B. F.; Simpson, A. L. |
| Review Title: | Radiomic analysis: Study design, statistical analysis, and other bias mitigation strategies |
| Abstract: | Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review hisses, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described. (C) RSNA, 2022 |
| Keywords: | accuracy; prediction; standardization; artifacts; computed-tomography; regression; models; tests; variability; issues |
| Journal Title: | Radiology |
| Volume: | 304 |
| Issue: | 2 |
| ISSN: | 0033-8419 |
| Publisher: | Radiological Society of North America, Inc. |
| Date Published: | 2022-08-01 |
| Start Page: | 265 |
| End Page: | 273 |
| Language: | English |
| ACCESSION: | WOS:000835989400002 |
| DOI: | 10.1148/radiol.211597 |
| PROVIDER: | wos |
| PMCID: | PMC9340236 |
| PUBMED: | 35579522 |
| Notes: | Review -- Source: Wos |
