Semantic Scholar Open Access 2022 64 sitasi

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

C. Moskowitz M. Welch M. Jacobs B. Kurland Amber L. Simpson

Lihat Sumber DOI

Abstrak

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 biases, 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.

Topik & Kata Kunci

Medicine

Penulis (5)

C. Moskowitz

M. Welch

M. Jacobs

B. Kurland

Amber L. Simpson

Format Sitasi

APA MLA BibTeX

Moskowitz, C., Welch, M., Jacobs, M., Kurland, B., Simpson, A.L. (2022). Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.. https://doi.org/10.1148/radiol.211597

Akses Cepat

Lihat di Sumber doi.org/10.1148/radiol.211597

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 64×
Sumber Database: Semantic Scholar
DOI: 10.1148/radiol.211597
Akses: Open Access ✓