https://github.com/bhklab/quantitative-review
Quantitative review of methods for analyzing multiple regions of interest (ROIs) in the context of engineered radiomics.
https://github.com/bhklab/quantitative-review
Last synced: 4 months ago
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Quantitative review of methods for analyzing multiple regions of interest (ROIs) in the context of engineered radiomics.
- Host: GitHub
- URL: https://github.com/bhklab/quantitative-review
- Owner: bhklab
- License: mit
- Created: 2024-02-16T23:33:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-29T19:35:01.000Z (7 months ago)
- Last Synced: 2024-12-30T03:21:01.973Z (5 months ago)
- Language: Python
- Size: 26.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Quantitative Review of methods for analyzing multiple regions of interest (ROIs) in the context of engineered radiomics.
Radiomics is a field within medical imaging whose primary goal is the transformation of medical images into mineable data for improved decision-making in diagnosis, prognosis, and treatment planning. Traditionally, radiomics has focused on the analysis of a single lesion within a patient, aiming to extract relevant features and understand the tumor's characteristics. While this practical approach minimizes computational complexity and utilizes simpler mathematical models, relying on a single lesion's analysis may not provide a complete picture of the disease's intra- and inter-lesion heterogeneity, particularly in patients with multiple lesions.
Currently, there is no established methodology for the optimal combination of radiomic features for patients with multiple lesions. The lack of a consensus methodology has led to a diverse array of approaches, each offering distinct advantages and trade-offs. This quantitative review endeavors to shed light on the available methodologies, highlight their strengths and weaknesses, and guide future research toward establishing best practices in this domain.