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Updated: Feb 2, 2026
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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
Published on: January 8, 2018
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[Radiomics: the process and applications in tumor research]
1Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center of Cancer, Tianjin 300060, China.
Zhonghua Zhong Liu Za Zhi [Chinese Journal of Oncology]
|November 28, 2018
Summary
Radiomics uses quantitative features from medical images for improved diagnosis and prediction. This computational approach offers objective and rich data compared to visual interpretation, aiding in various aspects of cancer care.
Area of Science:
- Medical Imaging
- Computational Pathology
- Oncology
Background:
- Radiomics offers high-throughput quantitative feature extraction from medical images.
- Traditional visual interpretation has limitations in objectivity and feature richness.
Purpose of the Study:
- To outline the process and potential of radiomics in medical image analysis.
- To highlight radiomics' role in enhancing tumor diagnosis and prediction.
Main Methods:
- Image acquisition and segmentation of regions of interest.
- Extraction and analysis of quantitative radiomic features.
- Development of classification or prediction models.
Main Results:
- Radiomics provides more efficient, objective, and diverse features than visual interpretation.
- High image quality and consistent parameters are crucial for radiomic analysis.
- Extracted features are limited to the segmented area.
Conclusions:
- Radiomics shows promise in tumor screening, diagnosis, grading, staging, and prognosis.
- Integrating radiomics with visual interpretation can improve tumor diagnosis and prediction.
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