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Updated: Feb 11, 2026
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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
Published on: January 8, 2018
13.7K
Radiomics in radiooncology - Challenging the medical physicist
Jan C Peeken1, Michael Bernhofer2, Benedikt Wiestler3
1Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany.
Summary
Artificial intelligence (AI) in medical imaging, particularly radiomics and machine learning in radiooncology, presents new challenges and opportunities. Medical physicists must integrate diverse data for improved clinical decisions and treatment outcome predictions.
Area of Science:
- Radiology
- Medical Physics
- Oncology
Background:
- Artificial intelligence (AI) is rapidly advancing in medical image analysis.
- Radiooncology increasingly utilizes big data concepts for high-throughput image processing.
- Radiomics and machine learning are key AI technologies impacting clinical decision support.
Purpose of the Study:
- To emphasize the evolving role of medical physicists in AI-driven radiooncology.
- To address challenges in implementing big data and AI in clinical environments.
- To explore the integration of diverse data for enhanced prediction models.
Main Methods:
- Describing techniques for processing minable data and extracting radiomics features.
- Associating extracted features with clinical, physical, and biological data.
- Developing prediction models with an emphasis on clinical significance.
Main Results:
- Radiomics analysis serves as an independent information source influencing radiooncology practice.
- Radiomics can predict patient prognosis, treatment response, and genetic changes.
- Integrating imaging, clinical, genetic, and dosimetric data ('panomics') challenges medical physicists.
Conclusions:
- Big data processing in radiooncology supports clinical decisions and improves treatment outcome prediction.
- Medical physicist involvement is crucial for integrating physical data into radiomics.
- Medical physics organizations should offer AI training for radiooncology applications.
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