Radiomics in radiooncology – Challenging the medical physicist. (April 2018)

• Record Type: Journal Article
• Authors: Peeken, Jan C.; Bernhofer, Michael; Wiestler, Benedikt; Goldberg, Tatyana; Cremers, Daniel; Rost, Burkhard; Wilkens, Jan J.; Combs, Stephanie E.; Nüsslin, Fridtjof
• Is Part Of: Physica medica. Volume 48(2018)
• Page Start: 27
• Page End: 36
• Publication Date: 2018-04
• Subjects: Radiomics -- Radiogenomics -- Machine learning -- Neural networks -- Convolutional neural network; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.03.012
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Radiomics allows the prediction of patients' prognosis, treatment response and toxicity. Integration of radiomic data with physical information may advance the field further. Data mining and big data analysis are crucial for success and require specific expertise. Multidisciplinary teams should involve medical physicists, clinicians and computer scientists. Abstract: Purpose: Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. Methods: Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. Results: Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. Conclusions: The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.