Abstract
In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics. The quantitative analysis of medical images outperform the information that can be obtained through traditional visual analysis. The recognition of neoplasm molecular and genetic characteristics in a non-invasive way, based on routine radiological examinations, potentially allow complete tumor profiling and subsequent treatment customization at practically zero costs. This process is further boosted with the availability of increased computing power and development of artificial intelligence approaches.
| Original language | English |
|---|---|
| Pages (from-to) | 36-39 |
| Number of pages | 4 |
| Journal | Nuclear Medicine Review |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Artificial Intelligence
- Positron emission tomography
- Radiomics
ASJC Scopus subject areas
- General Medicine
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