Data science opportunities to improve radiotherapy planning and clinical decision making Review
| Author: | Deasy, J. O. |
| Review Title: | Data science opportunities to improve radiotherapy planning and clinical decision making |
| Abstract: | Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations (‘digital twins’) could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments. © 2024 |
| Keywords: | adult; review; treatment planning; neoplasm; neoplasms; radiotherapy dosage; radiotherapy; diagnostic imaging; probability; clinical decision making; computer simulation; radiotherapy planning, computer-assisted; cancer control; radiation dose distribution; personalized medicine; procedures; clinical decision-making; predictive model; humans; human; precision medicine; radiotherapy planning system; deep learning; radiomics; data science; digital twin |
| Journal Title: | Seminars in Radiation Oncology |
| Volume: | 34 |
| Issue: | 4 |
| ISSN: | 1053-4296 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2024-10-01 |
| Start Page: | 379 |
| End Page: | 394 |
| Language: | English |
| DOI: | 10.1016/j.semradonc.2024.07.012 |
| PUBMED: | 39271273 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Review -- MSK corresponding author is Joseph Deasy -- Source: Scopus |
