Current status and future developments in predicting outcomes in radiation oncology.
Br J Radiol
; 95(1139): 20220239, 2022 Oct 01.
Article
in En
| MEDLINE
| ID: mdl-35867841
ABSTRACT
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiation Oncology
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Br J Radiol
Year:
2022
Type:
Article
Affiliation country:
United States