Cargando…

Current status and future developments in predicting outcomes in radiation oncology

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, l...

Descripción completa

Detalles Bibliográficos
Autores principales:	Niraula, Dipesh, Cui, Sunan, Pakela, Julia, Wei, Lise, Luo, Yi, Ten Haken, Randall K, El Naqa, Issam
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The British Institute of Radiology. 2022
Materias:	Review Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9793488/ https://www.ncbi.nlm.nih.gov/pubmed/35867841 http://dx.doi.org/10.1259/bjr.20220239

Ejemplares similares

Interpretable artificial intelligence in radiology and radiation oncology
por: Cui, Sunan, et al.
Publicado: (2023)

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy
por: Niraula, Dipesh, et al.
Publicado: (2021)

Author Correction: Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy
por: Niraula, Dipesh, et al.
Publicado: (2023)

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling
por: Luo, Yi, et al.
Publicado: (2019)

A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy
por: Luo, Yi, et al.
Publicado: (2022)

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration
por: Wei, Lise, et al.
Publicado: (2023)

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy
por: Tseng, Huan-Hsin, et al.
Publicado: (2018)

A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS)
por: Niraula, Dipesh, et al.
Publicado: (2023)

Editorial: Machine Learning With Radiation Oncology Big Data
por: Deng, Jun, et al.
Publicado: (2018)

Machine learning in radiation oncology: theory and applications
por: El Naqa, Issam, et al.
Publicado: (2015)

The big data effort in radiation oncology: Data mining or data farming?
por: Mayo, Charles S., et al.
Publicado: (2016)

Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice
por: Li, Hua, et al.
Publicado: (2020)

Serum Levels of Hepatocyte Growth Factor and CD40 Ligand Predict Radiation-Induced Liver Injury()()()
por: Cuneo, Kyle C., et al.
Publicado: (2019)

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation
por: Kang, John, et al.
Publicado: (2021)

Radiation Oncology Research in Asia: Current Status and a Peep Into the Future From the Federation of Asian Organizations for Radiation Oncology
por: Chopra, Supriya, et al.
Publicado: (2023)

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy
por: Owen, Daniel R., et al.
Publicado: (2021)

In silico trials of combination immuno-radiation for unresectable hepatocellular carcinoma
por: Mendiratta-Lala, Mishal, et al.
Publicado: (2023)

Radiation oncology as part of medical education—current status and possible digital future prospects
por: Dapper, Hendrik, et al.
Publicado: (2020)

Mobile applications in radiation oncology—current choices and future potentials
por: Janssen, Stefan, et al.
Publicado: (2023)

Genitourinary Oncology: Current Status and Future Challenges
por: Bukowski, Ronald M.
Publicado: (2011)

Tumor response prediction in (90)Y radioembolization with PET-based radiomics features and absorbed dose metrics
por: Wei, Lise, et al.
Publicado: (2020)

Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis
por: Hinton, Tonaye, et al.
Publicado: (2022)

Radiation Metabolomics: Current Status and Future Directions
por: Menon, Smrithi S., et al.
Publicado: (2016)

Current radiation oncology /

Current radiation oncology
Publicado: (1996)

Targeting K-Ras-mediated DNA damage response in radiation oncology: Current status, challenges and future perspectives
por: Toulany, Mahmoud
Publicado: (2022)

Psycho-oncology: History, Current Status, and Future Directions in Japan
por: Akechi, Tatsuo
Publicado: (2018)

X-change symposium: status and future of modern radiation oncology—from technology to biology
por: Corradini, Stefanie, et al.
Publicado: (2021)

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries
por: Jochems, Arthur, et al.
Publicado: (2017)

Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data
por: Spencer, Sarah J., et al.
Publicado: (2009)

Oncologic Photodynamic Therapy: Basic Principles, Current Clinical Status and Future Directions
por: van Straten, Demian, et al.
Publicado: (2017)

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives
por: Chiu, Fang-Ying, et al.
Publicado: (2023)

Use of acupuncture to alleviate side effects in radiation oncology: Current evidence and future directions
por: Asadpour, Rebecca, et al.
Publicado: (2016)

Microwave Radiation and the Brain: Mechanisms, Current Status, and Future Prospects
por: Mumtaz, Sohail, et al.
Publicado: (2022)

Novel unconventional radiotherapy techniques: Current status and future perspectives – Report from the 2nd international radiation oncology online seminar
por: Tubin, S., et al.
Publicado: (2023)

Current concepts in clinical radiation oncology
por: Orth, Michael, et al.
Publicado: (2013)

Big Data Analytics for Prostate Radiotherapy
por: Coates, James, et al.
Publicado: (2016)

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges
por: Coates, James T. T., et al.
Publicado: (2021)

Current status and developments of German curriculum-based residency training programmes in radiation oncology
por: Büttner, Marcel, et al.
Publicado: (2021)

Interventional Oncology and Immuno-Oncology: Current Challenges and Future Trends
por: Posa, Alessandro, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_4q0s7bst06r84utnc21dhinpo1