Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.
Emine Elif OzkanTekin Ahmet SerelArap Sedat SoyupekZumrut Arda KaymakPublished in: Radiation protection dosimetry (2024)
Early or late rectal toxicity can be predicted with a high probability via dosimetric and physical data and machine learning algorithms of patients who underwent prostate +/- pelvic radiotherapy. The fact that rectal toxicity can be predicted before treatment, which may result in limiting the dose and duration of treatment, makes us think that artificial intelligence can enter our daily practice in a short time in this sense.
Keyphrases
- machine learning
- artificial intelligence
- rectal cancer
- big data
- prostate cancer
- locally advanced
- deep learning
- radiation therapy
- early stage
- oxidative stress
- end stage renal disease
- physical activity
- prognostic factors
- primary care
- chronic kidney disease
- healthcare
- ejection fraction
- radiation induced
- liver failure
- squamous cell carcinoma
- intensive care unit
- data analysis
- oxide nanoparticles