| dc.creator |
SOYUPEK, Arap Sedat |
|
| dc.creator |
SEREL, Tekin Ahmet |
|
| dc.creator |
KAYMAK, Zümrüt Arda |
|
| dc.creator |
ÖZKAN, Emine Elif |
|
| dc.date |
2024-08-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:21:25Z |
|
| dc.date.available |
2025-02-25T10:21:25Z |
|
| dc.identifier |
4af488ee-8c10-4b24-9334-3e022403212e |
|
| dc.identifier |
10.1093/rpd/ncae154 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/4af488ee-8c10-4b24-9334-3e022403212e/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99602 |
|
| dc.description |
Objective: Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality of life in these patients with long survival is an important problem. In this study, we aimed to evaluate the possibility of predicting rectal toxicity with artificial intelligence model which was including certain dosimetric parameters. Materials and methods: One hundred and thirty-seven patients with a diagnosis of prostate cancer who received curative radiotherapy for prostate +/− pelvic lymphatics were included in the study. The association of the clinical data and dosimetric data between early and late rectal toxicity reported during follow-up was evaluated. The sample size was increased to 274 patients by synthetic data generation method. To determine suitable models, 15 models were studied with machine learning algorithms using Python 2.3, Pycaret library. Random forest classifier was used with to detect active variables. Results: The area under the curve and accuracy were found to be 0.89–0.97 and 95%–99%, respectively, with machine learning algorithms. The sensitivity values for acute and toxicity were found to be 0.95 and 0.99, respectively. Conclusion: 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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy |
|
| dc.type |
info:eu-repo/semantics/article |
|