Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy
The CatBoost algorithm shows high accuracy and reliability for survival prediction in locally advanced cervical cancer when clinical and radiomic features are combined. The addition of radiomics data significantly improves model performance.
Authors
- Deniz Yanık (ORCID: https://orcid.org/0000-0001-5676-0293)
- Bayarmaa Khishigsuren (ORCID: https://orcid.org/0009-0004-9455-6962)
- Murat Emeç (ORCID: https://orcid.org/0000-0002-9407-1728)
- Mustafa Durmaz (ORCID: https://orcid.org/0009-0000-4848-5201)
- Seden Küçücük (ORCID: https://orcid.org/0000-0002-6984-3466)
- Kamuran İbiş (ORCID: https://orcid.org/0000-0001-7928-6264)
- Merve Gülbiz Kartal (ORCID: https://orcid.org/0000-0001-9018-4736)
- Mustafa Denizli (ORCID: https://orcid.org/0009-0005-4773-3652)
- İnci Kızıldağ Yirgin (ORCID: https://orcid.org/0000-0003-0563-5964)
- Ayça İribaş Çelik (ORCID: https://orcid.org/0000-0002-1686-7114)
- Irem Bunul (ORCID: https://orcid.org/0000-0002-9846-5660)
- Erkin Akyuz (ORCID: https://orcid.org/0000-0002-5007-7765)
Institutions
- Erzurum Regional Training and Research Hospital (TR)
- Istanbul University (TR)
Publication Details
- Journal
- Current Oncology
- Published
- 2025-10-27
- DOI
- https://doi.org/10.3390/curroncol32110602
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00