MEMurat Emec
Papers(1)
Machine Learning-Base…
Collaborators(10)
Mustafa DenizliMustafa DurmazNezihe Seden KucucukAyca Iribas CelikBayarmaa KhishigsurenDeniz YanikErkin Akyuzİnci Kizildag YırgınIrem BunulKamuran Ibis
Institutions(2)
Istanbul UniversityMsd Turkiye

Papers

Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy

Background: This study aims to evaluate the contribution of clinical and radiomic features to machine learning-based models for survival prediction in patients with locally advanced cervical cancer. Methods: Clinical and radiomic data from 161 patients were retrospectively collected from a single center. Radiomic features were obtained from contrast-enhanced magnetic resonance imaging (MRI) T1-weighted (T1W), T2-weighted (T2W), and diffusion-weighted (DWI) sequences. After data cleaning, feature engineering, and scaling, survival prediction models were created using the CatBoost algorithm with different data combinations (clinical, clinical + T1W, clinical + T2W, clinical + DWI). The performance of the models was evaluated using test accuracy, precision, recall, F1-score, ROC curve, and Bland–Altman analysis. Results: Models using both clinical and radiomic features showed significant improvements in accuracy and F1-score compared to models based solely on clinical data. In particular, the CatBoost_CLI + T2W_DMFS model achieved the best performance, with a test accuracy of 92.31% and an F1-score of 88.62 for distant metastasis-free survival prediction. ROC and Bland–Altman analyses further demonstrated that this model has high discriminative power and prediction consistency. Conclusions: 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.

29Works
1Papers
11Collaborators
Uterine Cervical NeoplasmsPrognosis

Positions

2026–

Researcher

İstanbul Nişantaşı Üniversitesi

2022–

PhD

Istanbul University · Computer Science

2021–

Öğretim Görevlisi Dr. / Lecturer PhD

Marmara University

2010–

Researcher

Dokuz Eylül University · Department of Information Technologies

Education

2022

Dokuz Eylül Üniversitesi · Computer Engineering(PhD)

2016

Dokuz Eylül Üniversitesi · Management information systems

2015

Ege Üniversitesi · Computer Enginering

Keywords
Murat EMEÇM. EmecM. EmeçEmecM.EmeçM.