| Title: |
Unlocking Tumor Aggressiveness in Endometrial Cancer: AI-Driven PET/CT Radiomics and Machine Learning for Prediction of High-Risk Tumor Histology |
| Authors: |
Samet Yagci; Evrim Erdemoglu; Mehmet Erdogan; Mustafa Avci; Ahmet Tunc; Ismail Ozkoc; Sevim Sureyya Sengul |
| Source: |
Cancers ; Volume 18 ; Issue 6 ; Pages: 905 |
| Publisher Information: |
Multidisciplinary Digital Publishing Institute |
| Publication Year: |
2026 |
| Collection: |
MDPI Open Access Publishing |
| Subject Terms: |
endometrial cancer; [18F]-FDG PET/CT; machine learning; radiomics |
| Description: |
Purpose: Accurate preoperative risk stratification in endometrial cancer (EC) is essential for guiding surgical and therapeutic decisions. This study aimed to evaluate the discriminative performance of [18F]-FDG PET/CT-derived radiomic features combined with machine learning models for differentiating low-risk (LRH-EC) and high-risk histology (HRH-EC) subtypes. Methods: A total of 159 patients with histopathologically confirmed EC who underwent preoperative [18F]-FDG PET/CT were retrospectively analyzed. Radiomic features were extracted using LIFEx version 7.4.0 software following IBSI guidelines. After FDR correction and Pearson correlation–based redundancy reduction (|r| > 0.80), 16 radiomic features were retained for modeling. Three feature configurations (Conventional PET parameters, Radiomics16, and Combined) were evaluated. Machine learning models were developed using stratified 5-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, F1-score, Wilson confidence intervals, DeLong’s test, and McNemar’s test. Results: Artificial Neural Network (ANN) (AUC = 0.709) and Random Forest (RF) (AUC = 0.686) achieved the highest discriminative performance within the Radiomics16 feature set. No statistically significant superiority between algorithms or feature configurations was observed by DeLong analysis. However, McNemar’s test demonstrated significant patient-level classification differences for the Combined ANN model (p < 0.001). NGTDM_Coarseness and SUVmin emerged as the most influential features, reflecting tumor heterogeneity and metabolic activity. Conclusions: [18F]-FDG PET/CT-based radiomics combined with machine learning provides moderate yet consistent discrimination between LRH-EC and HRH-EC. While external validation is required, this approach may support noninvasive preoperative risk stratification in endometrial cancer. |
| Document Type: |
text |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Clinical Research of Cancer; https://dx.doi.org/10.3390/cancers18060905 |
| DOI: |
10.3390/cancers18060905 |
| Availability: |
https://doi.org/10.3390/cancers18060905 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.2387B970 |
| Database: |
BASE |