Investigator
Istanbul University
Association Between Adipose Tissue Distribution and Survival in Recurrent Ovarian Cancer Patients Treated With Anti‐ VEGF Therapy: A Retrospective CT ‐Based Analysis
ABSTRACT Objective The aim of this study was to evaluate the effect of visceral, subcutaneous and intermuscular adipose tissue areas measured by computed tomography at diagnosis on survival in ovarian cancer patients receiving second‐line bevacizumab treatment. Materials and Methods This retrospective study included 41 ovarian cancer patients who received second‐line bevacizumab treatment at Istanbul University Oncology Institute between 2009 and 2024. Visceral, subcutaneous and intermuscular adipose tissue areas at the L3 and T12 vertebral levels were measured from the computed tomography images of the patients at the time of diagnosis, and these areas were normalized by the square of body height and index values (cm 2 /m 2 ) were calculated. The effect of adipose tissue parameters on overall survival and disease‐free survival was analyzed. Results In multivariate analysis, only subcutaneous adipose tissue at the T12 level was found to be an independent predictor of overall survival. Subgroup analyses also showed that survival was significantly shorter in patients with low subcutaneous fat area or index. Similarly, low body mass index was also associated with unfavorable survival outcomes. Visceral and intermuscular adipose tissue parameters had no significant effect on survival. Conclusion Subcutaneous adipose tissue measured by diagnostic computed tomography may serve as a potential prognostic biomarker in ovarian cancer patients. These findings support the integration of body composition analysis into clinical decision‐making processes.
The “Undefined and Ignored Normal Tissue” Bulboclitoral Complex in Locally Advanced Cervical Cancer Treated with Definitive Radiochemotherapy: Is It Not the Organ at Risk?
Background and Objectives: The bulboclitoral complex (BCC) is an essential organ for female sexual health. However, it is not defined as an organ at risk in any guideline defining target volumes in radiotherapy of gynecological cancers, and there is no information about dose constraint. Materials and Methods: Simulation computed tomography scans of 20 patients diagnosed with locally advanced cervical cancer were used retrospectively. The volumetric modulated arc therapy treatment plan with a total dose of 45 Gy in 25 fractions was created using the planning target volume (PTV)-standard, which was created without considering the BCC, and the PTV-BCC spared, which were contoured and included in the optimization. Bulboclitoral complex doses in PTV-standard and PTV-BCC spared plans were compared using the paired simple t test. Results: Median BCC volume was 17.6 cm3 (11.20–25.50). Bulboclitoral complex maximum dose (Dmax) was median 49.07 Gy (48.49–50.25) and 28.81 Gy (18.14–44.61) in the PTV-standard and PTV-BCC spared plans, respectively, and the BCC Dmax was statistically significantly lower in the PTV-BCC spared plan (p < 0.001). When comparing BCC percentage of volume receiving 45 Gy (V45), the median values for PTV-standard and PTV-BCC spared plans were 37.5% (13.3–82.6) and 0%, respectively (p ≤ 0.001). Conclusions: The bulboclitoral complex can be dosimetrically protected from radiation by contouring and optimizing it as an organ at risk in the radiotherapy plan. The clinical effects of protecting the BCC from radiation as an organ at risk on sexual health need to be investigated.
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.
