Identifying Key Challenges in Ovarian Tumor Classification: A Comparative Study Using Deep Learning and Radiomics

Ovarian tumor malignancy classification is a challenging task for radiologists using current algorithms, as they do not reach the required accuracy for clinical decision-making. The heterogeneous nature of ovarian tumors makes differentiation more difficult compared to other classification tasks, such as e.g. lung-nodule classification, even for experienced radiologists. The integration of computed tomography (CT) data with deep learning holds potential to improve accuracy, but its application remains in its infancy, where most previous work relies on radiomic feature extraction. This study compares a radiomics-based model and two deep learning models across both ovarian and lung datasets. The results demonstrate that although these models are promising, there is considerable variation in performance across the classification tasks, which reflects their level of difficulty. The performed feature analysis shows that ovarian tumors exhibit high feature heterogeneity and lack feature robustness, underscoring the need for novel methods to enhance feature differentiation. However, it is also shown that it is possible to gain performance for a majority of cases, by identifying features that can filter out outliers. Furthermore, the obtained results suggest that tumor volume plays a clear role in ovarian tumor classification, where large malignant tumors are most difficult to classify. These findings highlight that there is no one-size-fits-all solution to deep learning for tumor classification in CT, as model performance depends heavily on the task. Tailoring models to address dataset-specific challenges is critical to advancing their clinical utility. The code for our work is published on GitHub at: https://github.com/EloySchultz/OvaCADx_SPIE2025.