## 4 Discussion
Majority voting consistently improved classification accuracy, reinforcing the advantage of ensemble methods in reducing individual model biases and enhancing robustness in medical imaging tasks. Overall, the differences among models are likely due to the adopted architecture affecting the overall performance, for tumoral vs. non-tumoral classification, too. This discrimination is an endpoint generally investigated using magnetic resonance imaging (MRI) performed with multiple types of sequences [17]. Using this imaging modality, [PERSON] et al. [33] classified with homemade CNN 545 ovarian lesions as benign or malignant, using a dataset confirmed by either pathology or imaging follow-up, reporting an AUC of 0.91 \(\pm\) 0.05 and an F1-score of 0.87 \(\pm\) 0.06. A similar approach was implemented by [PERSON] et al. [34] to distinguish malignant and benignant ovaries, defined based on the consensus of the two radiologists, with AUC values ranging from 0.83 to 0.89, accuracy ranging from 0.81 to 0.87, sensitivity from 0.77 to 0.85, and specificity from 0.77 to 0.92. Moreover, [PERSON] et al. [35] used the T2-weighted MRI to distinguish between type I and type II NEOCs with a cohort of 437 cancer patients with an AUC [95% IC] of 0.86 [0.786, 0.946]. Similar results on CT images were reported by [PERSON] et al. [27] on 20 patients (i.e., 2560 benign and 2370 malignant slices) with the U-Net approach (based on VGG16 and ResNet152 combined with RF and Gradient Boosting) to segment and classify lesions in the ovarian district with the F1-score ranging from 0.819 to 0.918. Of note, we used a larger dataset of 119 CE-CT images (i.e., 10,752 benign and 19,712 malignant slices) analyzed with both ResNet50 and VGG19 associated with a GB algorithm, resulting in an F1-score of 0.880. These results seem promising considering that CE-CT is expected to have a lower image quality when compared to multiple sequences of MRI when used for diagnostic purposes by expert radiologists, thus suggesting that AI-based approaches can reveal details not visible by human eyes. Moreover, the accessibility of MRI is often limited by long acquisition times and the limited number of scanners relative to CT modality. For this reason, several attempts to use alternative imaging, including ultrasound (US), have been explored. [PERSON] et al. [36] used a pure ML approach on a dataset comprising 187 ovarian US images (112 benign, 75 malignant), obtaining an AUC of 0.877 with an SVM algorithm. Regarding data quality, while CE-CT generally offers a lower image quality than MRI, AI-driven CT analysis presents a compelling alternative due to the widespread availability and rapid acquisition of CT scans. This advantage makes CT-based AI models particularly valuable in clinical settings where MRI access is restricted, broadening their potential impact on ovarian cancer diagnostics. Our dataset was generated in a prospective study founded by the Italian Ministry of Health (ENDO-2021-12371926), thus guaranteeing the possibility to use harmonized protocols for CE-CT acquisition. On the contrary, most of the above-mentioned studies refer to retrospective datasets. Regarding automatic segmentation, significant efforts have been made to extend its application from ultrasound (US) imaging to various types of medical images [37,38]. Different segmentation techniques have been implemented for CT acquisitions; they are mainly divided into methods based on texture features and methods based on gray-level features, including amplitude segmentation based on histogram features, edge-based ones, and region-based ones [39]. In this context, AI-based algorithms can be seen as tools to further optimize these basic techniques. Nevertheless, although several AI-based tools for the automatic segmentation of organs have been developed and proven to reduce variability among young and experienced physicians [40], to the best of our knowledge, no software for automatic ovary segmentation is currently available for CT images.