A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer

C. Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coadǎ, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari
In: Applied Sciences · 2025 · vol. 15(6) , pp. 3070 · doi:10.3390/app15063070 · W4408366425
article OA: gold CC0 ⤵ 2 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-06 ⓘ

This study developed hybrid convolutional neural network models using contrast-enhanced CT images to differentiate endometriosis-associated ovarian cancer from other ovarian cancers and non-tumoral tissues.

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Abstract

Background/Objectives: The aim of this study was the early identification of endometriosis-associated ovarian cancer (EAOC) versus non-endometriosis associated ovarian cancer (NEOC) or non-cancerous tissues using pre-surgery contrast-enhanced-Computed Tomography (CE-CT) images in patients undergoing surgery for suspected ovarian cancer (OC). Methods: A prospective trial was designed to enroll patients undergoing surgery for suspected OC. Volumes of interest (VOIs) were semiautomatically segmented on CE-CT images and classified according to the histopathological results. The entire dataset was divided into training (70%), validation (10%), and testing (20%). A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. The results of each CNN model for each slice within a VOI were combined using three rival machine learning (ML) models, i.e., Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbor (KNN), to obtain a final output distinguishing between EAOC and NEOC, and between EAOC/NEOC and non-tumoral tissues. Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. Each model’s performance was assessed based on the area under the receiver operating characteristic (ROC) curve (AUC), F1-score, sensitivity, and specificity. These metrics were then integrated into a Machine Learning Cumulative Performance Score (MLcps) to provide a comprehensive evaluation on the test dataset. Results: An MLcps value of 0.84 identified the VGG19 + majority voting ensemble as the optimal model for distinguishing EAOC from NEOC, achieving an AUC of 0.85 (95% CI: 0.70–0.98). In contrast, the VGG19 + SVM model, with an MLcps value of 0.76, yielded an AUC of 0.79 (95% CI: 0.63–0.93). For differentiating EAOC/NEOC from non-tumoral tissues, the VGG19 + SVM model demonstrated superior performance, with an MLcps value of 0.93 and an AUC of 0.97 (95% CI: 0.92–1.00). Conclusions: Hybrid models based on CE-CT have the potential to differentiate EAOC and NEOC patients as well as between OC (EAOC and NEOC) and non-tumoral ovaries, thus potentially supporting gynecological surgeons in personalized surgical approaches such as more conservative procedures.

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endometriosis

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last seen: 2026-06-04T00:00:01.174412+00:00
License: CC0 · commercial use OK