Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

Background The optimal number of neoadjuvant chemotherapy cycles in patients with advanced ovarian cancer is still disputed. Objective To evaluate the impact of the number of neoadjuvant chemotherapy cycles and role of optimal cytoreduction on the prognosis of patients with advanced ovarian cancer. Methods Clinical and pathological details were examined. Patients were evaluated combining the number of cycles of neoadjuvant chemotherapy—namely, ‘interval debulking surgery’ after up to four neoadjuvant chemotherapy cycles, and ‘delayed debulking surgery’ after more than four cycles of therapy. Results A total of 286 patients were included in the study. Complete cytoreduction with no residual peritoneal disease (CC0) was achieved in 74 (74%) patients with interval debulking surgery and 124 (66.7%) patients with delayed interval debulking. Of those with residual disease, there were 26/88 (29.5%) patients in the interval debulking surgery group and 62/88 (70.5%) patients in the delayed debulking surgery group. Comparison of patients with delayed debulking-CC0 and interval debulking-CC0 showed no difference in progression-free survival (p=0.3) or overall survival (p=0.4), while significantly worse outcomes were observed in patients with interval debulking-CC1 (p=0.02 and p=0.04, respectively). Specifically, patients with interval debulking-CC1 had an approximately 67% increased risk of disease progression (p=0.04; HR=2.01 (95% CI 1.04 to 4.18)) and a 69% higher risk of death than patients with delayed debulking-CC0 (p=0.03; HR=2.34 (95% CI 1.11 to 4.67)). Conclusion Increasing the number of neoadjuvant chemotherapy cycles does not worsen patient outcomes if complete resection is achieved. Nevertheless, additional prospective trials are necessary to establish the optimum number of neoadjuvant chemotherapy cycles.

Coada CA, et al. J Gynecol Oncol. 2023 Sep;34:e82. https://doi.org/10.3802/jgo.2023.34.e82

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.

Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.