RESUMEN
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.
RESUMEN
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.
