Combined robotic surgery for concomitant treatment of endometrial cancer and obesity.

BACKGROUND: Endometrial Cancer (EC) is strongly linked to obesity. Bariatric surgery is recognized as a long-term solution for weight loss in severely obese patients. This pilot study investigates the feasibility, intraoperative and 30-day morbidity outcomes of integrating gynecological surgical staging and bariatric robotic surgery in class II and III obese patients affected by early EC or Endometrial Intraepithelial Neoplasia (EIN). METHODS: Patients aged over 18 years old with early EC or EIN and class II and III obesity (Body mass index (BMI) ≥ 35 kg/m2) who are surgical and anesthesiologic candidates. Standard robotic surgery for early EC staging performed alone (THBSO group) or in conjunction with sleeve gastrectomy (THBSO + SG group) for obesity management was proposed. RESULTS: Of the 13 patients who met the inclusion criteria, 5 (38.46%) opted for combined surgery. The groups showed a significant difference in preoperative BMI (49.68 kg/m2 vs. 40.24 kg/m2 p = 0.017 with and without SG), preoperative weight (143.92 kg vs. 105.62 kg p = 0.004 with and without SG), preoperative (p = 0.01) and postoperative (p = 0.005) aspartate transaminase (AST). The THBSO + SG group had higher anesthesia induction end-tidal carbon dioxide (ETCO2) (p = 0.05), final Partial pressure of carbon dioxide (PaCO2) (p = 0.044), anesthesia induction lactate (p = 0.001) and final lactate (p = 0.011) without a significant difference in final pH (p = 0.31). Operative time was longer in the THBSO + SG group (p < 0.001), but this did not result in longer ICU (p = 0.351), total hospital stays (p = 0.208), nor increased blood loss and transfusion. The simultaneous combined approach had an 80% success rate. At 6 months, the THBSO + SG group achieved significantly greater weight loss than the THBSO group (ΔBMI - 11.81 kg/m2 vs - 1.72 kg/m2, p = 0.003, with and without SG). CONCLUSION: Integrating robotic EC staging with SG in obese women with early EC increased the operative time without increasing intraoperative risks, early and 30 days post-surgery complication and offering a promising approach to simultaneously treating both conditions.

Post-operative residual disease and number of cycles of neoadjuvant chemotherapy in advanced epithelial ovarian carcinoma.

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.

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.

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.