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.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

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.