SUrgical Access and Pattern of Recurrence of Endometrial Cancer: The SUPeR Study, a Multicenter Retrospective Observational Study.

STUDY OBJECTIVE: To evaluate recurrence rate and pattern in apparently early-stage endometrial cancer (EC) treated with minimally invasive surgery (MIS) and compare it to the "historical" populations treated by laparotomy. Secondary outcomes were to establish if, among MIS recurrent patients, intermediate-high/high-risk patients presented the same recurrence pattern compared to those at low/intermediate-risk and to evaluate time to first recurrence (TTR) of the study population. DESIGN: Multicenter retrospective observational study. SETTING: Five Italian Gynecologic Oncology referral centers. PATIENTS: All patients with proven recurrence of apparently early-stage EC treated with MIS from January 2017 to June 2022 . The laparotomic historical cohort was obtained from Laparoscopy Compared With Laparotomy for Comprehensive Surgical Staging of Uterine Cancer: Gynecologic Oncology Group Study (LAP2) and Laparoscopic Approach to Cancer of the Endometrium trials. INTERVENTIONS: Evaluation of recurrence rate and pattern. MEASUREMENTS AND MAIN RESULTS: Seventy-seven recurrences occurred on the total of 1028 patients treated with MIS for apparently early-stage EC during a median follow-up time of 36 months. The rate of recurrence in our cohort did not differ significantly from the rate of the historical cohort (7.4% vs 7.9%, odds ratio 0.9395, 95% CI 0.6901-1.2792). No significant differences were noticed for local, abdominal, nodal, and multiple site recurrence patterns; distant site recurrence appeared more likely in patients from the historical cohort. Postoperative low/intermediate risk patients had a higher likelihood of local recurrence compared to intermediate-high/high risk patients. Mean TTR was 19 months. No significant difference of TTR was observed for each pattern of recurrence compared to others. CONCLUSION: MIS appears to be safe for the treatment of early-stage EC. We did not identify any recurrence pattern specifically associated with MIS in early-stage EC.

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.

Where Morphological and Molecular Classifications Meet: The Role of p53 Immunohistochemistry in the Prognosis of Low-Risk Endometrial Carcinoma (GLAMOUR Study).

No prospective study has validated molecular classification to guide adjuvant treatment in endometrial cancer (EC), and not even retrospective data are present for patients with morphological low-risk EC. We conducted a retrospective, multicenter, observational study including 370 patients with low-risk endometrioid EC to evaluate the incidence and prognostic role of p53 abnormal expression (p53abn) in this specific subgroup. Among 370 patients, 18 had abnormal expressions of p53 (4.9%). In 13 out of 370 patients (3.6%), recurrences were observed and two were p53abn. When adjusting for median follow-up time, the odds ratio (OR) for recurrence among those with p53abn versus p53 wild type (p53wt) was 5.23-CI 95% 0.98-27.95, p = 0.053. The most common site of recurrence was the vaginal cuff (46.2%). One recurrence occurred within the first year of follow-up, and the patient exhibited p53abn. Both 1-year and 2-year DFS rates were 94.4% and 100% in the p53abn and p53wt groups, respectively. One patient died from the disease and comprised p53wt. No difference in OS was registered between the two groups; the median OS was 21.9 months (16.4-30.1). Larger multicenter studies are needed to tailor the treatment of low-risk EC patients with p53abn. Performing molecular classification on all EC patients might be cost-effective, and despite the limits of our relatively small sample, p53abn patients seem to be at greater risk of recurrence, especially locally and after two years since diagnosis.