The paradigm shift in advanced ovarian cancer: Outcomes of extensive primary cytoreductive surgery. A single-center retrospective analysis.

OBJECTIVE: The standard surgical treatment of advanced ovarian carcinoma is primary debulking surgery (PDS) aiming to complete cytoreduction. The need to achieve complete cytoreduction has shifted the surgical paradigm to more complex procedures, whose impact on morbidity is controversial. The objective of this retrospective analysis is to explore the impact of extensive PDS on morbidity and oncologic outcomes in a real-world scenario. METHODS: A retrospective single-center analysis was performed on 137 patients with advanced high-grade ovarian carcinoma (HGOC) who received PDS in 2015-2020. Patients treated in 2015-2017 (Group 1) were compared to patients treated in 2018-2020 (Group 2). The two periods were chosen according to the higher complexity of surgical procedures introduced in 2018. RESULTS: The increase in complete cytoreduction observed in Group2 (RD 0: 33 % vs 61 %, p = 0,008) was related to a higher surgical complexity (Aletti Score: 4 vs 6, p = 0,003) and did not reflect an increase in peri-operative complications (CCI: 20,9 vs 20,9, p = 0,11). After a median FUP of 44 months, PFS and OS at 24 months were 33,60 % vs 47,33 % (p = 0,288) and 72,10 % vs 80,37 % (p = 0,022) in Group 1 and 2, respectively. CONCLUSIONS: An extensive surgical effort leads to a significant increase in complete cytoreduction with acceptable morbidity. Arm-in-arm with novel maintenance therapies, it contributes to increasing the outcomes of patients with advanced HGOC.

Low-Volume Metastases in Cervical Cancer: Does Size Matter?

The implementation of sentinel lymph node (SLN) biopsy is changing the scenario in the surgical treatment of early-stage cervical cancer, and the oncologic safety of replacing bilateral pelvic lymphadenectomy with SLN biopsy is currently under investigation. Part of the undisputed value of SLN biopsy is its diagnostic accuracy in detecting low-volume metastases (LVM) via pathologic ultrastaging. In early-stage cervical cancer, the reported incidence of LVM ranges from 4 to 20%. The prognostic impact and the role of adjuvant treatment in patients with LVM is still unclear. Some non-prespecified analyses in prospective studies showed no impact on the oncologic outcomes compared to node-negative disease. However, the heterogeneity of the studies, the differences in the disease stage and the use of adjuvant treatment, and the concomitant pelvic lymphadenectomy (PLND) make reaching any conclusions on this topic hard. Current guidelines suggest considering micrometastases (MIC) as a node-positive disease, while considering isolated tumor cells (ITC) as a node-negative disease with a low level of evidence. This review aims to highlight the unanswered questions about the definition, identification, and prognostic and therapeutic roles of LVM and to underline the present and future challenges we are facing. We hope that this review will guide further research, giving robust evidence on LVM and their impacts on clinical practice.

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.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.