Antonio Gil-Moreno

Background/Objectives: Universal surgical risk calculators are not validated for use and poorly predict postoperative morbidity and mortality for women undergoing gynaecological oncology surgery. This adversely affects communication of risk resulting in poorly informed decisions and missed opportunities for medical optimization to mitigate risk preoperatively. We present the development and external validation of our novel GO SOAR surgical risk calculator for use to preoperatively predict postoperative thirty-day surgical morbidity and mortality in relation to gynaecological oncology surgeries. Methods: New logistic regression models were developed using the GO SOAR1 training cohort (n = 1811) and externally validated in an independent prospective cohort (n = 416) for two outcomes: thirty-day postoperative mortality (alive versus dead) and thirty-day postoperative morbidity (any complication (Clavien–Dindo I–V) versus none). Performance of the GO SOAR models was compared against established all-purpose surgical risk calculators (SORT/POSSUM/P-POSSUM/NSQIP). Model discrimination was assessed with sensitivity calculated at a clinically significant prespecified specificity threshold of 90%. Results: For mortality, AUROC was 0.752 (95% CI 0.570–0.935) for GO SOAR full and 0.795 (95% CI 0.660–0.930) for GO SOAR condensed; corresponding sensitivities at 90% specificity were 57.1% and 42.9%. For morbidity, AUROC was 0.698 (95% CI 0.641–0.755) and 0.703 (95% CI 0.646–0.760) for the full and condensed models, respectively, with sensitivities of 30.4% and 32.1% at 90% specificity. Conclusions: The GO SOAR model using a data-driven, gynaecological-oncology-specific approach at 90% specificity, achieved the highest observed sensitivity among the evaluated calculators. Accurate surgical risk predictions are crucial for major oncological surgery, where complications can diminish quality of life and affect long-term cancer survival. A model such as the GO SOAR surgical risk calculator that uses readily available preoperative data, regardless of income setting, is essential in reducing global disparities in surgical outcomes.

Intratumor heterogeneity presents a major challenge in precision oncology for endometrial cancer (EC). Circulating tumor DNA (ctDNA) offers a minimally invasive method to monitor tumor evolution and therapeutic resistance. In this retrospective study, we evaluated a tumor‐agnostic NGS panel to detect and track ctDNA in 18 EC patients and directly compared its performance with a tumor‐informed ddPCR approach. ctDNA was detected by NGS in over 60% of plasma samples, while ddPCR showed higher positivity rates in paired samples (71.9% vs 62.5%), with overall concordance of 65.7% and fair agreement (Cohen's kappa = 0.23). The cfDNA‐NGS panel identified a broad spectrum of alterations, including relapse‐specific mutations indicative of clonal evolution, but showed lower sensitivity for low‐frequency variants compared with ddPCR. Discordant cases, including false‐negative results in both approaches, highlight the impact of assay sensitivity, target selection, and biological factors on ctDNA detection. ctDNA dynamics correlated with disease progression and treatment response, although detection was limited in cases with brain metastases. These findings support the utility of tumor‐agnostic ctDNA monitoring in advanced EC and highlight the importance of assay quality and careful interpretation to address limitations such as clonal hematopoiesis and technical sensitivity.

Objective: The aim of this thesis was to externally validate a predictive model of suboptimal surgery in advanced ovarian cancer, developed by doctors Escrig and Llueca. The model classifies patients pre-surgically to estimate the likelihood of incomplete cytoreductive surgery. Methods: A retrospective cohort comparison between two time periods was performed. Validation used a new cohort of 83 patients with advanced ovarian cancer, prospectively collected between 2017 and 2023 across five hospitals (experimental group). This group was compared with the original control cohort (2013–2016), which had served for model development. The predictive models (R3 and R4) are based on the Peritoneal Carcinomatosis Index (PCI) assessed by CT, laparoscopic PCI, and the presence of intestinal sub-obstruction. For model R4, intraoperative PCI was also included. Results: The experimental group had a lower rate of suboptimal cytoreduction compared with the control group (4.8% vs. 13.8%; p = 0.049). Significant differences were observed in ascites (49.4% vs. 27.5%; p = 0.002), and no patient in the experimental group presented intestinal sub-obstruction (0% vs. 8%; p = 0.002). Although at least 13 suboptimal surgeries were expected for validation, only four occurred. The predictive models did not classify any of these four cases as high risk, instead categorizing them as low or intermediate risk. Conclusions: Statistical external validation could not be performed due to event scarcity. This reduced incidence is attributed to selection bias: highly experienced surgical teams from participating centres likely applied criteria similar to those of the model, referring high risk patients (e.g., with intestinal sub-obstruction) to neoadjuvant therapy and thus avoiding suboptimal primary surgeries. Although direct validation was not possible, the findings indirectly suggest that the model is effective in guiding patient selection and improving surgical outcomes.

Objective: Our study evaluated the time to normalization of postvoid residual volume after radical hysterectomy and identified risk factors for postoperative bladder dysfunction. We also aimed to establish a predictive threshold for bladder dysfunction on the third postoperative day to develop a decision-making algorithm for postoperative voiding management. Methods: This retrospective, single-center study included early-stage cervical cancer patients undergoing type B1 or C1 radical hysterectomy. Factors associated with elevated postvoid residual volume were analyzed using logistic regression, and the threshold was determined using the Youden index. Results: 67 patients were included: 36 patients (53.7%) underwent C1 radical hysterectomy and 31 (46.3%) B1. At discharge, 13 (19.4%) patients required a catheter: 8 (61.5%) required intermittent catheterization, 5 (38.5%) had a Foley catheter. By postoperative day 3, 49 (73.1%) patients recovered their voiding function. The median time to postvoid residual volume recovery was 1 day (IQR: 1–2) for type B1 and 2.5 days (IQR: 2–5) for type C1 (p < 0.01). Compared with B1, C1 radicality was independently associated with a higher risk of postoperative voiding dysfunction (OR = 11.46; 95% CI: 1.75–75.24; p < 0.05). Based on these findings, we propose an algorithm for risk-adapted postoperative voiding management: B1 patients can safely have catheters removed on postoperative day 1 without a voiding trial, whereas C1 patients require one. C1 patients with postvoid residual volume ≥170 mL should have delayed catheter removal. Conclusions: Surgical radicality is a risk factor for postoperative bladder dysfunction. In type C1 radical hysterectomy, a postvoid residual volume ≥170 mL on the first postoperative day identifies patients at high risk of delayed recovery, supporting a tailored approach to postoperative voiding management.