The peritoneal carcinomatosis index (PCI) can be used to quantify the tumor burden in patients with advanced ovarian cancer. The aim of the present study was to establish a predictive model for suboptimal cytoreductive surgery (SCS) (residual tumor of > 1 cm) using preoperative and intraoperative determination of the PCI. In total, 110 consecutive patients treated for advanced ovarian cancer during a 4-year period in our institution were assessed. Eighty of these patients were eligible for primary debulking surgery and thus included in the present study. All data were prospectively collected and retrospectively evaluated. We determined the PCI both preoperatively and intraoperatively and assessed postoperative complications. A PCI of > 20 was the best cut-off with which to predict a risk of SCS among all three diagnostic techniques assessed in this study (computed tomography, laparoscopy, and laparotomy). Intraoperative PCI determination was associated with the lowest risk of false negatives for SCS when detecting a PCI of < 20. The combination of preoperative computed tomography and laparoscopy, when both techniques predicted SCS, was associated with the lowest risk of false positives for SCS when detecting a PCI of > 20. The combination of computed tomography and laparoscopy to obtain the PCI can help to determine which patients with advanced ovarian cancer are suitable for primary debulking surgery and which should undergo neoadjuvant chemotherapy.

The standard treatment for advanced ovarian cancer (AOC) is cytoreduction surgery and adjuvant chemotherapy. Tumor volume after surgery is a major prognostic factor for these patients. The ability to perform complete cytoreduction depends on the extent of disease and the skills of the surgical team. Several predictive models have been proposed to evaluate the possibility of performing complete cytoreductive surgery (CCS). External validation of the prognostic value of three predictive models (Fagotti index and the R3 and R4 models) for predicting suboptimal cytoreductive surgery (SCS) in AOC was performed in this study. The scores of the 3 models were evaluated in one hundred and three consecutive patients diagnosed with AOC treated in a tertiary hospital were evaluated. Clinicopathological features were collected prospectively and analyzed retrospectively. The performance of the three models was evaluated, and calibration and discrimination were analyzed. The calibration of the Fagotti, R3 and R4 models showed odds ratios of obtaining SCSs of 1.5, 2.4 and 2.4, respectively, indicating good calibration. The discrimination of the Fagotti, R3 and R4 models showed an area under the ROC curve of 83%, 70% and 81%, respectively. The negative predictive values of the three models were higher than the positive predictive values for SCS. The three models were able to predict suboptimal cytoreductive surgery for advanced ovarian cancer, but they were more reliable for predicting CCS. The R4 model discriminated better because it includes the laparotomic evaluation of the peritoneal carcinomatosis index.

Objective: Peritoneal carcinomatosis is a disease’s presentation in the advanced stages of many gynecologic tumours. The distribution and volume of the disease are the main factors in achieving complete debulking. Diagnostic laparoscopy is a technique to allow evaluation of the disease. This study’s objective is to compare two laparoscopic scores (Fagotti’s index and Sugarbaker’s peritoneal cancer index (PCI)) and assess the diagnostic accuracy to select patients for neoadjuvant treatment and reduce unnecessary laparotomies. Methods: A non-randomised retrospective cohort study was conducted in patients with peritoneal carcinomatosis (ovarian and endometrial origin) who underwent laparoscopy and subsequent laparotomy. We evaluated the scores’ ability to predict incomplete surgery and whether they were related to the patients’ prognosis. Results: We included 34 patients, of which 23.5% received neoadjuvant chemotherapy. The rate of complete cytoreductive surgery was 79.4% (n = 27 patients). The highest sensitivity was obtained with a PCI value greater than 20. It was the best parameter to determine incomplete debulking. Survival curves were analysed according to the “cut off” established for each score, and statically significant differences were found using PCI with respect to Fagotti’s Index. However, these differences were not found with Fagotti’s score. Conclusion: The best diagnostic method to classify patients with peritoneal cancer is the PCI. It could be adapted to each surgical team because it allows identifying the “cut off point”, which depends on incomplete surgery rate.

Background: The second cytoreductive surgery performed for a patient who has recurrent ovarian cancer remains controversial. Our study analyzes overall survival (OS) and disease-free survival (DFS) for cytoreductive surgery in addition to chemotherapy in recurrent ovarian cancer instead of chemotherapy alone. Methods: A meta-analysis was conducted using PubMed and the Cochrane database of systematic reviews to select randomized controlled studies. In total, three randomized studies were used, employing a total of 1249 patients. Results: The results of our meta-analysis of these randomized controlled trials identified significant differences in OS (HR = 0.83, IC 95% 0.70–0.99, p < 0.04) and DFS (HR = 0.63, IC 95% 0.55–0.72, p < 0.000001). A subgroup analysis comparing complete cytoreductive surgery and surgery with residual tumor achieved better results for both OS (HR = 0.65, IC 95% 0.49–0.86, p = 0.002) and DFS (HR = 0.67, IC 95% 0.53–0.82, p = 0.0008), with statistical significance. Conclusions: A complete secondary cytoreductive surgery (SCS) in recurrent ovarian cancer (ROC) demonstrates an improvement in the OS and DFS, and this benefit is most evident in cases where complete cytoreductive surgery is achieved. The challenge is the correct patient selection for secondary cytoreductive surgery to improve the results of this approach.

Introduction: Medical models assist clinicians in making diagnostic and prognostic decisions in complex situations. In advanced ovarian cancer, medical models could help prevent unnecessary exploratory surgery. We designed two models to predict suboptimal or complete and optimal cytoreductive surgery in patients with advanced ovarian cancer. Methods: We collected clinical, pathological, surgical, and residual tumor data from 110 patients with advanced ovarian cancer. Computed tomographic and laparoscopic data from these patients were used to determine peritoneal cancer index (PCI) and lesion size score. These data were then used to construct two-by-two contingency tables and our two predictive models. Each model included three risk score levels; the R4 model also included operative PCI, while the R3 model did not. Finally, we used the original patient data to validate the models (narrow validation). Results: Our models predicted suboptimal or complete and optimal cytoreductive surgery with a sensitivity of 83% (R4 model) and 69% (R3 model). Our results also showed that PCI>20 was a major risk factor for unresectability. Conclusion: Our medical models successfully predicted suboptimal or complete and optimal cytoreductive surgery in 110 patients with advanced ovarian cancer. Our models are easy to construct, based on readily available laboratory test data, simple to use clinically, and could reduce unnecessary exploratory surgery in this patient group.