OBJECTIVE: To test the applicability of the Arbeitsgemeinschaft Gynäkologische Onkologie (AGO) and Memorial Sloan Kettering (MSK) criteria in predicting complete cytoreduction (CC) in patients undergoing secondary cytoreductive surgery (SCS) for recurrent ovarian cancer (ROC). METHODS: Data of consecutive patients undergoing SCS were reviewed. The Arbeitsgemeinschaft Gynäkologische Onkologie OVARian cancer study group (AGO-OVAR) and MSK criteria were retrospectively applied. Nomograms, based on AGO criteria, MSK criteria and both AGO and MSK criteria were built in order to assess the probability to achieve CC at SCS. RESULTS: Overall, 194 patients met the inclusion criteria. CC was achieved in 161 (82.9%) patients. According to the AGO-OVAR criteria, we observed that CC was achieved in 87.0% of patients with positive AGO score. However, 45 out of 71 (63.4%) patients who did not fulfilled the AGO score had CC. Similarly, CC was achieved in 87.1%, 61.9% and 66.7% of patients for whom SCS was recommended, had to be considered and was not recommended, respectively. In order to evaluate the predictive value of the AGO-OVAR and MSK criteria we built 2 separate nomograms (c-index: 0.5900 and 0.5989, respectively) to test the probability to achieve CC at SCS. Additionally, we built a nomogram using both the aforementioned criteria (c-index: 0.5857). CONCLUSION: The AGO and MSK criteria help identifying patients deserving SCS. However, these criteria might be strict, thus prohibiting a beneficial treatment in patients who do not met these criteria. Further studies are needed to clarify factors predicting CC at SCS.
Cytoreduction Surgical Procedures ; Humans ; Nomograms ; Ovarian Neoplasms* ; Recurrence ; Retrospective Studies

OBJECTIVE: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. METHODS: This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. RESULTS: Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included: disease-free interval (DFI) (importance: 0.231), retroperitoneal recurrence (importance: 0.178), residual disease at primary surgical treatment (importance: 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance: 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance: 0.306). Other important variables included: CC (importance: 0.217), and FIGO stage at presentation (importance: 0.100). CONCLUSION: According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process.
Artificial Intelligence* ; Decision Making ; Gynecology ; Humans ; Neurons ; Obstetrics ; Ovarian Neoplasms* ; Recurrence ; Retrospective Studies ; Weights and Measures*