Introduction/Background 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.
Methodology 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 findings emphasize that the proper application of our R3–R4 models in primary debulking surgery requires maximal surgical effort including UAS techniques and well-prepared multidisciplinary surgical teams. These two models can predict CCS+OCS and SCS, depending on the SCS prevalence of the surgical team. The dynamic nature of the models means that they can be applied in different institutions with different OCS or SCS rates,and they can be changed with changes in the SCS prevalence of the surgical team. The proper integration of the R3 and R4 models with recently-developed molecular and clinical-radiological models, including age, performance status, and comorbidities may help improve the decision-making process in the future.
Disclosure Nothing to disclose.
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