A framework for a personalized surgical approach to ovarian cancer

Abstract

The standard approach for the treatment of advanced-stage ovarian cancer is upfront cytoreductive surgery followed by a combination of platinum-based and taxane-based chemotherapy. The extent of residual disease following upfront cytoreductive surgery correlates with objective response to adjuvant chemotherapy, rate of pathological complete response at second-look assessment operations, and progression-free survival and overall survival. Contemporary data and meta-analyses indicate a correlation between volume of residual disease and patient outcome, with those patients undergoing complete gross resection having the best outcomes. Thus, attention has focused on surgical efforts to remove as much disease as possible with the metric of 'optimal' cytoreduction being R0 disease. Because patients with R0 resection seem to have the best overall outcomes, preoperative or intraoperative assessment to avoid unnecessary primary debulking surgery has become common. The use of serum CA-125 levels, physical examination and CT imaging have lacked accuracy in determining if disease can be optimally debulked. Therefore, an algorithm that identifies patients in whom complete gross resection at primary surgery is likely to be achieved would be expected to improve patient survival. We discuss contemporary definitions of 'optimal' residual disease, and opportunities to personalize surgical therapy and improve the quality of surgical care.

Figure 1

Plaque-like disease involving the right diaphragm

Figure 2

The Anderson Algorithm for laparoscopic disease assessment of patients with advanced ovarian cancer. PIV = predictive index value. TRS = tumor reductive surgery. NACT = neoadjuvant chemotherapy. QI = quality improvement

Figure 3

Novel clinical trial design for patients treated on the Anderson Algorithm. LSC = laparoscopic evaluation and score. WOO = window of opportunity. NACT = neoadjuvant chemotherapy. Pac = paclitaxel. Carbo = carboplatin.