Substantial discordance between structured pre-operative computed tomography (CT) reports and intraoperative findings in advanced ovarian cancer cytoreductive surgery, affecting treatment decisions
The aim of this study was to assess the agreement and diagnostic accuracy of structured preoperative computed tomography (CT) findings compared to intraoperative findings in advanced ovarian cancer patients undergoing primary or interval cytoreductive surgery. Patients with CT scans suggesting advanced ovarian cancer were enrolled in the study. Agreement between CT reports, reviewed using European Society of Urogenital Radiology (ESUR) criteria, and surgical findings were evaluated with the kappa coefficient. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each radiologic feature. From February 2018 to September 2020, 258 patients with International Federation of Gynaecology and Obstetrics (FIGO) stage IIIB-IV epithelial ovarian cancer were enrolled. Agreement between ESUR-reviewed CT reports and surgical findings was slight to fair (kappa = 0.115-0.352). The most common CT findings were peritoneal carcinomatosis, omental metastases, and bowel involvement. Sensitivity and specificity of peritoneal carcinomatosis were 0.91 (95% confidence interval [CI]: 0.86-0.94) and 0.19 (95% CI: 0.10-0.31), with an area under the receiver operating characteristic curve (AUC) of 0.55 (95% CI: 0.46-0.64). Omental metastases had a sensitivity of 0.91 (95% CI: 0.87-0.95) and specificity of 0.27 (95% CI: 0.16-0.40) with an AUC of 0.59 (95% CI: 0.52-0.65). Bowel involvement showed a sensitivity of 0.61 (95% CI: 0.54-0.67), specificity of 0.71 (95% CI: 0.58-0.83), and AUC of 0.66 (95% CI: 0.58-0.74). This study demonstrates limited concordance between ESUR-reviewed CT reports and intraoperative findings in advanced ovarian cancer. Even when interpreted by expert radiologists, CT imaging alone may inadequately reflect disease burden. These findings emphasise the ongoing challenges of imaging-based surgical planning and support the need for further development and validation of more accurate preoperative assessment tools.