A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma

Objectives. To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from nonOCCC and to develop a nomogram to preoperatively predict the probability of OCCC.Methods. This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis & LE;90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC. Results. The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)].Conclusions. A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach. & COPY; 2023 Elsevier Inc. All rights reserved.

Automated summary

A preoperative nomogram model was developed to predict the probability of ovarian clear cell carcinoma (OCCC) by analyzing clinical, laboratory, and radiological variables.