A prediction nomogram for suboptimal debulking surgery in patients with serous ovarian carcinoma based on MRI T1 dual-echo imaging and diffusion-weighted imaging.

Background: Serous ovarian carcinoma (SOC) has the highest morbidity and mortality among ovarian carcinoma. Accurate identification of the probability of suboptimal debulking surgery (SDS) is critical. This study aimed to develop a preoperative prediction nomogram of SDS for patients with SOC.

Methods: A prediction model was established including 205 patients of SOC from institution A, and 45 patients from institution B were enrolled for external validation. Multivariate logistic regression was used to screen independent predictors and establish a nomogram to predict the occurrence of SDS.

Results: Multivariate logistic regression demonstrated that the CA-125 level (odds ratio [OR] 8.260, 95% confidence interval [CI] 2.003-43.372), relationship between the sigmoid colon/rectum and ovarian mass (OR 28.701, 95% CI 4.561-286.070), diaphragmatic metastasis (OR 12.369, 95% CI 1.675-274.063), and FIGO stage (OR 32.990, 95% CI 6.623-274.509) were independent predictors for SDS. The area under the curve, concordance index, and 95% CI of the nomogram constructed from the above four factors were 0.951, 0.934, and 0.919-0.982, respectively. The model showed a good fit by the Hosmer-Lemeshow test (training set, p = 0.2475; internal validation set, p = 0.2355; external validation set, p = 0.2707). The external validation proved the reliability of the prediction nomogram. The calibration curve was close to the ideal diagonal line. The decision curve analysis demonstrated a significantly better net benefit. The clinical impact curve indicated good effectiveness in clinical application.

Conclusion: A prediction nomogram for SDS in patients with SOC provides gynecologists with an accurate and effective tool for appropriate management.