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

Abstract

Abstract 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.