Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer

Abstract

Objective: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. Methods: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. Results: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77–0.90) and 0.82 (95% CI: 0.71–0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. Conclusions: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and II EOC. Advances in knowledge: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists noninvasively identify EOC types before surgery.

Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment

“…Our nomogram is the first nomogram that is specifically applied to OC patients with PE treatment. Meanwhile, compared to reported studies of OC patients, all variables included in our nomograms are objective and not affected by the subjective evaluation of doctors and the individual feelings of patients [ 28 – 30 ]. Also, all variables could be obtained easily in clinical practice, which facilitated its wide application in clinical practice.…”

Development of Nomogram Models Based on Peripheral Blood Score and Clinicopathological Parameters to Predict Preoperative Advanced Stage and Prognosis for Epithelial Ovarian Cancer Patients

“…There are many different types of models based on clinicopathological parameters, imaging characteristics, or genetic signature to predict the stage, pathologic types, surgical outcomes, and reaction to chemotherapy, OS, or PFS, [42][43][44][45][46][47][48] but their practicability is limited partly owing to the expensive and redundant examination such as molecular diagnosis or genomic sequence. We urgently need models grounded on clinical variables with superior convenience.…”

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

“…Ultrasound is the first imaging modality for the assessment of ovarian pathologies but the differential diagnosis between different entities may be challenging based only on the qualitative assessment. Initial single-centre studies applied the radiomics analysis on ultrasound images to predict the histopathological types and grades of epithe-lial ovarian cancers [145,146] and prognosis [147] with reported good performances but validation can be problematic as the ultrasound images' acquisitions depend on operator experience. Considering the main aim of this review and the application of CT and MRI in clinical practice, further discussion will be focused on the application of cross-section imaging in different scenarios.…”