Abstract
ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.MethodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.ResultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.ConclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.
Highlights
Ovarian cancer (OC) is the deadliest form of gynecological malignancy, which consists of approximately one fourth of all the gynecological cancers but with a cancer-associated mortality approximately the combined rates of cervical and uterine cancers [1]
The purpose of this study is to investigate the feasibility and accuracy of radiomics signature in the differentiating of Epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images for patients with OC
The results indicated that age and cancer antigen 125 (CA-125) levels were histological subtype-related factors for patients with OC
Summary
Ovarian cancer (OC) is the deadliest form of gynecological malignancy, which consists of approximately one fourth of all the gynecological cancers but with a cancer-associated mortality approximately the combined rates of cervical and uterine cancers [1]. The emerging of targeted therapy and identification of gene abnormalities in different histological subtypes open new perspectives for a personalized management for patients with OC [2, 3]. The differentiation of histological subtypes is critical for the assessment of the prognosis and treatment responses of cancer patients [4, 5]. There is a significant difference in the therapeutic schedule for EOC and NEOC treatment. Some subtypes of EOC such as clear cell and mucinous ovarian cancers which resistant to conventional platinum/taxane chemotherapy due to the differences in chemosensitivity [8,9,10]. An accurate identification of histological types in patients with OC in preoperative is important since it guides the personalized treatment and surveillance planning
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