Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery

Abstract

Objective To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. Methods A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. Results The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. Conclusion The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma

“…If we can associate the patient’s internal pathways and prognosis with the different texture characteristics of the tumor, it will be useful for the diagnosis and treatment of the patient in the future. In our study, the first-order statistics features such as Skewness, Minimum, Median, RobustMeanAbsoluteDeviation and RootMeanSquared appeared in a high proportion of the final included features, which describe the intensity values of the tumor and are applied to many classification tasks ( 29 , 34 ). Therefore, radiomics features extracted from ultrasound image of BC could be a potential auxiliary method for clinicians to identify the Ki-67 status.…”

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

“…Due to the difference in treatment and prognosis between type I and type II EOC, it is necessary to classify the pathological type after a diagnosis of OC ( 5 ). In this regard, Tang et al ( 62 ) investigated ultrasonic images of patients with EOC (n=154), and divided them into type I and type II EOC according to the pathology. The seven features with the greatest differences were screened out using LASSO regression ten-fold cross-validation.…”