Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. - Zhang, He; Mao, Yunfei; Chen, Xiaojun; Wu, Guoqing; Liu, Xuefen; Zhang, Peng; Bai, Yu; Lu, Pengcong; Yao, Weigen; Wang, Yuanyuan; Yu, Jinhua; Zhang, Guofu (European Radiology, 2019)
Purpose: To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients.Method: A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis.Result: For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001).Conclusion: Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy.Key Points: • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis.