Cancer Management and Research (2021-01-01)

MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

  • Yu X,
  • Wang L,
  • Yu H,
  • Zou Y,
  • Wang C,
  • Jiao J,
  • Hong H,
  • Zhang S

Journal volume & issue
Vol. Volume 13
pp. 329 – 336

Abstract

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Xin-ping Yu,1 Lei Wang,1 Hai-yang Yu,2 Yu-wei Zou,3 Chang Wang,1 Jin-wen Jiao,1 Hao Hong,4 Shuai Zhang2 1Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China; 2Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China; 3Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China; 4Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, People’s Republic of ChinaCorrespondence: Shuai ZhangDepartment of Radiology, The Affiliated Hospital of Qingdao University, NO. 1677 Wutaishan Road, Huangdao District, Shandong Province 266000, People’s Republic of ChinaTel +86 18661804638Email [email protected]: To investigate whether multidetector computed tomography (MDCT)-based radiomics features can discriminate between serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).Patients and Methods: Eighty patients with SBOTs and 102 patients with SMOTs, confirmed by pathology (training set: n = 127; validation set: n = 55) from December 2017 to June 2020, were enrolled in this study. The interclass correlation coefficient (ICC) and least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics parameters derived from MDCT images on the arterial phase (AP), venous phase (VP), and equilibrium phase (EP). Receiver operating characteristic (ROC) analysis of each selected parameter was carried out. Heat maps were created to illustrate the distribution of the radiomics parameters. Three models incorporating selected radiomics parameters generated by support vector machine (SVM) classifiers in each phase were analyzed by ROC and compared using the DeLong test.Results: The most predictive features selected by ICC and LASSO regression between SBOTs and SMOTs included 9 radiomics parameters on AP, VP, and EP each. Three models on AP, VP, and EP incorporating the selected features generated by SVM classifiers produced AUCs of 0.80 (accuracy, 0.75; sensitivity, 0.74; specificity, 0.75), 0.86 (accuracy, 0.78; sensitivity, 0.80; specificity, 0.75), and 0.73 (accuracy, 0.69; sensitivity, 0.71; specificity, 0.67), respectively. There were no significant differences in the AUCs among the three models (AP vs. VP, P = 0.199; AP vs. EP, P = 0.260; VP vs. EP, P = 0.793).Conclusion: MDCT-based radiomics features could be used as biomarkers for the differentiation of SBOTs and SMOTs.Keywords: ovarian tumors, multidetector computed tomography; MDCT, radiomics

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