Journal of Oncology / 2021 / Article / Tab 4 / Review Article
Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms Table 4 Different clinical applications of radiomics in ovarian tumors.
References Case numbers Radiomic method Results [130 ] 187 US US-based radiomics could be efficiently used for developing the classification stage in ovarian tumor [131 ] 59 CT CT features of serous borderline tumors were distinct from low-grade serous carcinomas [132 ] Not mentioned OCT 3D texture analysis of OCT was useful for quantitatively characterizing ovarian tissue [133 ] 38 OCT OCT-based radiomics had the potential to classify different subtypes of ovarian tissue [134 ] 10 SHG SHG texture analysis had the potential for ovarian cancer classification [135 ] 10 SHG 3D SHG texture analysis achieved high accuracy for classifying high-grade cancer tissue and normal ovarian tissue [136 ] 8 SHG Metastatic tissue images features were distinct from that of healthy tissues [137 ] 91 CT CT-based radiomics had the potential to predict responses of ovarian cancer patients to chemotherapy [138 ] 120 CT CT-based radiomic features computed from both spatial and frequency domains had a reliable prediction ability of tumor response to postsurgical chemotherapy [139 ] 364 CT Radiomic prognostic vector (RPV) could be exploited to personalized therapy of epithelial ovarian cancer (EOC) and had the potential to apply in other cancer types [140 ] 38 CT Quantitative metrics noninvasively capturing spatial intersite heterogeneity may predict outcomes in patients with HGSOC [141 ] 142 CT Radiomic signature was potential prognostic markers that may allow for individualized evaluation of patients with advanced HGSOC