CT texture analysis in histological classification of epithelial ovarian carcinoma

Author(s):  
He An ◽  
Yiang Wang ◽  
Esther M. F. Wong ◽  
Shanshan Lyu ◽  
Lujun Han ◽  
...  
Imaging ◽  
2021 ◽  
Author(s):  
Veronica Frank ◽  
Sonaz Shariati ◽  
Bettina Katalin Budai ◽  
Bence Fejér ◽  
Ambrus Tóth ◽  
...  

ABSTRACTIt has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.


2008 ◽  
Vol 68 (S 01) ◽  
Author(s):  
S Mahner ◽  
C Baasch ◽  
J Schwarz ◽  
L Wölber ◽  
F Jänicke ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.


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