Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms.

ABSTRACT:

Objective

This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms.

Methods

This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to det

SUBMITTER: He Z 

PROVIDER: S-EPMC9893092 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature