Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Fig. 1 | BMC Cancer

Fig. 1

From: A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer

Fig. 1

The workflow of whole study. The workflow consists of four main steps: Data collection and preprocessing, which involves the acquisition of CT images and clinical pathological data; Image segmentation and feature extraction, which includes the extraction of four types of features: shape features, first-order features, textural features, and wavelet features; Feature selection, which is performed using Pearson correlation analysis and the LASSO Cox regression algorithm; and Model construction and validation, which includes the development of a nomogram

TCGA-TCIA, The Cancer Genome Atlas and The Cancer Imaging Archive; EOC, epithelial ovarian cancer; CT, computed tomography; LASSO, least absolute shrinkage and selection operator; FIGO, International Federation of Gynecology and Obstetrics; Rad-score, radiomics score

Back to article page

BMC Cancer

ISSN: 1471-2407

Contact us