Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. Issue 3 (May 2022)

• Record Type: Journal Article
• Authors: Li, Beibei; Sun, Mingli; Yao, Peng; Chang, Zhihui; Liu, Zhaoyu
• Is Part Of: Journal of computer assisted tomography. Volume 46:Issue 3(2022)
• Page Start: 371
• Page End: 378
• Publication Date: 2022-05
• Subjects: genomics -- imaging -- machine learning -- ovarian cancer -- radiogenomics; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001279
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract: A new interdisciplinary approach based on medical imaging phenotypes, gene expression patterns, and clinical parameters, referred to as radiogenomics, has recently been developed for biomarker identification and clinical risk stratification in oncology, including for the assessment of ovarian cancer. Some radiological phenotypes (implant distribution, lymphadenopathy, and texture-derived features) are related to specific genetic landscapes ( BRCA, BRAF, SULF1, the Classification of Ovarian Cancer), and integrated models can improve the efficiency for predicting clinical outcomes. The establishment of databases in medical images and gene expression profile with large sample size and the improvement of artificial intelligence algorithm will further promote the application of radiogenomics in ovarian cancer.