Classification of 1H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis

✍ Scribed by J. C. Wallace; G. P. Raaphorst; R. L. Somorjai; C. E. Ng; M. Kee Fung Fung; M. Senterman; Ianc. P. Smith

• Book ID: 102957447
• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 829 KB
• Volume: 38
• Category: Article
• ISSN: 0740-3194
• DOI: 10.1002/mrm.1910380411

✦ Synopsis

Abstract

Proton (^1^H) magnetic resonance (MR) spectra of ex vivo biopsy samples of ovarian cancers provided biochemical information that was used to discriminate cancer from normal ovarian tissue. Possible differences present in intrinsically resistant tumors or changes in biochemistry after the induction of resistance were identified. Using multivariate techniques, in particular linear discriminant analysis (LDA), ovarian cancer was distinguished from normal ovarian tissue with a sensitivity of 100%, a specificity of 95% and an accuracy of 98%. Moreover, LDA was able to distinguish untreated ovarian cancer from recurrent ovarian cancer with a sensitivity of 92%, a specificity of 100%, and an accuracy of 97%; removal of the single “fuzzy” specimen increased the accuracy to 100%. Applications of this knowledge to in vivo measurements could lead to noninvasive diagnosis of ovarian cancer.