Add like
Add dislike
Add to saved papers

Discrimination of malignant transformation from benign endometriosis using a near-infrared approach.

The aim of the present single-center retrospective study was to investigate the discrimination of malignant transformation from ovarian endometrioma (OE) using a near-infrared approach ex vivo . Cystic fluid samples were collected from patients with OE (n=34) and endometriosis-associated ovarian cancer (EAOC) (n=12). The light reflected from each sample of cystic fluid [change in luminance, Δl (cd/m2 ) = background luminance-cystic fluid luminance at 800 nm] was spectrally measured by a near-infrared CCD camera with band-path filter (800 nm). The Δl in EAOC was significantly lower compared with that in OE. On regression analysis, a positive correlation was observed between the Δl and Hb level in the cystic fluid, and this association was exponential. The diagnostic sensitivity and specificity of Δl was 83.3 and 94.1% at the cutoff value of 21.5 cd/m2 , with an area under the ROC curve of 0.897. The present ex vivo study potentially provides a powerful near-infrared approach for quantitative discrimination between EAOC and benign OE, with high sensitivity and specificity, which may have clinical applications.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app