Name
Abstract | ||
|---|---|---|
For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an adjacent slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an adjacent slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an adjacent slice. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.844494 | Proceedings of SPIE |
Keywords | Field | DocType |
Prostate cancer,Biomarker,Imaging biomarker,MALDI | Early detection,Early Cancer Detection,Imaging biomarker,Biomarker (medicine),Prostate cancer,Artificial intelligence,MALDI imaging,Pathology,Computer vision,Prostate,Bioinformatics,Cancer,Physics | Conference |
Volume | ISSN | Citations |
7624 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayyappa Vadlamudi | 1 | 2 | 1.12 |
| Shao-Hui Chuang | 2 | 6 | 2.28 |
| Xiaoyan Sun | 3 | 15 | 4.26 |
| Lisa Cazares | 4 | 20 | 2.36 |
| julius o nyalwidhe | 5 | 1 | 0.75 |
| Dean Troyer | 6 | 3 | 1.53 |
| John O. Semmes | 7 | 55 | 5.05 |
| jiang li | 8 | 23 | 9.88 |
| Frederic D Mckenzie | 9 | 75 | 18.51 |