Name
Abstract | ||
|---|---|---|
Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.844403 | Proceedings of SPIE |
Keywords | Field | DocType |
Prostate Cancer,Texture Analysis,MLP Classification,Histopathology | Histopathology,Biopsy,Biomarker Analysis,Biomarker (medicine),Prostate cancer,Artificial intelligence,MALDI imaging,Pathology,Computer vision,Prostate,Bioinformatics,Cancer,Physics | Conference |
Volume | ISSN | Citations |
7624 | 0277-786X | 1 |
PageRank | References | Authors |
0.41 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shao-Hui Chuang | 1 | 6 | 2.28 |
| Xiaoyan Sun | 2 | 15 | 4.26 |
| Lisa Cazares | 3 | 20 | 2.36 |
| julius o nyalwidhe | 4 | 1 | 0.75 |
| Dean Troyer | 5 | 3 | 1.53 |
| John O. Semmes | 6 | 55 | 5.05 |
| jiang li | 7 | 23 | 9.88 |
| Frederic D Mckenzie | 8 | 75 | 18.51 |