Name
Title | ||
|---|---|---|
Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients |
Abstract | ||
|---|---|---|
Renal cell carcinoma (RCC) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different RCC patients. The application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85990-1_1 | MICCAI (2) |
Keywords | Field | DocType |
image analysis,random forest,tissue microarray | Cancer cell,Computer science,Renal clear cell carcinoma,Renal cell carcinoma,Tissue microarray,Cell,Staining,Immunohistochemistry,Pathology | Conference |
Volume | Issue | ISSN |
11 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
10 | 0.81 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas J. Fuchs | 1 | 343 | 22.48 |
| Peter J. Wild | 2 | 31 | 4.26 |
| H Moch | 3 | 131 | 15.90 |
| joachim m buhmann | 4 | 4363 | 730.34 |