Name
Title | ||
|---|---|---|
Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach |
Abstract | ||
|---|---|---|
We introduce a novel radiohistomorphometric method for quantitative correlation and subsequent discovery of imaging markers for aggressive prostate cancer (CaP). While this approach can be employed in the context any imaging modality and disease domain, we seek to identify quantitative dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) attributes that are highly correlated with density and architecture of tumor microvessels, surrogate markers of CaP aggressiveness. This retrospective study consisted of five Gleason score matched patients who underwent 3 Tesla multiparametric MRI prior to radical prostatectomy (RP). The excised gland was sectioned and quartered with a rotary knife. For each serial section, digitized images of individual quadrants were reconstructed into pseudo whole mount sections via previously developed stitching program. The individual quadrants were stained with vascular marker CD31 and annotated for CaP by an expert pathologist. The stained microvessel regions were quantitatively characterized in terms of density and architectural arrangement via graph algorithms, yielding a series of quantitative histomorphometric features. The reconstructed pseudo whole mount histologic sections were non-linearly co-registered with DCE MRI to identify tumor extent on MRI on a voxel-by-voxel basis. Pairwise correlations between kinetic and microvessel features within CaP annotated regions on the two modalities were computed to identify highly correlated attributes. Preliminary results of the radiohistomorphometric correlation identified 8 DCE MRI kinetic features that were highly and significantly (p<0.05) correlated with a number of microvessel parameters. Most of the identified imaging features were related to rate of washout (Rwo) and initial area under the curve (IAUC). Association of those attributes with Gleason patterns showed that the identified imaging features clustered most of the tumors with primary Gleason pattern of 3 together. These results suggest that Rwo and IAUC may be promising candidate imaging markers for identification of aggressive CaP in vivo. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2008136 | Proceedings of SPIE |
Keywords | Field | DocType |
algorithms,magnetic resonance imaging | Nuclear medicine,Ex vivo,Computer vision,Graph algorithms,Primary Gleason Pattern,Microvessel,In vivo,Multiparametric MRI,Artificial intelligence,Prostate cancer,Magnetic resonance imaging,Physics | Conference |
Volume | ISSN | Citations |
8676 | 0277-786X | 1 |
PageRank | References | Authors |
0.38 | 7 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asha Singanamalli | 1 | 18 | 1.78 |
| Rachel Sparks | 2 | 35 | 6.76 |
| Mirabela Rusu | 3 | 8 | 9.48 |
| Natalie Shih | 4 | 105 | 7.17 |
| amy ziober | 5 | 1 | 0.38 |
| John E. Tomaszewski | 6 | 198 | 18.60 |
| Mark Rosen | 7 | 22 | 4.24 |
| Michael Feldman | 8 | 204 | 14.87 |
| Anant Madabhushi | 9 | 1736 | 139.21 |