Prediction of Prostate-Specific Antigen Recurrence in Men with Long-term Follow-up Postprostatectomy Using Quantitative Nuclear Morphometry
- Robert W. Veltri1,
- M. Craig Miller2,
- Sumit Isharwal1,
- Cameron Marlow1,
- Danil V. Makarov1 and
- Alan W. Partin1
- 1The James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland and 2Quakertown, Pennsylvania
- Requests for reprints:
Robert W. Veltri, James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287. Phone: 410-614-6380; Fax: 410-614-3695. E-mail: rveltri1{at}jhmi.edu
Abstract
Background: Nuclear morphometric signatures can be calculated using nuclear size, shape, DNA content, and chromatin texture descriptors [nuclear morphometric descriptor (NMD)]. We evaluated the use of a patient-specific quantitative nuclear grade (QNG) alone and in combination with routine pathologic features to predict biochemical [prostate-specific antigen (PSA)] recurrence-free survival in patients with prostate cancer.
Methods: The National Cancer Institute Cooperative Prostate Cancer Tissue Resource (NCI-CPCTR) tissue microarray was prepared from radical prostatectomy cases treated in 1991 to 1992. We assessed 112 cases (72 nonrecurrences and 40 PSA recurrences) with long-term follow-up. Images of Feulgen DNA–stained nuclei were captured and the NMDs were calculated using the AutoCyte system. Multivariate logistic regression was used to calculate QNG and pathology-based solutions for prediction of PSA recurrence. Kaplan-Meier survival curves and predictive probability graphs were generated.
Results: A QNG signature using the variance of 14 NMDs yielded an area under the receiver operator characteristic curve (AUC-ROC) of 80% with a sensitivity, specificity, and accuracy of 75% at a predictive probability threshold of ≥0.39. A pathology model using the pathologic stage and Gleason score yielded an AUC-ROC of 67% with a sensitivity, specificity, and accuracy of 70%, 50%, and 57%, respectively, at a predictive probability threshold of ≥0.35. Combining QNG, pathologic stage, and Gleason score yielded a model with an AUC-ROC of 81% with a sensitivity, specificity, and accuracy of 75%, 78%, and 77%, respectively, at a predictive probability threshold of ≥0.34.
Conclusions: PSA recurrence is more accurately predicted using the QNG signature compared with routine pathology information alone. Inclusion of a morphometry signature, routine pathology, and new biomarkers should improve the prognostic value of information collected at surgery. (Cancer Epidemiol Biomarkers Prev 2008;17(1):102–10)
- quantitative nuclear morphometry
- nuclear structure
- biochemical recurrence
- prostate cancer progression
- Feulgen DNA stain
- Genitourinary cancers: prostate
- Risk assessment
- Tumor Progression, Invasion, and Metastasis
- Chromatin structure/higher order regulation, acetylation
- Biomarkers and intervention
Footnotes
-
↵4 ask-cpctr-l{at}nci.nih.gov
-
Grant support: The Johns Hopkins University Prostate Cancer Specialized Programs of Research Excellence grant P50CA58236, Early Detection Research Network NCI/NIH grant CA08623-06, Prostate Cancer Foundation, and the Patana Fund.
-
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
-
- Accepted October 31, 2007.
- Received February 23, 2007.
- Revision received October 3, 2007.
