Prediction of prostate-specific antigen recurrence in men with long-term follow-up postprostatectomy using quantitative nuclear morphometry.

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 > or =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 > or =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 > or =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.

• Authors: Robert W Veltri, M Craig Miller, Sumit Isharwal, Cameron Marlow, Danil V Makarov, Alan W Partin
• Journal: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology (Cancer Epidemiol Biomarkers Prev) Vol. 17 Issue 1 Pg. 102-10 (Jan 2008) ISSN: 1055-9965 [Print] United States
• PMID: 18199716 (Publication Type: Journal Article)

Chemical References

• Biomarkers, Tumor
• DNA, Neoplasm
• Prostate-Specific Antigen

Topics

• Adenocarcinoma (blood, pathology, surgery)
• Adult
• Aged
• Area Under Curve
• Biomarkers, Tumor (blood)
• Cell Nucleus (pathology)
• Cohort Studies
• DNA, Neoplasm (analysis)
• Disease-Free Survival
• Follow-Up Studies
• Humans
• Image Processing, Computer-Assisted
• Male
• Middle Aged
• Neoplasm Recurrence, Local (diagnosis)
• Prostate-Specific Antigen (blood)
• Prostatectomy
• Prostatic Neoplasms (blood, pathology, surgery)
• ROC Curve
• Sensitivity and Specificity
• Survival Rate