Prediction of prostate-specific antigen recurrence in men with long-term follow-up postprostatectomy using quantitative nuclear morphometry.
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.
Veltri
Robert W
RW
James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA. rveltri1@jhmi.edu
Miller
M Craig
MC
Isharwal
Sumit
S
Marlow
Cameron
C
Makarov
Danil V
DV
Partin
Alan W
AW
D000230:Adenocarcinoma / Q000097:blood / Q000473:pathology* / Q000601:surgery; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000097:blood; D002467:Cell Nucleus / Q000473:pathology; D015331:Cohort Studies; D004273:DNA, Neoplasm / Q000032:analysis*; D018572:Disease-Free Survival; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis*; D017430:Prostate-Specific Antigen / Q000097:blood*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood / Q000473:pathology* / Q000601:surgery; D012372:ROC Curve; D012680:Sensitivity and Specificity; D015996:Survival Rate
D014408:Biomarkers, Tumor; D004273:DNA, Neoplasm; D017430:Prostate-Specific Antigen