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.
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: Carlos E de Andrea; Antonio Sergio Petrilli; Reynaldo Jesus-Garcia; Luiz F Bleggi-Torres; Maria Teresa S Alves Journal: Int J Clin Exp Pathol Date: 2011-01-30
Authors: Sumit Isharwal; Michael C Miller; Cameron Marlow; Danil V Makarov; Alan W Partin; Robert W Veltri Journal: Prostate Date: 2008-07-01 Impact factor: 4.104
Authors: Robert W Veltri; Sumit Isharwal; M Craig Miller; Jonathan I Epstein; Leslie A Mangold; Elizabeth Humphreys; Alan W Partin Journal: Prostate Date: 2008-12-01 Impact factor: 4.104
Authors: Madeleine S Q Kortenhorst; Sumit Isharwal; Paul J van Diest; Wasim H Chowdhury; Cameron Marlow; Michael A Carducci; Ronald Rodriguez; Robert W Veltri Journal: Mol Cancer Ther Date: 2009-04 Impact factor: 6.261
Authors: Sumit Isharwal; M Craig Miller; Jonathan I Epstein; Leslie A Mangold; Elizabeth Humphreys; Alan W Partin; Robert W Veltri Journal: Urology Date: 2009-02-03 Impact factor: 2.649