利用定量核形态测定法对前列腺切除术后长期随访男性的前列腺特异性抗原复发情况进行预测。

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

作者信息

Veltri Robert W, Miller M Craig, Isharwal Sumit, Marlow Cameron, Makarov Danil V, Partin Alan W

机构信息

James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

出版信息

Cancer Epidemiol Biomarkers Prev. 2008 Jan;17(1):102-10. doi: 10.1158/1055-9965.EPI-07-0175.

DOI:10.1158/1055-9965.EPI-07-0175

PMID:18199716

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.

摘要

背景

可使用细胞核大小、形状、DNA含量和染色质纹理描述符[细胞核形态计量描述符（NMD）]来计算细胞核形态计量特征。我们评估了单独使用患者特异性定量核分级（QNG）以及将其与常规病理特征相结合来预测前列腺癌患者生化[前列腺特异性抗原（PSA）]无复发生存率的情况。

方法

从1991年至1992年接受根治性前列腺切除术的病例中制备了美国国立癌症研究所合作前列腺癌组织资源（NCI-CPCTR）组织微阵列。我们评估了112例进行长期随访的病例（72例未复发和40例PSA复发）。采集了福尔根DNA染色细胞核的图像，并使用AutoCyte系统计算NMD。采用多变量逻辑回归来计算用于预测PSA复发的QNG和基于病理的解决方案。生成了Kaplan-Meier生存曲线和预测概率图。

结果

使用14个NMD的方差得出的QNG特征在预测概率阈值≥0.39时，受试者操作特征曲线下面积（AUC-ROC）为80%，灵敏度、特异性和准确性为75%。使用病理分期和Gleason评分的病理模型在预测概率阈值≥0.35时，AUC-ROC为67%，灵敏度、特异性和准确性分别为70%、50%和57%。将QNG、病理分期和Gleason评分相结合得出的模型在预测概率阈值≥0.34时，AUC-ROC为81%，灵敏度、特异性和准确性分别为75%、78%和77%。