Quantitative alterations in nuclear structure predict prostate carcinoma distant metastasis and death in men with biochemical recurrence after radical prostatectomy
Quantitative alterations in nuclear structure predict prostate carcinoma distant metastasis and death in men with biochemical recurrence after radical prostatectomy
复制标题
DOI：
10.1002/cncr.11852
复制
发表时间：
2003-12-15
期刊：
影响因子：
6.2
通讯作者：
Veltri, RW
中科院分区：
文献类型：
--
作者：
Khan, MA;Walsh, PC;Veltri, RW
背景显微镜下组织学分级是男性前列腺癌（PCa）手术治疗后进展的最佳预测因子。在手术时准确预测哪些患者可能发展为转移性PCa的能力将使辅助治疗的疾病管理得以优化。作者评估了病理学、核形态学和染色质参数预测227例接受根治性前列腺切除术后生化复发和长期随访的男性转移性前列腺癌进展和/或死亡的能力。使用多变量逻辑回归（LR）计算定量核分级（QNG）解决方案，使用60个核大小、形状、DNA含量和染色质组织的核形态描述符（NMD）的方差，预测远处转移和/或PCA特异性死亡。还生成LR模型，以使用病理变量和最佳QNG解决方案的组合来预测该结果。生成考克斯比例风险模型，并使用Kaplan-Meier图显示基于病理学、QNG和这些变量组合的三个风险组。采用病理学保留淋巴结（LN）状态、精囊状态和前列腺切除术Gleason评分的多变量LR模型，在90%灵敏度下，曲线下面积-受试者操作特征（AUC-ROC）为75%，准确度为59%。最佳QNG解决方案使用25个NMD的方差，在90%灵敏度下产生84%的AUC-ROC和70%的准确度。联合病理学-QNG模型保留了LN状态、淋巴结切除术Gleason评分和QNG，在90%灵敏度下，AUC-ROC为86%，准确度为76%。考克斯比例风险模型产生了以下显著的单变量和多变量风险比：QNG，分别为3.5和2.9; LN，分别为2.7和1.8;以及直肠切除术Gleason评分，分别为2.8和2.1。计算机辅助图像分析测量的肿瘤细胞核结构的变化是前列腺癌进展和死亡的强有力的预测因素，长期随访的男性在接受根治性前列腺切除术后生化复发。QNG解决方案可以作为一种新的补充生物标志物，用于在手术时准确预测PCa进展。(C)2003年美国癌症协会。
BACKGROUND. Microscopic histologic grade has been the best predictor of prostate carcinoma (PCa) progression in men after surgical therapy. The ability to predict accurately, at the time of surgery, which patients are likely to develop metastatic PCa would enable optimization of disease management with adjuvant therapy. The authors assessed the ability of pathologic, nuclear morphometric, and chromatin parameters to predict metastatic PCa progression and/or death in 227 men with biochemical recurrence and long-term follow-up after undergoing radical prostatectomy.METHODS. Multivariate logistic regression (LR) was used to calculate quantitative nuclear grade (QNG) solutions using the variances of 60 nuclear morphometric descriptors (NMDs) of nuclear size, shape, DNA content, and chromatin organization that predicted distant metastasis and/or PCa-specific death. An LR model also was generated to predict this outcome using a combination of pathologic variables and the best QNG solution. Cox proportional hazards models were generated, and Kaplan-Meier plots were used to display three risk groups based on pathology, QNG, and a combination of these variables.RESULTS. A multivariate LR model using pathology retained lymph node (LN) status, seminal vesicle status, and prostatectomy Gleason score, yielding an area under the curve-receiver operator characteristic (AUC-ROC) of 75% with an accuracy of 59% at 90% sensitivity. The best QNG solution used the variance of 25 NMDs, yielding an AUC-ROC of 84% and an accuracy of 70% at 90% sensitivity. The combined pathology-QNG model retained LN status, prostatectomy Gleason score, and QNG, yielding an AUC-ROC of 86% with an accuracy of 76% at 90% sensitivity. The Cox proportional hazards models produced the following significant univariate and multivariate hazard ratios: QNG, 3.5 and 2.9, respectively; LN, 2.7 and 1.8, respectively; and prostatectomy Gleason score, 2.8 and 2.1, respectively.CONCLUSIONS. Alterations in the structure of tumor nuclei measured by computer-assisted image analysis were strong predictors of PCa progression and death in men with long-term follow-up who had biochemical recurrence after undergoing radical prostatectomy. QNG solutions can serve as a new supplemental biomarker for accurate prediction of PCa progression at the time of surgery. (C) 2003 American Cancer Society.