Veltri Robert W, Marlow Cameron, Khan Masood A, Miller Michael C, Epstein Jonathan I, Partin Alan W
Department of Urology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Prostate. 2007 Aug 1;67(11):1202-10. doi: 10.1002/pros.20614.
Alterations in nuclei structure and DNA content captured from Gleason grading patterns 3, 4 and 5 of radical prostatectomy (RP) cases were determined by a computer-assisted microscope. Quantitative Nuclear Morphometry (QNM) profiles were created to evaluate variability in nuclear structure within each of these grades.
A tissue microarray (TMA) was constructed using RP cases and the prostate cancer (PCa) TMA cores prepared from 20 GG-3, 9 GG-4, 10 GG-5 patterns, and 20 benign cancer-adjacent cases from RP archival paraffin blocks. Feulgen-stained nuclei were captured from 0.6 mm spots using the AutoCyte system. Pools of 1100 nuclei captured from each test group were used to calculate Multivariate Logistic Regression (MLR) models that generated predictive indices and predictive probabilities (PP) to make comparisons between and within each set of pooled nuclei.
A single QNM profiles yielded areas of receiver operator characteristic curves (ROC) that distinguished differences among benign cancer-adjacent nuclei and GG-3 (ROC-AUC = 0.78); GG-4 (ROC-AUC = 0.86) and GG-5 (ROC-AUC = 0.88) with accuracies of 73%, 78% and 80% respectively. Applying PP plots generated from MLR models of GG 3, 4, and 5 nuclei clearly demonstrated marked heterogeneity within each of these three GG patterns.
QNM signatures illustrate alterations in nuclei structure, based upon nuclear morphometry within each of these three GG patterns, and might signify potential variations in PCa disease risk of progression outcomes. In the future a modified system of Gleason grading that considers not only glandular architecture but also quantitative nuclear grade may ensure accuracy in prognosis.
通过计算机辅助显微镜确定从根治性前列腺切除术（RP）病例的Gleason分级模式3、4和5中捕获的细胞核结构和DNA含量的改变。创建定量核形态计量学（QNM）图谱以评估这些分级中每个分级内细胞核结构的变异性。
使用RP病例构建组织微阵列（TMA），并从RP存档石蜡块中制备前列腺癌（PCa）TMA芯，其中包括20个Gleason分级3（GG-3）、9个GG-4、10个GG-5模式以及20个癌旁良性病例。使用AutoCyte系统从0.6毫米的斑点中捕获福尔根染色的细胞核。从每个测试组捕获的1100个细胞核池用于计算多变量逻辑回归（MLR）模型，该模型生成预测指数和预测概率（PP），以在每组合并的细胞核之间和内部进行比较。
单个QNM图谱产生了区分癌旁良性细胞核与GG-3（ROC曲线下面积[AUC]=0.78）、GG-4（ROC-AUC=0.86）和GG-5（ROC-AUC=0.88）之间差异的受试者操作特征曲线（ROC）区域，准确率分别为73%、78%和80%。应用从GG 3、4和5细胞核的MLR模型生成的PP图清楚地显示了这三种GG模式中每一种模式内的明显异质性。
QNM特征说明了基于这三种GG模式中每一种模式内的核形态计量学的细胞核结构改变，并且可能表明PCa疾病进展结果风险的潜在差异。未来，一种不仅考虑腺泡结构而且考虑定量核分级的改良Gleason分级系统可能确保预后的准确性。
