Sklarew R J, Bodmer S C, Pertschuk L P
Department of Medicine, New York Medical College, Elmsford 10523.
Cytometry. 1990;11(3):359-78. doi: 10.1002/cyto.990110307.
"Receptogram Analysis" has been developed as a pattern-oriented approach for predicting endocrine response in breast cancer based upon quantification of the estrogen receptor immunocytochemical assay (ERICA), using a Quantimet Imaging System. Response prediction was evaluated in 58 stage III and IV patients receiving endocrine therapy (primarily Tamoxifen). The Receptogram is a composite of the univariate distributions of nuclear receptor content, IOD(S), and concentration (MOD), and their bivariate contour plot; where (S) is the calculated nuclear radius in section. MOD distributions were classified into four types based upon peak modality and kurtosis (I-IV), and contour plots were classified into four subtypes (A-D) based upon contour slope. Patients failing therapy were ERICA--or their receptogram revealed co-existent ER+ and ER- tumor cells (type II), highly skewed MOD distributions lacking defined peaks (type IV), or contours with nearly horizontal slope (type C). Response was realized in 9/16 type I patients, with a single positive MOD peak, and in 9/15 type III patients, with discrete, multimodal MOD peaks. In contrast, 0/8 type II, 0/12 type IV, and 0/10 type C patients were responders. Receptogram analysis was superior to cytosol assay (DCC) as a response discriminant: positive predictive value, 53% vs. 33%; negative predictive value, 100% vs. 75%; sensitivity, 100% vs. 83%; specificity, 68% vs. 23%; and accuracy, 78% vs. 41%, respectively. Alternately, patients were assigned to potentially responsive or non-responsive groups based upon thresholded mean receptor parameters: field MOD, mean nuclear MOD (NMOD), and mean NMOD(PF) where PF is the ER+ nuclear fraction. While these parameters correlated with DCC (r = .72, 0.69, and 0.69), they were only marginally better in predictive value.
“受体图分析”是一种基于模式的方法，通过使用定量图像分析系统对雌激素受体免疫细胞化学分析（ERICA）进行定量，来预测乳腺癌的内分泌反应。对58例接受内分泌治疗（主要是他莫昔芬）的III期和IV期患者进行了反应预测评估。受体图是核受体含量、IOD(S)和浓度（MOD）的单变量分布及其双变量等高线图的综合；其中(S)是切片中计算出的核半径。MOD分布根据峰值模态和峰度分为四种类型（I-IV），等高线图根据等高线斜率分为四种亚型（A-D）。治疗失败的患者为ERICA阴性，或其受体图显示存在ER+和ER-肿瘤细胞共存（II型）、高度偏态的MOD分布且无明确峰值（IV型）或等高线斜率近乎水平（C型）。16例I型患者中有9例出现反应，其MOD呈单一阳性峰值，15例III型患者中有9例出现反应，其MOD呈离散多峰。相比之下，8例II型、12例IV型和10例C型患者中均无反应者。作为反应判别方法，受体图分析优于胞浆分析（DCC）：阳性预测值分别为53%和33%；阴性预测值分别为100%和75%；敏感性分别为100%和83%；特异性分别为68%和23%；准确性分别为78%和41%。另外，根据阈值化的平均受体参数（视野MOD、平均核MOD（NMOD）和平均NMOD(PF)，其中PF是ER+核分数）将患者分为潜在反应组或无反应组。虽然这些参数与DCC相关（r = 0.72、0.69和0.69），但其预测价值仅略好一些。
