De Laurentiis M, Gallo C, De Placido S, Perrone F, Pettinato G, Petrella G, Carlomagno C, Panico L, Delrio P, Bianco A R
Dipartimento di Endocrinologia ed Oncologia Molecolare e Clinica, Facoltà di Medicina, Università Feferico II, Napoli, Italy.
Br J Cancer. 1996 May;73(10):1241-7. doi: 10.1038/bjc.1996.238.
We investigated the association between pathological characteristics of primary breast cancer and degree of axillary nodal involvement and obtained a predictive index of the latter from the former. In 2076 cases, 17 histological features, including primary tumour and local invasion variables, were recorded. The whole sample was randomly split in a training (75% of cases) and a test sample. Simple and multiple correspondence analysis were used to select the variables to enter in a multinomial logit model to build an index predictive of the degree of nodal involvement. The response variable was axillary nodal status coded in four classes (N0, N1-3, N4-9, N > or = 10). The predictive index was then evaluated by testing goodness-of-fit and classification accuracy. Covariates significantly associated with nodal status were tumour size (P < 0.0001), tumour type (P < 0.0001), type of border (P = 0.048), multicentricity (P = 0.003), invasion of lymphatic and blood vessels (P < 0.0001) and nipple invasion (P = 0.006). Goodness-of-fit was validated by high concordance between observed and expected number of cases in each decile of predicted probability in both training and test samples. Classification accuracy analysis showed that true node-positive cases were well recognised (84.5%), but there was no clear distinction among the classes of node-positive cases. However, 10 year survival analysis showed a superimposible prognostic behaviour between predicted and observed nodal classes. Moreover, misclassified node-negative patients (i.e. those who are predicted positive) showed an outcome closer to patients with 1-3 metastatic nodes than to node-negative ones. In conclusion, the index cannot completely substitute for axillary node information, but it is a predictor of prognosis as accurate as nodal involvement and identifies a subgroup of node-negative patients with unfavourable prognosis.
我们研究了原发性乳腺癌的病理特征与腋窝淋巴结受累程度之间的关联,并从前者获得了后者的预测指标。在2076例病例中,记录了17种组织学特征,包括原发性肿瘤和局部浸润变量。整个样本被随机分为训练样本(75%的病例)和测试样本。使用简单和多重对应分析来选择进入多项logit模型的变量,以建立一个预测淋巴结受累程度的指标。反应变量是腋窝淋巴结状态,分为四类编码(N0、N1 - 3、N4 - 9、N≥10)。然后通过检验拟合优度和分类准确性来评估预测指标。与淋巴结状态显著相关的协变量有肿瘤大小(P < 0.0001)、肿瘤类型(P < 0.0001)、边界类型(P = 0.048)、多中心性(P = 0.003)、淋巴管和血管侵犯(P < 0.0001)以及乳头侵犯(P = 0.006)。通过训练样本和测试样本中预测概率的每个十分位数的观察病例数与预期病例数之间的高度一致性验证了拟合优度。分类准确性分析表明,真正的淋巴结阳性病例得到了很好的识别(84.5%),但在淋巴结阳性病例的类别之间没有明显区分。然而,10年生存分析表明,预测的和观察到的淋巴结类别之间的预后行为重叠。此外,误分类为淋巴结阴性的患者(即预测为阳性的患者)的结局更接近有1 - 3个转移淋巴结的患者,而不是淋巴结阴性的患者。总之,该指标不能完全替代腋窝淋巴结信息,但它是一个与淋巴结受累程度一样准确的预后预测指标,并识别出一组预后不良的淋巴结阴性患者。