Steyerberg E W, Keizer H J, Fosså S D, Sleijfer D T, Toner G C, Schraffordt Koops H, Mulders P F, Messemer J E, Ney K, Donohue J P
Department of Public Health, Erasmus University, Rotterdam, The Netherlands.
J Clin Oncol. 1995 May;13(5):1177-87. doi: 10.1200/JCO.1995.13.5.1177.
To develop a statistical model that predicts the histology (necrosis, mature teratoma, or cancer) after chemotherapy for metastatic nonseminomatous germ cell tumor (NSGCT).
An international data set was collected comprising individual patient data from six study groups. Logistic regression analysis was used to estimate the probability of necrosis and the ratio of cancer and mature teratoma.
Of 556 patients, 250 (45%) had necrosis at resection, 236 (42%) had mature teratoma, and 70 (13%) had cancer. Predictors of necrosis were the absence of teratoma elements in the primary tumor, prechemotherapy normal alfa-fetoprotein (AFP), normal human chorionic gonadotropin (HCG), and elevated lactate dehydrogenase (LDH) levels, a small prechemotherapy or postchemotherapy mass, and a large shrinkage of the mass during chemotherapy. Multivariate combination of predictors yielded reliable models (goodness-of-fit tests, P > .20), which discriminated necrosis well from other histologies (area under the receiver operating characteristic (ROC) curve, .84), but which discriminated cancer only reasonably from mature teratoma (area, .66). Internal and external validation confirmed these findings.
The validated models estimate with high accuracy the histology at resection, especially necrosis, based on well-known and readily available predictors. The predicted probabilities may help to choose between immediate resection of a residual mass or follow-up, taking into account the expected benefits and risks of resection, feasibility of frequent follow-up, the financial costs, and the patient's individual preferences.
建立一个统计模型,用于预测转移性非精原细胞瘤性生殖细胞肿瘤(NSGCT)化疗后的组织学类型(坏死、成熟畸胎瘤或癌)。
收集了一个国际数据集,其中包含来自六个研究组的个体患者数据。采用逻辑回归分析来估计坏死的概率以及癌与成熟畸胎瘤的比例。
556例患者中,250例(45%)在切除时出现坏死,236例(42%)有成熟畸胎瘤,70例(13%)有癌。坏死的预测因素包括原发肿瘤中无畸胎瘤成分、化疗前甲胎蛋白(AFP)正常、人绒毛膜促性腺激素(HCG)正常、乳酸脱氢酶(LDH)水平升高、化疗前或化疗后肿块较小以及化疗期间肿块大幅缩小。预测因素的多变量组合产生了可靠的模型(拟合优度检验,P>.20),该模型能很好地将坏死与其他组织学类型区分开来(受试者操作特征曲线下面积,.84),但仅能合理地区分癌与成熟畸胎瘤(面积,.66)。内部和外部验证证实了这些发现。
基于众所周知且易于获得的预测因素,经过验证的模型能够高精度地估计切除时的组织学类型,尤其是坏死情况。预测概率有助于在考虑切除的预期益处和风险、频繁随访的可行性、经济成本以及患者个人偏好的基础上,选择对残留肿块立即进行切除还是进行随访。