Sentinel Node Status Prediction by Four Statistical Models Results From a Large Bi-Institutional Series (n=1132)
Sentinel Node Status Prediction by Four Statistical Models Results From a Large Bi-Institutional Series (n=1132)
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DOI：
10.1097/sla.0b013e3181b07ffd
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发表时间：
2009-12-01
影响因子：
9
通讯作者：
Rossi, Carlo R.
中科院分区：
文献类型：
--
作者：
Mocellin, Simone;Thompson, John F.;Rossi, Carlo R.
目的：利用预测皮肤黑色素瘤前哨淋巴结（SN）状态的统计模型，提高对皮肤黑色素瘤患者前哨淋巴结活检（SNB）的选择。背景数据：目前接受SNB的患者中约80%为淋巴结阴性。在没有确凿证据表明snb相关的生存获益的情况下，这些患者可能被过度治疗。在这里，我们测试了4种不同模型在预测SN状态方面的效率。方法：对1132例在意大利和澳大利亚接受SNB治疗的黑色素瘤患者的临床病理资料（年龄、性别、肿瘤厚度、Clark水平、消退、溃疡、组织学亚型、有丝分裂指数）进行分析。对数据进行了逻辑回归、分类树、随机森林和支持向量机模型的拟合。建立预测模型的目的是使负预测值（NPV）最大化，并通过最小化错误率来降低SNB程序的发生率。结果：经过交叉验证的逻辑回归，分类树、随机森林和支持向量机预测模型获得了临床相关的NPV（分别为93.6%、94.0%、97.1%和93.0%）、SNB减少（分别为27.5%、29.8%、18.2%和30.1%）和错误率（分别为1.8%、1.8%、0.5%和2.1%）。讨论：使用常用的临床病理变量，预测模型可以在可接受的（1%-2%）误差范围内，术前识别出可能避免SNB的患者比例（约25%）。如果在大型前瞻性系列中得到验证，这些模型可能会在临床环境中实施，以改善患者选择，最终将提高患者的生活质量，优化医疗保健系统的资源配置。
Objective: To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status.Summary Background Data: About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNB-associated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status.Methods: The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate.Results: After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively).Discussion: Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients (similar to 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.