背景:对于恶性黑色素瘤,存在四个外部验证的前哨淋巴结活检(SNB)预测列线图,每个都包含不同的临床和组织病理学变量,这可能导致同一患者的风险估计大不相同。我们通过使用假设的黑色素瘤病例证明了这种变异性。
方法:我们比较了MSKCC和MIA计算器。使用随机数生成器,300个假想的薄薄的黑色素瘤“患者”是由不同的年龄创造的,肿瘤厚度,克拉克级别,尸体上的位置,溃疡,黑色素瘤亚型,有丝分裂,和淋巴管浸润(LVI)。卡方检验用于检测列线图之间风险估计的统计学显着差异。在预测差异>10%的情况下,使用多元线性回归来确定最相关的贡献病理特征。
结果:在300个随机产生的病例中,164个被删除,因为他们的临床情况不太可能。MSKCC列线图计算出的风险通常低于MIA(p<0.001)。使用MSKCC计算器,任何“患者”获得的最高风险评分为136名患者中的一名(0.7%)达到15%,而使用MIA列线图,136例患者中的58例(43%,p<0.001)预测风险>15%。对列线图差异>10%的患者进行回归分析显示LVI(26,p<0.001),有丝分裂(14,p<0.001),和黑色素瘤亚型(8,p<0.001)是MIA中系数高的因素,在MSKCC中不存在。
结论:在预测SNB风险时,列线图是有用的工具,但提供的风险输出对所包含的预测因子相当敏感。
BACKGROUND: Four externally validated sentinel node biopsy (SNB) prediction nomograms exist for malignant melanoma that each incorporate different clinical and histopathologic variables, which can result in substantially different risk estimations for the same patient. We demonstrate this variability by using hypothetical melanoma cases.
METHODS: We compared the MSKCC and MIA calculators. Using a random number generator, 300 hypothetical thin melanoma \"patients\" were created with varying age, tumor thickness, Clark level, location on the body, ulceration, melanoma subtype, mitosis, and lymphovascular invasion (LVI). The chi-square test was used to detect statistically significant differences in risk estimations between nomograms. Multivariate linear regression was used to determine the most relevant contributing pathologic features in cases where the predictions diverged by > 10%.
RESULTS: Of 300 randomly generated cases, 164 were deleted as their clinical scenarios were unlikely. The MSKCC nomogram generally calculated a lower risk than the MIA (p < 0.001). The highest risk score attained for any \"patient\" using MSKCC calculator was 15% achieved in one of 136 patients (0.7%), whereas using the MIA nomogram, 58 of 136 patients (43%, p < 0.001) had predicted risk >15%. Regression analysis on patients with >10% difference between nomograms revealed LVI (26, p < 0.001), mitosis (14, p < 0.001), and melanoma subtype (8, p < 0.001) were the factors with high coefficients within MIA that were not present in MSKCC.
CONCLUSIONS: Nomograms are useful tools when predicting SNB risk but provide risk outputs that are quite sensitive to included predictors.