Improved Risk Prediction Calculator for Sentinel Node Positivity in Patients With Melanoma: The Melanoma Institute Australia Nomogram
Improved Risk Prediction Calculator for Sentinel Node Positivity in Patients With Melanoma: The Melanoma Institute Australia Nomogram
复制标题
DOI：
10.1200/jco.19.02362
复制
发表时间：
2020-08-20
影响因子：
45.3
通讯作者：
Varey, Alexander H. R.
中科院分区：
文献类型：
--
作者：
Lo, Serigne N.;Ma, Jiawen;Varey, Alexander H. R.
目的对于原发性皮肤黑色素瘤患者，前哨淋巴结（SN）转移的风险根据多种临床病理参数而变化。国家综合癌症网络 (NCCN) 和 ASCO/肿瘤外科学会 (SSO) 指南以及纪念斯隆·凯特琳癌症中心 (MSKCC) 在线列线图可以协助选择 SN 活检的患者。我们试图使用替代临床病理参数开发一种改进的在线风险计算器，以更准确地预测 SN 阳性。 患者和方法 对在澳大利亚黑色素瘤研究所 (MIA) 接受 SN 活检的 3,477 名黑色素瘤患者的数据进行了分析。通过用有丝分裂率、黑色素瘤亚型和淋巴血管侵犯替换 MSKCC 模型中的身体部位和 Clark 水平，开发了新的列线图。新列线图的预测性能使用来自德克萨斯大学 MD 安德森癌症中心 (n = 3,496) 的数据进行了外部验证。 结果 MSKCC 模型受试者工作特征曲线的预测准确度为 67.7%（95% CI，65.3% 至 70.0%）。 MIA 模型的预测准确度为 73.9%（95% CI，71.9% 至 75.9%），比 MSKCC 模型准确度提高了 9.2% (P < .001)。在 2,748 名 SN 阴性患者中，根据 MIA 模型、MSKCC 模型以及 NCCN 或 ASCO/SSO 标准，未进行 SN 活检的比例分别为 22.1%、13.4% 和 12.4%。外部验证生成的 C 统计值为 75.0%（95% CI，73.2% 至 76.7%）。 结论 开发了稳健的列线图，比现有方法更准确地估计黑色素瘤患者 SN 阳性的风险。该模型仅需要输入 6 个广泛使用的临床病理参数。重要的是，与使用 MSKCC 列线图或 NCCN 或 ASCO/SSO 指南相比，接受不必要的 SN 活检的患者数量将显着减少，而不会失去敏感性。在线计算器可在 www.melanomarisk.org.au 上找到。
PURPOSE For patients with primary cutaneous melanoma, the risk of sentinel node (SN) metastasis varies according to several clinicopathologic parameters. Patient selection for SN biopsy can be assisted by National Comprehensive Cancer Network (NCCN) and ASCO/Society of Surgical Oncology (SSO) guidelines and the Memorial Sloan Kettering Cancer Center (MSKCC) online nomogram. We sought to develop an improved online risk calculator using alternative clinicopathologic parameters to more accurately predict SN positivity.PATIENTS AND METHODS Data from 3,477 patients with melanoma who underwent SN biopsy at Melanoma Institute Australia (MIA) were analyzed. A new nomogram was developed by replacing body site and Clark level from the MSKCC model with mitotic rate, melanoma subtype, and lymphovascular invasion. The predictive performance of the new nomogram was externally validated using data from The University of Texas MD Anderson Cancer Center (n = 3,496).RESULTS The MSKCC model receiver operating characteristic curve had a predictive accuracy of 67.7% (95% CI, 65.3% to 70.0%). The MIA model had a predictive accuracy of 73.9% (95% CI, 71.9% to 75.9%), a 9.2% increase in accuracy over the MSKCC model (P < .001). Among the 2,748 SN-negative patients, SN biopsy would not have been offered to 22.1%, 13.4%, and 12.4% based on the MIA model, the MSKCC model, and NCCN or ASCO/SSO criteria, respectively. External validation generated a C-statistic of 75.0% (95% CI, 73.2% to 76.7%).CONCLUSION A robust nomogram was developed that more accurately estimates the risk of SN positivity in patients with melanoma than currently available methods. The model only requires the input of 6 widely available clinicopathologic parameters. Importantly, the number of patients undergoing unnecessary SN biopsy would be significantly reduced compared with use of the MSKCC nomogram or the NCCN or ASCO/SSO guidelines, without losing sensitivity. An online calculator is available at www.melanomarisk.org.au.