A Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Mortality of Patients with Initially Diagnosed Metastatic Cutaneous Melanoma
A Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Mortality of Patients with Initially Diagnosed Metastatic Cutaneous Melanoma
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DOI：
10.1245/s10434-020-09341-5
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发表时间：
2020-11-15
影响因子：
3.7
通讯作者：
Zhang, Yange
中科院分区：
文献类型：
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作者：
Li, Wei;Xiao, Yang;Zhang, Yange
背景皮肤黑色素瘤和远处器官转移的结果各不相同。在预测模型中考虑所有预后指标可能有助于选择可以从个性化治疗策略中受益的病例。目的建立并验证转移性黑色素瘤患者的预后模型。方法从监测、流行病学和最终结果数据库中鉴定出1535例转移性皮肤黑色素瘤(IV期)患者。患者被随机分为训练组(n=1023)和验证组(n=512)。预后诺模图主要基于竞争风险回归模型的结果建立，用于预测癌症特异性死亡(CSD)。采用时变受试者工作特征曲线下面积(AUC)、校准曲线和判决曲线分析(DCAs)来评价诺模图。结果训练组和验证组的临床特征差异无统计学意义。在培训队列中，与CSD转移性黑色素瘤的患者、肿瘤和治疗相关的预测因素包括年龄、性别、种族、婚姻状况、保险、美国癌症T和N分期联合委员会、转移器官数量、手术治疗和化疗。所有这些因素都被用于诺模图的构建。训练和验证队列的随时间变化的AUC值表明诺模图具有良好的性能和区分性。6个月、12个月和18个月的AUC值在训练队列中分别为0.706、0.700和0.706，在验证队列中分别为0.702、0.670和0.656。6个月、12个月和18个月死亡概率的校准曲线显示，在两个队列中，诺模图的预测值与观察结果之间具有可接受的一致性。在不同时间点(6、12和18个月死亡)的大多数阈值概率中，DCA曲线在预测模型中显示出良好的正净收益。根据每个病例的诺模图总分，将所有患者分为低危组(n=511)、中危组(n=512)和高危组(n=512)，风险分类可以识别两个队列中死亡风险较高的病例。结论本研究建立了转移性黑色素瘤患者CSD的预测图和相应的风险分类系统，为转移性黑色素瘤患者的心理咨询和临床决策提供帮助。
Background Cutaneous melanoma and distant organ metastasis has varying outcomes. Considering all prognostic indicators in a prediction model might assist in selecting cases who could benefit from a personalized therapy strategy. Objective This study aimed to develop and validate a prognostic model for patients with metastatic melanoma. Methods A total of 1535 cases diagnosed with metastatic cutaneous melanoma (stage IV) were identified from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training (n = 1023) and validation (n = 512) cohorts. A prognostic nomogram was established based predominantly on results from the competing-risk regression model for predicting cancer-specific death (CSD). The area under the time-dependent receiver operating characteristic curve (AUC), calibration curves, and decision curve analyses (DCAs) were used to evaluate the nomogram. Results No significant differences were observed in the clinical characteristics between the training and validation cohorts. In the training cohort, patient-, tumor-, and treatment-related predictors of CSD for metastatic melanoma included age, sex, race, marital status, insurance, American Joint Committee on Cancer T and N stage, number of metastatic organs, surgical treatment, and chemotherapy. All these factors were used for nomogram construction. The time-dependent AUC values of the training and validation cohorts suggested a favorable performance and discrimination of the nomogram. The 6-, 12-, and 18-month AUC values were 0.706, 0.700, and 0.706 in the training cohort, and 0.702, 0.670, and 0.656 in the validation cohort, respectively. The calibration curves for the probability of death at 6, 12, and 18 months showed acceptable agreement between the values predicted by the nomogram and the observed outcomes in both cohorts. DCA curves showed good positive net benefits in the prognostic model among most of the threshold probabilities at different time points (death at 6, 12, and 18 months). Based on the total nomogram scores of each case, all patients were divided into the low-risk (n = 511), intermediate-risk (n = 512), and high-risk (n = 512) groups, and the risk classification could identify cases with a high risk of death in both cohorts. Conclusions A predictive nomogram and a corresponding risk classification system for CSD in patients with metastatic melanoma were developed in this study, which may assist in patient counseling and in guiding clinical decision making for cases with metastatic melanoma.