Development and validation of a nomogram for elderly patients with ulcerative melanoma.

IF 1.5 4区医学 Q3 DERMATOLOGY

Melanoma Research Pub Date : 2023-12-13 DOI:10.1097/cmr.0000000000000940

Jie Yan, Haiyan Wang, Xiaoou Lu, Fengjuan Li

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Abstract

The current state of survival prediction models for elderly patients with ulcerative melanoma (uCM) is limited. We sought to develop a nomogram model that can predict overall survival of geriatric patients with uCM. The Surveillance, Epidemiology, and End Results (SEER) database served as a source for patients diagnosed with uCM between 2004 and 2015. Statistical analyses were conducted to determine the significant prognostic elements affecting overall survival using multivariate and univariate Cox proportional risk regression models. Subsequently, an independent forecasting nomogram was developed on the basis of these identified predictors. The predictive model was then assessed and validated through the utilization of receiver operating characteristic curves, calibration curves as well as decision curves. The study included a total of 5019 participants. Univariate and multivariate analyses revealed age, sex, marital status, primary site, tumor size, N stage, M stage, histological type, and surgery were independent prognostic factors. A nomogram was developed using the findings from both univariate and multivariate Cox analyses (P < 0.05). The receiver operating characteristic curves, which vary over time, and the area under the curve (AUC) for the training and validation cohorts, demonstrated the nomogram's strong discriminatory ability. Additionally, the calibration curves indicated satisfactory agreement between the predicted values from the nomogram and the practical outcomes observed in both cohorts. Furthermore, the decision curve analysis curves displayed favorable positive net gains at all times, when the critical value is most likely to occur. In this study, age, sex, marital status, primary site, tumor size, N stage, M stage, histologic type and surgery were determined as independent predictors for elderly patients with uCM. Then, a predictive model with good discriminatory ability was constructed to predict 12-, 24-, and 36-month overall survival in geriatric patients with uCM, which facilitates patients' counseling and individualized medical decision.

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开发并验证老年溃疡性黑色素瘤患者的提名图。

目前，针对老年溃疡性黑色素瘤（uCM）患者的生存预测模型非常有限。我们试图开发一种能预测老年溃疡性黑色素瘤患者总生存期的提名图模型。监测、流行病学和最终结果（SEER）数据库是 2004 年至 2015 年期间确诊为溃疡性黑色素瘤患者的资料来源。研究人员使用多变量和单变量考克斯比例风险回归模型进行了统计分析，以确定影响总生存期的重要预后因素。随后，在这些已确定的预测因素基础上开发了独立预测提名图。然后利用接收者操作特征曲线、校准曲线和决策曲线对预测模型进行评估和验证。该研究共纳入 5019 名参与者。单变量和多变量分析显示，年龄、性别、婚姻状况、原发部位、肿瘤大小、N 期、M 期、组织学类型和手术是独立的预后因素。根据单变量和多变量 Cox 分析结果（P < 0.05）绘制了一个提名图。随着时间推移而变化的接收者操作特征曲线以及训练组和验证组的曲线下面积（AUC）表明，提名图具有很强的判别能力。此外，校准曲线显示，提名图的预测值与两个队列中观察到的实际结果之间的一致性令人满意。此外，当临界值最有可能出现时，决策曲线分析曲线在任何时候都显示出有利的正净收益。在这项研究中，年龄、性别、婚姻状况、原发部位、肿瘤大小、N 期、M 期、组织学类型和手术被确定为老年尿路肿瘤患者的独立预测因素。然后，构建了一个具有良好判别能力的预测模型，用于预测老年肿瘤细胞癌患者 12 个月、24 个月和 36 个月的总生存率，这有助于为患者提供咨询和做出个体化医疗决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Melanoma Research 医学-皮肤病学

CiteScore

3.40

自引率

4.50%

发文量

139

审稿时长

6-12 weeks

期刊介绍： Melanoma Research is a well established international forum for the dissemination of new findings relating to melanoma. The aim of the Journal is to promote the level of informational exchange between those engaged in the field. Melanoma Research aims to encourage an informed and balanced view of experimental and clinical research and extend and stimulate communication and exchange of knowledge between investigators with differing areas of expertise. This will foster the development of translational research. The reporting of new clinical results and the effect and toxicity of new therapeutic agents and immunotherapy will be given emphasis by rapid publication of Short Communications. Thus, Melanoma Research seeks to present a coherent and up-to-date account of all aspects of investigations pertinent to melanoma. Consequently the scope of the Journal is broad, embracing the entire range of studies from fundamental and applied research in such subject areas as genetics, molecular biology, biochemistry, cell biology, photobiology, pathology, immunology, and advances in clinical oncology influencing the prevention, diagnosis and treatment of melanoma.