A novel tool for predicting the risk of cancer-specific early death in older patients with primary malignant melanoma of skin: a population-based analysis

IF 3.5 3区 医学 Q2 ONCOLOGY Frontiers in Oncology Pub Date : 2024-09-06 DOI:10.3389/fonc.2024.1387014
Yan Lei, Shucui Wang, Jun Chen, Lanjun Liu, Linting Huang, Xiujuan Wu, Hui Xu, Yali Yang
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Abstract

BackgroundPrimary malignant melanoma (MM) of skin threatens health, especially in the older population, causing a significant risk of early death. The purpose of this study was to establish a diagnostic nomogram to predict the early mortality risk in older patients with primary skin MM and to determine the independent risk factors of cancer-specific early death in such patients.MethodsThe Surveillance, Epidemiology and End Results (SEER) database provided the clinical and pathological characteristics of older patients with primary skin MM from 2000 to 2019. Initially, a 7:3 random assignment was used to place the recruited patients into training and validation cohorts. Then, the independent risk variables of cancer-specific early death in those individuals were determined using univariate and multivariate logistic regression analysis. Those patients’ diagnostic nomograms were constructed using the acquired independent risk variables. Ultimately, the performance of the newly created diagnostic nomogram was verified using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves.ResultsIn this study, 2,615 patients in total were included. Age, histology, liver metastasis, tumor stage, surgery, therapy, and radiation were found to be independent risk factors following statistical analysis, with a special emphasis on early death in older patients with primary skin MM. A diagnostic nomogram for the cancer-specific early death risk was created and validated based on these variables. High agreement was reported between the expected and actual probabilities in the calibration curves. Area under the curves (AUC) of the novel created diagnostic nomogram was greater than that of each independent risk factor, with AUCs for the training and validation cohorts being 0.966 and 0.971, respectively. The nomogram had a high value for its applicability in clinical settings, according to DCA.ConclusionIn older patients with primary skin MM, the current study created a diagnostic nomogram to predict the probability of cancer-specific early death. Because of the nomograms’ good performance, physicians will be better able to identify older patients who are at a high risk of early death and treat them individually to increase their survival benefit.
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预测老年原发性恶性皮肤黑色素瘤患者癌症特异性早期死亡风险的新工具:基于人群的分析
背景原发性皮肤恶性黑色素瘤(MM)威胁着人们的健康,尤其是在老年人群中,会导致很大的早期死亡风险。本研究的目的是建立一个诊断提名图来预测老年原发性皮肤 MM 患者的早期死亡风险,并确定此类患者癌症特异性早期死亡的独立风险因素。方法监测、流行病学和最终结果(SEER)数据库提供了 2000 年至 2019 年老年原发性皮肤 MM 患者的临床和病理特征。首先,采用7:3随机分配法将所招募的患者分为训练队列和验证队列。然后,利用单变量和多变量逻辑回归分析确定了这些患者癌症特异性早期死亡的独立风险变量。利用获得的独立风险变量构建这些患者的诊断提名图。最后,利用校准曲线、接收者操作特征曲线(ROC)和决策曲线分析(DCA)曲线验证了新创建的诊断提名图的性能。经统计分析发现,年龄、组织学、肝转移、肿瘤分期、手术、治疗和放疗是独立的风险因素,其中年龄较大的原发性皮肤 MM 患者的早期死亡尤为突出。根据这些变量创建并验证了癌症特异性早期死亡风险诊断提名图。据报告,校准曲线中的预期概率与实际概率高度一致。新创建的诊断提名图的曲线下面积(AUC)大于每个独立风险因素的曲线下面积,训练组和验证组的AUC分别为0.966和0.971。结论 在老年原发性皮肤 MM 患者中,本研究创建了一个诊断提名图来预测癌症特异性早期死亡的概率。由于提名图的良好表现,医生将能更好地识别有较高早期死亡风险的老年患者,并对他们进行个体化治疗,以提高他们的生存率。
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
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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