Scholarlink

Background <p>The association between membranous nephropathy (MN) and malignant tumors has long been focused. However, most existing studies have primarily concentrated on patients diagnosed with malignant tumors within a limited timeframe, typically defined as one year before or after the diagnosis of MN. This narrow focus only captures a subset of MN patients complicated by malignant tumors, leaving those diagnosed outside this timeframe understudied and largely unexplored. In the present study, we aim to comprehensively investigate the clinicopathological characteristics of MN patients complicated with malignant tumors and to develop an effective predictive model for identifying the risk of malignancy in MN patients.</p> Methods <p>A retrospective analysis was conducted on the demographic, clinical, and pathological characteristics of 174 MN patients complicated with malignant tumors and 604 idiopathic membranous nephropathy (IMN) patients without malignant tumors. All patients were randomly allocated into a training cohort (<i>n</i> = 584) and a validation cohort (<i>n</i> = 194) in a 3:1 ratio. A predictive model was developed using regression analysis, and its performance was evaluated in terms of discrimination, calibration, and clinical utility through the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA).</p> Results <p>MN patients complicated with malignant tumors demonstrated significantly increased deposition rates of glomerular IgG1, IgG2, IgG3, and PLA2R, as well as decreased deposition rates of IgG4. Based on independent risk factors, a predictive model was developed, which exhibited excellent performance upon validation.</p> Conclusion <p>In this largest cohort to date of MN patients with malignant tumors, a predictive model was constructed using pathological parameters to estimate the risk of malignancy effectively. This tool aims to assist clinicians in decision-making and improve the prognosis of high-risk MN patients by facilitating tumor screening at the time of initial diagnosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Construction and validation of a predictive model for malignant tumors in patients with membranous nephropathy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月22日

查看全文
  • Yaling Zhai,
  • Shuaigang Sun,
  • Wenhui Zhang,
  • Huijuan Tian,
  • Zhanzheng Zhao

摘要

Background

The association between membranous nephropathy (MN) and malignant tumors has long been focused. However, most existing studies have primarily concentrated on patients diagnosed with malignant tumors within a limited timeframe, typically defined as one year before or after the diagnosis of MN. This narrow focus only captures a subset of MN patients complicated by malignant tumors, leaving those diagnosed outside this timeframe understudied and largely unexplored. In the present study, we aim to comprehensively investigate the clinicopathological characteristics of MN patients complicated with malignant tumors and to develop an effective predictive model for identifying the risk of malignancy in MN patients.

Methods

A retrospective analysis was conducted on the demographic, clinical, and pathological characteristics of 174 MN patients complicated with malignant tumors and 604 idiopathic membranous nephropathy (IMN) patients without malignant tumors. All patients were randomly allocated into a training cohort (n = 584) and a validation cohort (n = 194) in a 3:1 ratio. A predictive model was developed using regression analysis, and its performance was evaluated in terms of discrimination, calibration, and clinical utility through the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

MN patients complicated with malignant tumors demonstrated significantly increased deposition rates of glomerular IgG1, IgG2, IgG3, and PLA2R, as well as decreased deposition rates of IgG4. Based on independent risk factors, a predictive model was developed, which exhibited excellent performance upon validation.

Conclusion

In this largest cohort to date of MN patients with malignant tumors, a predictive model was constructed using pathological parameters to estimate the risk of malignancy effectively. This tool aims to assist clinicians in decision-making and improve the prognosis of high-risk MN patients by facilitating tumor screening at the time of initial diagnosis.