ArticleBMC nephrology2025
Construction and validation of a predictive model for malignant tumors in patients with membranous nephropathy.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodsA 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).
resultsMN 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.
conclusionIn 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.
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