Work

A predictive system comprising serum microRNAs and radiomics for residual retroperitoneal masses in metastatic nonseminomatous germ cell tumors

Year: 2024

Type: article

Abstract: Predicting the histopathology of residual retroperitoneal masses (RMMs) before post-chemotherapy retroperitoneal lymph node dissection in metastatic nonseminomatous germ cell tumors (NSGCTs) can guide individualized treatment and minimize complications. Previous single approach-based models perform poorly in validation. Herein, we introduce a machine learning model that evolves from a single-dimensional tumor diameter to incorporate high-dimensional radiomic features, with its effectiveness assessed using the macro-average area under the receiver operating characteristic curves (AUCs). In addition, we utilize more precise and specific microRNAs (miRNAs), not common clinical indicators, to construct an integrated radiomics-miRNA predictive system, achieving an AUC of 0.91 (0.80–0.99) in the prospective test set. We further develop a web-based dynamic nomogram for swift and precise calculation of the histopathological probabilities of RMMs based on radiomic scores and serum miRNA levels. The radiomics-miRNA integrated system offers a promising tool to select personalized treatments for patients with metastatic NSGCT. • A radiomics-miRNA model enables prediction of the histopathology of pcRPLND • The integrated model shows better predictive accuracy than the single methodology • A web-based tool enables swift calculation of histopathological probabilities Li et al. develop a radiomics-microRNA integrated predictive system, which can predict the histopathology of residual retroperitoneal masses before post-chemotherapy retroperitoneal lymph node dissection in metastatic nonseminomatous germ cell tumors. The utilization of microRNAs significantly enhances the predictive performance of the integrated model, demonstrating its potential for personalized treatment.

Source: Cell Reports Medicine

DOI: 10.1016/j.xcrm.2024.101843