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

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.