AUTHOR=Zechuan Liu , Tianshi Lyu , Tiantian Li , Shoujin Cao , Hang Yao , Ziping Yao , Haitao Guan , Zeyang Fan , Yinghua Zou , Jian Wang 

TITLE=The radiomics-clinical nomogram for predicting the response to initial superselective arterial embolization in renal angiomyolipoma, a preliminary study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1334706

DOI=10.3389/fonc.2024.1334706

ISSN=2234-943X

ABSTRACT=Purpose: To explore a radiomics-clinical model for predicting the response to initial superselective arterial embolization (SAE) in renal angiomyolipoma (RAML).: A total of 78 patients with RAML were retrospectively enrolled. Clinical data was recorded and evaluated. Radiomic features were extracted from preoperative contrast-enhanced CT (CECT). Least absolute shrinkage and selection operator (LASSO), intra-and inter-class correlation coefficients (ICCs) were used to feature selection. Logistic regression analysis was performed to develop the radiomics, clinical and combined models where 5-fold cross-validation method was used. The predictive performance and calibration were evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to measure the clinical usefulness. Results: Tumor shrinkage rate was 29.7% totally and both fat and angiomyogenic components were significantly reduced. In the radiomics model, 12 significant features were selected. In the clinical model, maximum diameter (p=0.001), angiomyogenic tissue ratio (p=0.032), aneurysms (p=0.048) and post-SAE time (p=0.002) were significantly associated with greater volume reduction after SAE. Because of the severe linear dependence between radiomics signature and some clinical parameters, the combined model eventually included Rad-score, aneurysm and post-SAE time. The radiomics-clinical model showed better discrimination (mean AUC=0.83) than radiomics model (mean AUC=0.60) and clinical model (mean AUC=0.82). Calibration curve and decision curve analysis 3 (DCA) showed goodness-of-fit and clinical usefulness of the radiomics-clinical model. Conclusions: The radiomics-clinical model incorporating radiomics features and clinical parameters can potentially predict the positive response to initial SAE in RAML and provide support for clinical treatment decisions.