A radiomics model derived by combination of radiomics signature and clinical risk factors predict of lymph node metastasis for men renal pelvis urothelial carcinoma

Abstract

This study aims to propose a radiomics model to identify male patients suffering from renal pelvis urothelial carcinoma (RPUC) with preoperative lymph node (LN) metastasis. In a study involving 133 male RPUC patients, 94 were assigned to a training group and 39 to a testing group. Their arterial-phase computed tomography (CT) images were analyzed to extract radiomics features, which were then refined through data reduction and feature selection. Using the least absolute shrinkage and selection operator (LASSO), a radiomics signature was created, which was then incorporated into a Logistic regression classifier in the training group to predict pathologic lymph node metastases. A comprehensive radiomics model was developed using multivariate logistic regression, integrating clinical risk factors. The model’s efficacy was evaluated in both sets using discrimination, calibration and decision curve analyses in both the training and testing sets. The constructed signature, composed of eight promising imaging-derived features, showed strong discrimination ability in both sets (training: area under the curve (AUC) 0.836 and testing: AUC, 0.817). When combined with CT-reported tumor status, the radiomics model demonstrated excellent calibration and discrimination, achieving an AUC of 0.849 in the training set and 0.851 in the testing set. The radiomics model, incorporating both the radiomics signature and the CT-reported tumor status, could help in the preoperative individualized prediction of LN metastasis in male patients with RPUC.

Keywords

• renal pelvis urothelial carcinoma
• radiomics
• lymph node metastasis
• computed tomography
• men