LAUSR

Objectives To evaluate the usefulness of a radiomics-based prediction model for predicting response and survival outcomes of patients with metastatic urothelial carcinoma treated with immunotherapy targeting programmed cell death 1 (PD-1) and its ligand (PD-L1). Methods Sixty-two patients who underwent immunotherapy were divided into training ( n  = 41) and validation sets ( n  = 21). A total of 224 measurable lesions were identified on contrast-enhanced CT. A radiomics signature was constructed with features selected using a least absolute shrinkage and selection operator algorithm in the training set. A radiomics-based model was built based on a radiomics signature consisting of five reliable RFs and the presence of visceral organ involvement using multivariate logistic regression. According to a cutoff determined on the training set, patients in the validation set were assigned to either high- or low-risk groups. Kaplan-Meier analysis was performed to compare progression-free and overall survival between high- and low-risk groups. Results For predicting objective response and disease control, the areas under the receiver operating characteristic curves of the radiomics-based model were 0.87 (95% CI, 0.65–0.97) and 0.88 (95% CI, 0.67–0.98) for the validation set, providing larger net benefit determined by decision curve analysis than without radiomics-based model. The high-risk group in the validation set showed shorter progression-free and overall survival than the low-risk group (log-rank p  = 0.044 and p  = 0.035). Conclusions The radiomics-based model may predict the response and survival outcome in patients treated with PD-1/PD-L1 immunotherapy for metastatic urothelial carcinoma. This approach may provide important and decision tool for planning immunotherapy. Key Points • A radiomics-based model was built based on radiomics features and the presence of visceral organ involvement for prediction of outcomes in metastatic urothelial carcinoma treated with immunotherapy. • This prediction model demonstrated good prediction of treatment response and higher net benefit than no model in the independent validation set. • This radiomics-based model demonstrated significant associations with progression-free and overall survival between low-risk and high-risk groups.