Recurrence after RC is one of the main factors affecting the prognosis of BCa [14]. At present, the most important method for diagnosing BCa is cystoscopy. Imaging plays an irreplaceable role in the early diagnosis and evaluation of treatment effects in patients and has obvious noninvasive advantages over endoscopy [15,16,17,18]. Pathological grade is still the most critical factor affecting the treatment and prognosis of BCa, but with the rise of radiomics applications in oncology [19], extracting high throughput information based on raw images may be able to quantify tumor heterogeneity earlier [20, 21]. Therefore, noninvasive tools for predicting recurrence after RC for BCa appear to be valuable in clinical decision-making.

To date, several relevant studies have reported the application value of CT radiomics in predicting the prognosis after BCa surgery, among which Piotr Woznicki et al. found that radiomics features based on preoperative CT scans had prognostic value in predicting overall survival before RC in a study of 301 BCa patients who underwent RC and pelvic lymphadenectomy. Among them, the AUC of the clinical model was 0.761, and the AUC of the radiomics model was 0.771, which suggests that the predictive performance of the radiomics model is comparable to that of the verified clinical parameters [11]. Qian et al. reported that radiomics features extracted from multistage CT images combined with important clinicopathological risk factors can predict the recurrence of BCa 2 years after surgery, but the recurrence of BCa after RC was not included separately in the systematic study [12, 13]. Therefore, whether CT radiomics combined with clinical factors can predict local recurrence or metastatic recurrence after RC for BCa needs to be further verified.

In this study, a radiomics-clinical nomogram model based on preoperative CT extraction of high throughput radiomics features and important clinical risk factors was initially explored for individualized BCa recurrence risk stratification after RC.

Among the important clinical factors selected, our univariate analysis showed that tumor size, pathologic T stage, and lymph node stage were strongly associated with tumor recurrence after RC in patients with BCa and that these factors influenced the prognosis of patients, consistent with previous studies [22, 23]. However, tumors are heterogeneous in individual patients, and the TNM staging system used to predict long term survival is not entirely accurate [24,25,26]. Interestingly, based on high throughput data normalization and the lasso regression algorithm, eight radiomics texture features with the highest correlation with tumor recurrence after RC in patients with BCa were finally included, including one firstorder feature, four GLSZM features, one GLCM feature, one GLDM feature and one GLRLM feature. In recent years, they have been extensively studied [27, 28]. Preliminary evidence suggests that these features may be a good characterization of histoheterogeneity and histopathological differences among patients with BCa [29]. Multivariate analysis combining tumor size, pathologic T stage, lymph node stage and Rad-Score showed that pathological T stage and Rad-Score were independent risk factors for tumor recurrence after RC. Based on both, we developed a radiomics-clinical nomogram model for recurrence risk stratification. The results showed that the AUC (95% CI) of the training and validation sets of the nomogram model was 0.840 (0.743–0.937) and 0.883 (0.777–0.989), respectively, which was significantly higher than that of the radiomics model, indicating that the radiomics- clinical model was superior to the radiomics model in predicting recurrence after RC in patients with BCa. The calibration plot confirmed that the predicted performance is reliable and valid. DCA also clearly showed that, when the risk threshold is greater than 0.1, the composite model has more net benefits than the clinical model alone or the radiomics model.

According to the nomogram, when pathologic T stage was less than 2 and Rad-Score was -0.5717195401, the minimum value reaching the predicted value was less than 0.1. When pathologic T stage was more than 2, the Rad-Score was 2.336809971, the maximum predicted value is greater than 0.9. The recurrence risk group was divided according to the median predicted value of the training set's nomogram model, that is, the median recurrence risk score. the comprehensive comparison of various factors showed that the radiomics-clinical model is still better than the other two models. Chi-square test results showed a close correlation between recurrence risk stratification and true recurrence.

KM map analysis showed that RFS of patients with different recurrence risk groups were also different. The performance of radiomics-clinical nomogram is valuable for the estimation of RFS. In summary, the nomogram scoring system of this study may serve as a more effective tool to accurately and rapidly assess the risk of recurrence after RC through simple computational methods, enabling early individualized treatment of BCa patients.

Our current research still has some unanswered questions. First, this was a single center study with a limited sample size, which requires additional multicenter data samples and external validation. Second, due to the incompleteness of the data in the single institutional database, possible predictors, such as tumor location and radical urinary diversion, were not included in this study, and subsequent studies can further analyze these in depth. In addition, biomarkers, such as WDR72 and methylation of LMX1A, have been reported to be closely associated with BCa recurrence [30, 31]. These factors were not included in this study, and we will try to build more efficient predictive models by combining clinical factors, radiomics, and important biomarkers. What's more, by repeatedly modeling and using multiple data dimensionality reduction methods to reduce overfitting, it is difficult to find an absolute subset of the selected features because it relies on the segmentation of the training and test sets. We believe that future research may be able to establish a variety of models and provide the more reliable solution for clinical diagnosis and treatment after multiple evaluations. As a result, this study still needs to be further improved before clinical application [32].

In conclusion, this study combined clinical factors and radiomics characteristics of CT to construct a composite model to predict local or metastatic recurrence of BCa after RC. These preliminary results showed that the model has good efficacy in stratifying recurrence risk, which may provide some help for the individualized treatment of BCa patients.