Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer

The retrospective study was approved by an institutional ethics review board and exempted patients from the need for informed consent, the approval numbers and dates of the Institutional Review Borad are (B) KY2022238 and December 30, 2022, respectively. In total, 134 patients who underwent RC at the Southwest Hospital of Army Medical University from June 2014 to June 2019 and were diagnosed with postoperative pathological confirmation of BCa were retrospectively enrolled. The inclusion criteria were as follows: (1) underwent RC and was pathologically confirmed as BCa; (2) CT scan was performed 1 month before RC, and the images were clear; and (3) complete clinicopathological data were available. The exclusion criteria were as follows: (1) CT scan of poor quality, mainly manifested as unclear display of bladder tumors caused by artifacts or other factors; (2) other treatment, including chemotherapy or immunotherapy, prior to CT scan; and (3) incomplete clinicopathological data or loss to follow-up.

SPSS 26.0 software (IBM) was used for univariate and multivariate analyses to study the clinical risk factors and independent risk factors among the cohort. A t test was used for measurement data, a chi-square test was used for counting data, and single factor analysis of variance was used for preliminary feature screening. In this study, R statistical software version 4.2.2 (https://​www.​r-project.​org/​) was used for ICC calculation, model building and performance evaluation, and survival curve drawing. The R packages used included “glmnet,” “rms,” “pROC,” “rmda,” and “survival.” P<0.05 indicates statistical significance.

Overall, 134 patients with BCa were enrolled in this study. All patients underwent RC and pelvic lymph node dissection followed by urinary diversion (Briker or orthotropic ileal bladder surgery). Among them, 119 were males, and 15 were females, with a mean age of 64.4 ± 9.7 years. During the 3-year follow-up, patients were divided into a recurrence group (n=24) and a no recurrence group (n=110) according to whether they relapsed. The median RFS of patients with relapse was 12 months. Comparisons of gender, age, tumor number, tumor size, grade, pathologic T stage, lymph node stage and histology type between the two groups are shown in Table 1.

In this study, 1316 radiomics features were extracted, among which the average features of ICC>0.75 showed good agreement between the two groups of observers, and 1076 features of ICC>0.75 prepared the following feature selection. The correlation threshold of 0.9 was selected for correlation coefficient analysis, and the 13 best features with a P value of <0.05 were then screened out by analysis of variance. Each feature data point was standardized, and 8 features were screened out after 10-fold cross verification (Fig. 2a, b) using LASSO regression algorithm. Finally, they were established as logistic regression models for radiomics. The model showed an AUC of 0.777 (95% CI 0.666–0.887) for the training set and 0.772 (95% CI 0.634–0.911) for the validation set (Fig. 2c, d). Based on the independent variable coefficients and cutoff values of the radiomics model, the regression coefficients were obtained. In the training set, the 8 retained features were linearly combined by the corresponding coefficient weighting to obtain the calculation formula of Rad-Score, which could quantify the radiomic features and predict the recurrence risk.

Univariate and multivariate analyses were performed based on the Rad-Score and important clinical factors of the patients in the training set, in which tumor size, pathologic T stage, lymph node stage and Rad-Score were identified as clinical risk factors (P <0.05). Meanwhile, in the multivariate analysis based on clinical risk factors, pathologic T stage (OR 0.176, 95% CI 0.036–0.653 P=0.015) and Rad-score (OR 2.933, 95% CI 1.336–7.951 P=0.018) were still identified as independent prognostic factors, as shown in Table 2. Combined with the Rad-score and pathologic T stage, a radiomics-clinical nomogram model for predicting local or metastatic recurrence after RC in BCa patients was established (Fig. 3a). The ROC curves of the model showed that the AUCs of the training set and the validation set were 0.840 (95% CI 0.743-0.937) and 0.883 (95% CI, 0.777-0.989), respectively, the accuracies were 0.817 and 0.878 (Fig. 3b, c), respectively, and the calibration curves of the training set and the validation set performed well, showing that the model can predict the recurrence rate of BCa within 3 years after RC in good agreement with the actual situation (Fig. 3d, e). According to the nomogram, if the patient is pathologic T stage≥2 and the Rad-Score is -2, the scores will be close to 17.5 and 45, respectively, and the “total score” is 62.5 points, which corresponds to a “recurrence risk” of approximately 0.18 points. The patients were divided into a low risk recurrence group and a high risk recurrence group according to the median recurrence risk in the training set (0.12). The decision curves of different models in the cohort are shown in Fig. 3f, DCA shows that the clinical utility performance of the radiomics-clinical model is higher than that of the clinical model and the radiomics model. As shown in Fig. 4, the performance of recurrence risk stratification was quantitatively evaluated, and the comprehensive prediction performance of the radiomics-clinical model was significantly better than that of the clinical model and the radiomics model, as shown in Table 3. Based on nomogram recurrence risk stratification, Kaplan-Meier (KM) plots of RFS in the first 3 years after RC in the low risk recurrence group and high risk recurrence group were plotted, as shown in Fig. 5.