Using apparent diffusion coefficient maps and radiomics to predict pathological grade in upper urinary tract urothelial carcinoma.

BACKGROUND: The apparent diffusion coefficient (ADC) has been reported as a quantitative biomarker for assessing the aggressiveness of upper urinary tract urothelial carcinoma (UTUC), but it has typically been used only with mean ADC values. This study aims to develop a radiomics model using ADC maps to differentiate UTUC grades by incorporating texture features and to compare its performance with that of mean ADC values. METHODS: A total of 215 patients with histopathologically confirmed UTUC were enrolled retrospectively and divided into training and test sets. The optimum cutoff value for the mean ADC was derived using the receiver operating characteristic (ROC) curve. Radiomics features based on ADC maps were extracted and screened, and then a radiomics model was constructed. Both mean ADC values and the radiomics model were tested on the training and test sets. ROC curve and DeLong test were used to assess the diagnostic performance. RESULTS: The training set consisted of 151 patients (median age: 68.0, IQR: [63.0, 75.0] years; 80 males), whereas the test set consisted of 64 patients (median age: 68.0, IQR: [61.0, 72.3] years; 31 males). The ADC values were significantly lower in high-grade versus low-grade UTUC (1310 × 10(- 6)mm(2)/s vs. 1480 × 10(- 6)mm(2)/s, p < 0.001). The area under the curve (AUC) values of the mean ADC values in the training and test sets were 0.698 [95% confidence interval [CI]: 0.625-0.772] and 0.628 [95% CI: 0.474-0.782], respectively. Compared with the mean ADC values, the ADC-based radiomics model, which incorporates features such as log-sigma-1-0-mm-3D_glcm_ClusterProminence and wavelet-LLL_firstorder_10Percentile, obtained a significantly greater AUC in the training set (AUC: 1.000, 95% CI: 1.000-1.000, p < 0.001), and a trend towards statistical significance in the test set (AUC: 0.786, 95% CI: 0.651-0.921, p = 0.071). CONCLUSIONS: The ADC-based radiomics model showed promising potential in predicting the pathological grade of UTUC, outperforming the mean ADC values in classification accuracy. Further studies with larger sample sizes and external validation are necessary to confirm its clinical utility and generalizability. CLINICAL TRIAL NUMBER: Not applicable.