Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer

Background: Bladder cancer (BC) treatment algorithms depend on accurate tumor stag-ing. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited.Objective: To evaluate the performance of quantitative radiomics signatures for detec-tion of LN metastases in BC.Design, setting,and participants: Of 1354 patients with BC who underwent radical cys-tectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automat-ically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth.Outcome measurements and statistical analysis: Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detec-tion of LN metastases in BC. Statistical analysis was performed using the receiver oper-ating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC).Results and limitations: In total, 1845 LNs were manually segmented. Automated seg-mentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63).Conclusions: A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach.Patient summary: For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options. (c) 2022 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Automated summary

This study aimed to evaluate the performance of quantitative radiomics signatures in the detection of lymph node metastases in bladder cancer. By analyzing 1354 patients who underwent radical cystectomy, it was found that the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. The results showed that radiomics signatures can accurately discriminate lymph node metastases.