A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [18F]FDG PET/CT: a pathology-controlled study

Abstract

Objectives: Initial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [¹⁸F]FDG PET/CT provides insufficient and variable diagnostic performance. We aimed to develop and validate a machine-learning-based combination of criteria on [¹⁸F]FDG PET/CT to accurately identify pelvic LN involvement in bladder cancer patients.

Methods: Consecutive patients with localized MIBC who performed preoperative [¹⁸F]FDG PET/CT between 2010 and 2017 were retrospectively assigned to training (n = 129) and validation (n = 44) sets. The reference standard was the pathological status after extended pelvic LN dissection. In the training set, a random forest algorithm identified the combination of criteria that best predicted LN status. The diagnostic performances (AUC) and interrater agreement of this combination of criteria were compared to a consensus of experts.

Results: The overall prevalence of pelvic LN involvement was 24% (n = 41/173). In the training set, the top 3 features were derived from pelvic LNs (SUVmax of the most intense LN, and product of diameters of the largest LN) and primary bladder tumor (product of diameters). In the validation set, diagnostic performance did not differ significantly between the combination of criteria (AUC = 0.59 95%CI [0.43-0.73]) and the consensus of experts (AUC = 0.64 95%CI [0.48-0.78], p = 0.54). The interrater agreement was equally good with Κ = 0.66 for both.

Conclusion: The developed machine-learning-based combination of criteria performs as well as a consensus of experts to detect pelvic LN involvement on [¹⁸F]FDG PET/CT in patients with MIBC.

Key points: • The developed machine-learning-based combination of criteria performs as well as experts to detect pelvic LN involvement on [¹⁸F]FDG PET/CT in patients with muscle-invasive bladder cancer. • The top 3 features to predict LN involvement were the SUVmax of the most intense LN, the product of diameters of the largest LN, and the product of diameters of the primary bladder tumor.

Keywords: Artificial intelligence; Lymph nodes; Machine learning; Positron emission tomography computed tomography; Urinary bladder neoplasms.