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Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To investigate computed tomography (CT)-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer (NMIBC).

Methods

This retrospective study evaluated 147 consecutive patients who underwent contrast-enhanced CT and surgery for bladder cancer. Using corticomedullary-to-portal venous phase images, two independent readers analyzed bladder muscle invasion, tumor stalk, and tumor size, respectively. Three-point scale (i.e., from 0 to 2) was applied for assessing the suspicion degree of muscle invasion or tumor stalk. A multivariate prediction model using the CT parameters for achieving high positive predictive value (PPV) for NMIBC was investigated. The PPVs from raw data or 1000 bootstrap resampling and inter-reader agreement using Gwet’s AC1 were analyzed, respectively.

Results

Proportion of patients with NMIBC was 81.0% (119/147). The CT criteria of the prediction model were as follows: (a) muscle invasion score < 2; (b) tumor stalk score > 0; and (c) tumor size < 3 cm. From the raw data, PPV of the model for NMIBC was 92.7% (51/55; 95% confidence interval [CI] 82.4–98.0) in reader 1 and 93.3% (42/45; 95% CI 81.7–98.6) in reader 2. From the bootstrap data, PPV was 92.8% (95% CI 85.2–98.3) in reader 1 and 93.4% (95% CI 84.9–99.9) in reader 2. The model’s AC1 was 0.753 (95% CI 0.647–0.859).

Conclusion

The current CT-derived prediction model demonstrated high PPV for identifying patients with NMIBC. Depending on CT findings, approximately 30% of patients with bladder cancer may have a low need for additional MRI for interpreting vesical imaging-reporting and data system.

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Authors and Affiliations

  1. Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

    Kyung A Kang, Min Je Kim, Chan Kyo Kim & Sung Yoon Park

  2. Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Ghee Young Kwon

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Kang, K.A., Kim, M.J., Kwon, G.Y. et al. Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer. Abdom Radiol 49, 163–172 (2024). https://doi.org/10.1007/s00261-023-04069-8

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