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Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer

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

Abstract

Purpose

To investigate whether preoperative CT features can be used to predict risk stratification of non-muscle invasive bladder cancer (NMIBC).

Methods

The 168 patients with pathologically confirmed NMIBC who underwent preoperative CT urography were retrospectively analyzed and were divided into training (n = 117) and testing (n = 51) sets. According to the European Association of Urology Guidelines, patients were classified into low-risk (n = 50), medium-risk (n = 23), and high-risk (n = 95) groups. A random over-sample was performed to handle the offset caused by the unbalanced groups. We measured some CT features that may help stratify which for modeling were determined using an F-test-based feature selection with a tenfold cross-validation procedure, and the Gaussian Naive Bayes model was trained on the entire training set. In the testing set, the performance of the model was evaluated.

Results

The selected CT features were the maximum and the minimum diameter of the largest tumor, whether the largest tumor is located at the trigone, and tumor number. In the testing set, the model reached a macro- and micro- AUC of 0.783 and 0.745 with an accuracy of 0.529. As for the one-vs-rest problem, the model was most effective in identifying low-risk individuals, with an AUC, accuracy, sensitivity, and specificity of 0.870, 0.647, 1.000, and 0.438, respectively; the medium-risk group reached 0.814, 0.882, 0.250, and 0.936, respectively; the identification of the high-risk group was harder, going 0.665, 0.529, 0.250, and 0.870, respectively.

Conclusion

It is feasible to predict the risk stratification of NMIBC using preoperative CT features.

Graphical abstract

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

ADC:

Apparent diffusion coefficient

AUA:

American Urological Association

AUC:

Area under the curve

BCa:

Bladder cancer

CI:

Confidence interval

CIS:

Carcinoma in situ

CT:

Computed tomography

CTU:

CT urography

EAU:

European Association of Urology

HU:

Hounsfield Unit

NMIBC:

Non-muscle invasive BCa

ROC:

Receiver operating characteristic

ROI:

Region of interest

TURBT:

Transurethral resection of bladder tumor

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Acknowledgements

Li Mao, Xin Xiao, and Xiuli Li are employees of Deepwise AI Lab, Deepwise Healthcare, which contributed to the development of the CT-based prediction models described in the study. We’d like to thank Deepwise AI Lab, Deepwise Healthcare. for their technical support in this study.

Funding

This work was supported by National High Level Hospital Clinical Research Funding (Grant Nos. 2022-PUMCH-A-035, 2022-PUMCH-B-069, 2022-PUMCH-A-033), the Natural Science Foundation of China (Grant No. 81901742), the CAMS Innovation Fund for Medical Sciences (Grant No. 2022-I2M-C&T-B-019), the 2021 Key clinical Specialty Program of Beijing, Beijing Municipal Key Clinical Specialty Excellence Program.

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Author notes

  1. Li Chen and Gumuyang Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Li Chen, Gumuyang Zhang, Lili Xu, Xiaoxiao Zhang, Jiahui Zhang, Xin Bai, Ru Jin, Zhengyu Jin & Hao Sun

  2. Deepwise AI Lab, Deepwise Healthcare, Beijing, China

    Li Mao, Xin Xiao & Xiuli Li

  3. Department of Urology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Yi Xie

  4. National Center for Quality Control of Radiology, Beijing, China

    Zhengyu Jin & Hao Sun

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  1. Li Chen
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Contributions

LC, GZ, YX, ZJ and HS: guarantor of integrity of the entire study, GZ: study concepts and design, LC: literature research, LX, XZ, and JZ: clinical studies, JZ, XB, and RJ: experimental studies/data analysis, LM, XL, and XX: statistical analysis, LC and GZ: manuscript preparation, YX, ZJ, and HS: manuscript editing.

Corresponding authors

Correspondence to Yi Xie, Zhengyu Jin or Hao Sun.

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Conflict of interest

The authors of this manuscript declare relationships with the following company: Deepwise Healthcare. Li Mao, Xin Xiao, and Xiuli Li are employees of Deepwise Healthcare. The remaining authors declare they have no competing interests.

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Chen, L., Zhang, G., Xu, L. et al. Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer. Abdom Radiol 48, 659–668 (2023). https://doi.org/10.1007/s00261-022-03730-y

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