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Volume 12 Issue 5
Sep.  2021
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ZHANG Gumuyang, XU Lili, MAO Li, LI Xiuli, JIN Zhengyu, SUN Hao. CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 698-704. DOI: 10.12290/xhyxzz.2021-0511
Citation: ZHANG Gumuyang, XU Lili, MAO Li, LI Xiuli, JIN Zhengyu, SUN Hao. CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 698-704. DOI: 10.12290/xhyxzz.2021-0511
shu

CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study

  • 1.

    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

  • 2.

    AI Lab, Deepwise Healthcare, Beijing 100080, China

Funds: 

National Natural Science Foundation of China 8190742

More Information
  • Corresponding author:

    SUN Hao  Tel: 86-10-69154597, E-mail: sunhao_robert@126.com

  • Received Date: June 30, 2021
  • Accepted Date: August 04, 2021
  • Issue Publish Date: September 29, 2021
    Graphical Abstract
    • Abstract
  •   Objective  To investigate the feasibility of the CT-based radiomics model to predict the recurrence of bladder cancer in one year postoperatively.
      Methods  Patients with bladder cancer that received surgical treatment in Peking Union Medical College Hospital from May 2014 to July 2018 were retrospectively enrolled and followed up the recurrence of the disease. Nephrographic phase images of preoperative CT urography(CTU) performed in our hospital were collected. The images were filtered before radiomic feature extraction, and JMIM was used to identify the best radiomic features related to recurrence of bladder cancer. Random forest, AdaBoost, gradient boosting, and their combined model were used to build the model for predicting recurrence of bladder cancer after resection in one year. We applied 10-fold cross validation to validate each model and performed receiver operator characteristic curves to analyze the performance of each model.
      Results  A total of 228 cases were included in this study according to inclusion and exclusion criteria. Fifty-one patients had recurrence and the rest 177 patients had no recurrence in one year during postoperative follow-up. In the cross validation, the random forest model, AdaBoost model, gradient boosting model and combined model predicted the recurrence of bladder cancer with AUC of 0.729(95% CI: 0.649-0.809), 0.710(95% CI: 0.627-0.793), 0.709(95% CI: 0.624-0.793)and 0.732(95% CI: 0.651-0.812), accuracy of 76.8%(95% CI: 70.6%-82.0%), 73.7%(95% CI: 67.4%-79.2%), 61.8%(95% CI: 54.7%-67.7%)and 75.0%(95% CI: 68.8%-80.4%), sensitivity of 52.9%(95% CI: 38.6%-66.8%), 62.7%(95% CI: 48.1%-75.5%), 80.4%(95% CI: 64.3%-88.2%)and 58.8%(95% CI: 44.2%-72.1%), specificity of 83.6%(95% CI: 77.1%-88.6%), 76.8%(95% CI: 69.8%-82.7%), 56.5%(95% CI: 48.9%-63.9%)and 79.7%(95% CI: 72.8%-85.2%), respectively.
      Conclusion  Integration of CT-based radiomics prediction models can predict the recurrence risk of bladder cancer in one year postoperatively.
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    • References (18)
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