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

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

doi: 10.12290/xhyxzz.2021-0511

National Natural Science Foundation of China 8190742

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  • Corresponding author: SUN Hao  Tel: 86-10-69154597, E-mail: sunhao_robert@126.com
  • Received Date: 2021-07-01
  • Accepted Date: 2021-08-05
  • Publish Date: 2021-09-30
  •   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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  • [1] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68: 394-424. doi:  10.3322/caac.21492
    [2] Pang C, Guan Y, Li H, et al. Urologic cancer in China[J]. JPN J Clin Oncol, 2016, 46: 497-501. doi:  10.1093/jjco/hyw034
    [3] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48: 441-446. doi:  10.1016/j.ejca.2011.11.036
    [4] Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges[J]. Magnetic Resonance Imaging, 2012, 30: 1234-1248. doi:  10.1016/j.mri.2012.06.010
    [5] Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantita-tive radiomics approach[J]. Nat Commun, 2014, 5: 4006. doi:  10.1038/ncomms5006
    [6] Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278: 563-577. doi:  10.1148/radiol.2015151169
    [7] Spiess PE, Agarwal N, Bangs R, et al. Bladder Cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology[J]. J Natl Compr Canc Netw, 2017, 15: 1240-1267. doi:  10.6004/jnccn.2017.0156
    [8] Kluth LA, Black PC, Bochner BH, et al. Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature[J]. Eur Urol, 2015, 68: 238-253. doi:  10.1016/j.eururo.2015.01.032
    [9] Fernandez-Gomez J, Madero R, Solsona E, et al. Predicting nonmuscle invasive bladder cancer recurrence and progression in patients treated with bacillus Calmette-Guerin: the CUETO scoring model[J]. J Urol, 2009, 182: 2195-2203. doi:  10.1016/j.juro.2009.07.016
    [10] Sylvester RJ, van der Meijden AP, Oosterlinck W, et al. Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials[J]. Eur Urol, 2006, 49: 466-475. doi:  10.1016/j.eururo.2005.12.031
    [11] Oderda M, Ricceri F, Pisano F, et al. Prognostic factors including Ki-67 and p53 in Bacillus Calmette-Guerin-treated non-muscle-invasive bladder cancer: a prospective study[J]. Urol Int, 2013, 90: 184-190. doi:  10.1159/000343431
    [12] van Kessel KEM, van der Keur KA, Dyrskjøt L, et al. Molecular Markers Increase Precision of the European Associa-tion of Urology Non-Muscle-Invasive Bladder Cancer Progression Risk Groups[J]. Clin Cancer Res, 2018, 24: 1586-1593. doi:  10.1158/1078-0432.CCR-17-2719
    [13] Tilki D, Burger M, Dalbagni G, et al. Urine markers for detection and surveillance of non-muscle-invasive bladder cancer[J]. Euro Urol, 2011, 60: 484-492. doi:  10.1016/j.eururo.2011.05.053
    [14] Ueno Y, Takeuchi M, Tamada T, et al. Diagnostic Accuracy and Interobserver Agreement for the Vesical Imaging-Reporting and Data System for Muscle-invasive Bladder Cancer: A Multireader Validation Study[J]. Eur Urol, 2019, 76: 54-56. doi:  10.1016/j.eururo.2019.03.012
    [15] Wu S, Zheng J, Li Y, et al. A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer[J]. Clin Cancer Res, 2017, 23: 6904-6911. doi:  10.1158/1078-0432.CCR-17-1510
    [16] Zhang G, Xu L, Zhao L, et al. CT-based radiomics to predict the pathological grade of bladder cancer[J]. Eur Radiol, 2020, 30: 6749-6756. doi:  10.1007/s00330-020-06893-8
    [17] Zhang G, Sun H, Shi B, et al. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma[J]. Abdom Radiol (NY), 2017, 42: 561-568. doi:  10.1007/s00261-016-0897-2
    [18] Garapati SS, Hadjiiski L, Cha KH, et al. Urinary bladder cancer staging in CT urography using machine learning[J]. Med Phys, 2017, 44: 5814-5823. doi:  10.1002/mp.12510
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