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

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 retrospe...

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Autores principales:	ZHANG Gumuyang, XU Lili, MAO Li, LI Xiuli, JIN Zhengyu, SUN Hao
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Medical Journal of Peking Union Medical College Hospital 2021
Materias:	bladder cancer recurrence radiomics prediction model computed tomography Medicine R
Acceso en línea:	https://doaj.org/article/8a0e11efb2914960b0cad678af2810d7
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spelling	oai:doaj.org-article:8a0e11efb2914960b0cad678af2810d72021-12-01T01:42:05ZCT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study1674-908110.12290/xhyxzz.2021-0511https://doaj.org/article/8a0e11efb2914960b0cad678af2810d72021-10-01T00:00:00Zhttps://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2021-0511https://doaj.org/toc/1674-9081Objective 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.ZHANG GumuyangXU LiliMAO LiLI XiuliJIN ZhengyuSUN HaoEditorial Office of Medical Journal of Peking Union Medical College Hospitalarticlebladder cancerrecurrenceradiomicsprediction modelcomputed tomographyMedicineRZHXiehe Yixue Zazhi, Vol 12, Iss 5, Pp 698-704 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	bladder cancer recurrence radiomics prediction model computed tomography Medicine R
spellingShingle	bladder cancer recurrence radiomics prediction model computed tomography Medicine R 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
description	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.
format	article
author	ZHANG Gumuyang XU Lili MAO Li LI Xiuli JIN Zhengyu SUN Hao
author_facet	ZHANG Gumuyang XU Lili MAO Li LI Xiuli JIN Zhengyu SUN Hao
author_sort	ZHANG Gumuyang
title	CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study
title_short	CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study
title_full	CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study
title_fullStr	CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study
title_full_unstemmed	CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study
title_sort	ct-based radiomics to predict recurrence of bladder cancer after resection in one year: a preliminary study
publisher	Editorial Office of Medical Journal of Peking Union Medical College Hospital
publishDate	2021
url	https://doaj.org/article/8a0e11efb2914960b0cad678af2810d7
work_keys_str_mv	AT zhanggumuyang ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy AT xulili ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy AT maoli ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy AT lixiuli ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy AT jinzhengyu ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy AT sunhao ctbasedradiomicstopredictrecurrenceofbladdercancerafterresectioninoneyearapreliminarystudy
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CT-based Radiomics to Predict Recurrence of Bladder Cancer after Resection in One Year: A Preliminary Study

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