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Tomography
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Issue 4
10.18383/j.tom.2016.00184
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Article

Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study

by
Kenny H. Cha
1,*,
Lubomir M. Hadjiiski
1,
Ravi K. Samala
1,
Heang-Ping Chan
1,
Richard H. Cohan
1,
Elaine M. Caoili
1,
Chintana Paramagul
1,
Ajjai Alva
2 and
Alon Z. Weizer
3
1
Department of Radiology, University of Michigan, 1500 E. Medical, Center Drive, MIB C474, Ann Arbor, MI 48109-5842, USA
2
Internal Medicine, Hematology-Oncology, University of Michigan, Ann Arbor, MI 48109-5842, USA
3
Urology, Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109-5842, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 421-429; https://doi.org/10.18383/j.tom.2016.00184
Submission received: 3 September 2016 / Revised: 3 October 2016 / Accepted: 9 November 2016 / Published: 1 December 2016
Versions Notes

Abstract

Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response.
Keywords: computer-aided diagnosis; deep-learning; bladder cancer; treatment response; segmentation; level set computer-aided diagnosis; deep-learning; bladder cancer; treatment response; segmentation; level set

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MDPI and ACS Style

Cha, K.H.; Hadjiiski, L.M.; Samala, R.K.; Chan, H.-P.; Cohan, R.H.; Caoili, E.M.; Paramagul, C.; Alva, A.; Weizer, A.Z. Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study. Tomography 2016, 2, 421-429. https://doi.org/10.18383/j.tom.2016.00184

AMA Style

Cha KH, Hadjiiski LM, Samala RK, Chan H-P, Cohan RH, Caoili EM, Paramagul C, Alva A, Weizer AZ. Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study. Tomography. 2016; 2(4):421-429. https://doi.org/10.18383/j.tom.2016.00184

Chicago/Turabian Style

Cha, Kenny H., Lubomir M. Hadjiiski, Ravi K. Samala, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Chintana Paramagul, Ajjai Alva, and Alon Z. Weizer. 2016. "Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study" Tomography 2, no. 4: 421-429. https://doi.org/10.18383/j.tom.2016.00184

APA Style

Cha, K. H., Hadjiiski, L. M., Samala, R. K., Chan, H. -P., Cohan, R. H., Caoili, E. M., Paramagul, C., Alva, A., & Weizer, A. Z. (2016). Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study. Tomography, 2(4), 421-429. https://doi.org/10.18383/j.tom.2016.00184

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