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Tomography
Volume 5
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10.18383/j.tom.2018.00036
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Article

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response

by
Eric Wu
1,*,
Lubomir M. Hadjiiski
1,
Ravi K. Samala
1,
Heang-Ping Chan
1,
Kenny H. Cha
1,
Caleb Richter
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 C473, Ann Arbor, MI 48109-5842, USA
2
Department of Internal Medicine-Hematology/Oncology University of Michigan, 1500 E. Medical Center Drive, MIB C473, Ann Arbor, MI 48109-5842, USA
3
Department of Urology, University of Michigan, 1500 E. Medical Center Drive, MIB C473, Ann Arbor, MI 48109-5842, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 201-208; https://doi.org/10.18383/j.tom.2018.00036
Submission received: 10 January 2019 / Revised: 24 January 2019 / Accepted: 20 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment based on transfer learning by freezing different DL-CNN layers and varying the DL-CNN structure. Pre- and posttreatment computed tomography scans of 123 patients (cancers, 129; pre- and posttreatment cancer pairs, 158) undergoing chemotherapy were collected. After chemotherapy 33% of patients had T0 stage cancer (complete response). Regions of interest in pre- and posttreatment scans were extracted from the segmented lesions and combined into hybrid pre -post image pairs (h-ROIs). Training (pairs, 94; h-ROIs, 6209), validation (10 pairs) and test sets (54 pairs) were obtained. The DL-CNN consisted of 2 convolution (C1-C2), 2 locally connected (L3-L4), and 1 fully connected layers. The DL-CNN was trained with h-ROIs to classify cancers as fully responding (stage T0) or not fully responding to chemotherapy. Two radiologists provided lesion likelihood of being stage T0 posttreatment. The test area under the ROC curve (AUC) was 0.73 for T0 prediction by the base DL-CNN structure with randomly initialized weights. The base DL-CNN structure with pretrained weights and transfer learning (no frozen layers) achieved test AUC of 0.79. The test AUCs for 3 modified DL-CNN structures (different C1-C2 max pooling filter sizes, strides, and padding, with transfer learning) were 0.72, 0.86, and 0.69. For the base DL-CNN with (C1) frozen, (C1-C2) frozen, and (C1-C2-L3) frozen, the test AUCs were 0.81, 0.78, and 0.71, respectively. The radiologists' AUCs were 0.76 and 0.77. DL-CNN performed better with pretrained than randomly initialized weights.
Keywords: deep-learning; transfer learning; treatment response; segmentation; bladder deep-learning; transfer learning; treatment response; segmentation; bladder

Share and Cite

MDPI and ACS Style

Wu, E.; Hadjiiski, L.M.; Samala, R.K.; Chan, H.-P.; Cha, K.H.; Richter, C.; Cohan, R.H.; Caoili, E.M.; Paramagul, C.; Alva, A.; et al. Deep Learning Approach for Assessment of Bladder Cancer Treatment Response. Tomography 2019, 5, 201-208. https://doi.org/10.18383/j.tom.2018.00036

AMA Style

Wu E, Hadjiiski LM, Samala RK, Chan H-P, Cha KH, Richter C, Cohan RH, Caoili EM, Paramagul C, Alva A, et al. Deep Learning Approach for Assessment of Bladder Cancer Treatment Response. Tomography. 2019; 5(1):201-208. https://doi.org/10.18383/j.tom.2018.00036

Chicago/Turabian Style

Wu, Eric, Lubomir M. Hadjiiski, Ravi K. Samala, Heang-Ping Chan, Kenny H. Cha, Caleb Richter, Richard H. Cohan, Elaine M. Caoili, Chintana Paramagul, Ajjai Alva, and et al. 2019. "Deep Learning Approach for Assessment of Bladder Cancer Treatment Response" Tomography 5, no. 1: 201-208. https://doi.org/10.18383/j.tom.2018.00036

APA Style

Wu, E., Hadjiiski, L. M., Samala, R. K., Chan, H. -P., Cha, K. H., Richter, C., Cohan, R. H., Caoili, E. M., Paramagul, C., Alva, A., & Weizer, A. Z. (2019). Deep Learning Approach for Assessment of Bladder Cancer Treatment Response. Tomography, 5(1), 201-208. https://doi.org/10.18383/j.tom.2018.00036

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