Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

Journal: Tomography (Ann Arbor, Mich.)
PMID: 30854458

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.

Authors

  • Eric Wu
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Lubomir M Hadjiiski
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Ravi K Samala
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Heang-Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Kenny H Cha
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Caleb Richter
  • Richard H Cohan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Elaine M Caoili
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Chintana Paramagul
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Ajjai Alva
    Department of Internal Medicine, Hematology-Oncology, University of Michigan, Ann Arbor, Michigan.
  • Alon Z Weizer
    Department of Urology, Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan.

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