title: Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.
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.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Kenny H
authors.initials: KH
authors.lastname: Cha
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Ravi K
authors.initials: RK
authors.lastname: Samala
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Richard H
authors.initials: RH
authors.lastname: Cohan
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Chintana
authors.initials: C
authors.lastname: Paramagul
authors.affiliation: Department of Internal Medicine, Hematology-Oncology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Ajjai
authors.initials: A
authors.lastname: Alva
authors.affiliation: Department of Urology, Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan.
authors.forename: Alon Z
authors.initials: AZ
authors.lastname: Weizer
keywords: CT; bladder cancer; computer-aided diagnosis; deep-learning; level set; segmentation; treatment response