Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

Wu, Eric; Hadjiiski, Lubomir M; Samala, Ravi K; Chan, Heang-Ping; Cha, Kenny H; Richter, Caleb; Cohan, Richard H; Caoili, Elaine M; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z

Wu, Eric; Hadjiiski, Lubomir M; Samala, Ravi K; Chan, Heang-Ping; Cha, Kenny H; Richter, Caleb; Cohan, Richard H; Caoili, Elaine M; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

Affiliation

Wu E; Departments of Radiology.
Hadjiiski LM; Departments of Radiology.
Samala RK; Departments of Radiology.
Chan HP; Departments of Radiology.
Cha KH; Departments of Radiology.
Richter C; Departments of Radiology.
Cohan RH; Departments of Radiology.
Caoili EM; Departments of Radiology.
Paramagul C; Departments of Radiology.
Alva A; Internal Medicine-Hematology/Oncology, and.
Weizer AZ; Urology, University of Michigan, Ann Arbor, MI.

Tomography ; 5(1): 201-208, 2019 03.

Article in En | MEDLINE | ID: mdl-30854458

Subject(s)

Deep Learning; Urinary Bladder Neoplasms/diagnostic imaging; Urinary Bladder Neoplasms/drug therapy; Antineoplastic Agents/therapeutic use; Cystectomy; Decision Support Systems, Clinical; Drug Monitoring/methods; Humans; Neoadjuvant Therapy/methods; ROC Curve; Radiographic Image Interpretation, Computer-Assisted/methods; Sensitivity and Specificity; Tomography, X-Ray Computed/methods; Transfer, Psychology; Treatment Outcome; Urography/methods

Key words

CT; bladder; deep-learning; segmentation; transfer learning; treatment response

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Full text: 1 Database: MEDLINE Main subject: Urinary Bladder Neoplasms / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Year: 2019 Type: Article

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