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Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study.
Sun, Di; Hadjiiski, Lubomir; Alva, Ajjai; Zakharia, Yousef; Joshi, Monika; Chan, Heang-Ping; Garje, Rohan; Pomerantz, Lauren; Elhag, Dean; Cohan, Richard H; Caoili, Elaine M; Kerr, Wesley T; Cha, Kenny H; Kirova-Nedyalkova, Galina; Davenport, Matthew S; Shankar, Prasad R; Francis, Isaac R; Shampain, Kimberly; Meyer, Nathaniel; Barkmeier, Daniel; Woolen, Sean; Palmbos, Phillip L; Weizer, Alon Z; Samala, Ravi K; Zhou, Chuan; Matuszak, Martha.
Afiliação
  • Sun D; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Hadjiiski L; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Alva A; Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Zakharia Y; Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA.
  • Joshi M; Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA.
  • Chan HP; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Garje R; Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA.
  • Pomerantz L; Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA.
  • Elhag D; Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA.
  • Cohan RH; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Caoili EM; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Kerr WT; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Cha KH; U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993, USA.
  • Kirova-Nedyalkova G; Department of Radiology, Acibadem City Clinic, Tokuda Hospital, 1407 Sofia, Bulgaria.
  • Davenport MS; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Shankar PR; Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Francis IR; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Shampain K; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Meyer N; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Barkmeier D; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Woolen S; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Palmbos PL; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Weizer AZ; Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Samala RK; Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Zhou C; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Matuszak M; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Tomography ; 8(2): 644-656, 2022 03 02.
Article em En | MEDLINE | ID: mdl-35314631
ABSTRACT
This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians' diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers' clinical experience, institution affiliation, specialty, and the assessment times on the observers' diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved (p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers' performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Sistemas de Apoio a Decisões Clínicas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
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Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Bexiga Urinária / Sistemas de Apoio a Decisões Clínicas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos