Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study
Di Sun,
Lubomir Hadjiiski,
Ajjai Alva,
Yousef Zakharia,
Monika Joshi,
Heang-Ping Chan,
Rohan Garje,
Lauren Pomerantz,
Dean Elhag,
Richard H. Cohan,
Elaine M. Caoili,
Wesley T. Kerr,
Kenny H. Cha,
Galina Kirova-Nedyalkova,
Matthew S. Davenport,
Prasad R. Shankar,
Isaac R. Francis,
Kimberly Shampain,
Nathaniel Meyer,
Daniel Barkmeier,
Sean Woolen,
Phillip L. Palmbos,
Alon Z. Weizer,
Ravi K. Samala,
Chuan Zhou,
Martha Matuszak
Affiliations
Di Sun
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Lubomir Hadjiiski
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Ajjai Alva
Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA
Yousef Zakharia
Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA
Monika Joshi
Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA
Heang-Ping Chan
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Rohan Garje
Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA
Lauren Pomerantz
Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA
Dean Elhag
Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA
Richard H. Cohan
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Elaine M. Caoili
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Wesley T. Kerr
Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
Kenny H. Cha
U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993, USA
Galina Kirova-Nedyalkova
Department of Radiology, Acibadem City Clinic, Tokuda Hospital, 1407 Sofia, Bulgaria
Matthew S. Davenport
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Prasad R. Shankar
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Isaac R. Francis
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Kimberly Shampain
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Nathaniel Meyer
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Daniel Barkmeier
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Sean Woolen
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Phillip L. Palmbos
Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA
Alon Z. Weizer
Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA
Ravi K. Samala
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Chuan Zhou
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
Martha Matuszak
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
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.
