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

doi:10.3390/tomography8020054

Tomography (Mar 2022)

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

DOI: https://doi.org/10.3390/tomography8020054
Journal volume & issue: Vol. 8, no. 2
pp. 644 – 656

Abstract

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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.

Published in Tomography

ISSN: 2379-1381 (Print); 2379-139X (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics
Website: https://www.mdpi.com/journal/tomography

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