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 BarkmeierSean Woolen, Phillip L. Palmbos, Alon Z. Weizer, Ravi K. Samala, Chuan Zhou, Martha Matuszak

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)644-656
Number of pages13
JournalTomography
Volume8
Issue number2
DOIs
StatePublished - Apr 2022

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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