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

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

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11 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 language	English (US)
Pages (from-to)	644-656
Number of pages	13
Journal	Tomography
Volume	8
Issue number	2
DOIs	https://doi.org/10.3390/tomography8020054
State	Published - Apr 2022

All Science Journal Classification (ASJC) codes

Radiology Nuclear Medicine and imaging

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Sun, D., Hadjiiski, L., Alva, A., Zakharia, Y., Joshi, M., Chan, H. P., Garje, R., Pomerantz, L., Elhag, D., Cohan, R. H., Caoili, E. M., Kerr, W. T., Cha, K. H., Kirova-Nedyalkova, G., Davenport, M. S., Shankar, P. R., Francis, I. R., Shampain, K., Meyer, N., ... Matuszak, M. (2022). Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study. Tomography, 8(2), 644-656. https://doi.org/10.3390/tomography8020054

@article{11023f124930432e98c764f254c46e13,

title = "Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study",

abstract = "This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians{\textquoteright} 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{\textquoteright} clinical experience, institution affiliation, specialty, and the assessment times on the observers{\textquoteright} 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{\textquoteright} 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.",

author = "Di Sun and Lubomir Hadjiiski and Ajjai Alva and Yousef Zakharia and Monika Joshi and Chan, {Heang Ping} and Rohan Garje and Lauren Pomerantz and Dean Elhag and Cohan, {Richard H.} and Caoili, {Elaine M.} and Kerr, {Wesley T.} and Cha, {Kenny H.} and Galina Kirova-Nedyalkova and Davenport, {Matthew S.} and Shankar, {Prasad R.} and Francis, {Isaac R.} and Kimberly Shampain and Nathaniel Meyer and Daniel Barkmeier and Sean Woolen and Palmbos, {Phillip L.} and Weizer, {Alon Z.} and Samala, {Ravi K.} and Chuan Zhou and Martha Matuszak",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = apr,

doi = "10.3390/tomography8020054",

language = "English (US)",

volume = "8",

pages = "644--656",

journal = "Tomography",

issn = "2379-1381",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "2",

}

Sun, D, Hadjiiski, L, Alva, A, Zakharia, Y, Joshi, M, Chan, HP, Garje, R, Pomerantz, L, Elhag, D, Cohan, RH, Caoili, EM, Kerr, WT, Cha, KH, Kirova-Nedyalkova, G, Davenport, MS, Shankar, PR, Francis, IR, Shampain, K, Meyer, N, Barkmeier, D, Woolen, S, Palmbos, PL, Weizer, AZ, Samala, RK, Zhou, C & Matuszak, M 2022, 'Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study', Tomography, vol. 8, no. 2, pp. 644-656. https://doi.org/10.3390/tomography8020054

TY - JOUR

T1 - Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography

T2 - Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

AU - Sun, Di

AU - Hadjiiski, Lubomir

AU - Alva, Ajjai

AU - Zakharia, Yousef

AU - Joshi, Monika

AU - Chan, Heang Ping

AU - Garje, Rohan

AU - Pomerantz, Lauren

AU - Elhag, Dean

AU - Cohan, Richard H.

AU - Caoili, Elaine M.

AU - Kerr, Wesley T.

AU - Cha, Kenny H.

AU - Kirova-Nedyalkova, Galina

AU - Davenport, Matthew S.

AU - Shankar, Prasad R.

AU - Francis, Isaac R.

AU - Shampain, Kimberly

AU - Meyer, Nathaniel

AU - Barkmeier, Daniel

AU - Woolen, Sean

AU - Palmbos, Phillip L.

AU - Weizer, Alon Z.

AU - Samala, Ravi K.

AU - Zhou, Chuan

AU - Matuszak, Martha

PY - 2022/4

Y1 - 2022/4

N2 - 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.

AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85125961149&partnerID=8YFLogxK

U2 - 10.3390/tomography8020054

DO - 10.3390/tomography8020054

M3 - Article

C2 - 35314631

AN - SCOPUS:85125961149

SN - 2379-1381

VL - 8

SP - 644

EP - 656

JO - Tomography

JF - Tomography

IS - 2

ER -

Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

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