Next Article in Journal
Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge
Previous Article in Journal
A Fully Automated Deep Learning Network for Brain Tumor Segmentation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tomography
Volume 6
Issue 2
10.18383/j.tom.2020.00013
tomography-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support

by
Lubomir M. Hadjiiski
1,*,
Kenny H. Cha
1,
Richard H. Cohan
1,
Heang-Ping Chan
1,
Elaine M. Caoili
1,
Matthew S. Davenport
1,2,
Ravi K. Samala
1,
Alon Z. Weizer
2,
Ajjai Alva
3,
Galina Kirova-Nedyalkova
4,
Kimberly Shampain
1,
Nathaniel Meyer
1,
Daniel Barkmeier
1,
Sean A Woolen
5,
Prasad R. Shankar
1,
Isaac R. Francis
1 and
Phillip L. Palmbos
3
1
Department of Radiology, University of Michigan, 1500 E. Medical Center Drive, MIB C476, Ann Arbor, MI 48109-5842, USA
2
Departments of Urology, Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA
3
Internal Medicine, Division of Hematology-Oncology, University of Michigan, Ann Arbor, MI, USA
4
Department of Radiology, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria
5
Department of Radiology, University of California, San Francisco, Medical Center, San Francisco, CA, USA
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 194-202; https://doi.org/10.18383/j.tom.2020.00013
Submission received: 8 March 2020 / Revised: 2 April 2020 / Accepted: 4 May 2020 / Published: 1 June 2020
Versions Notes

Abstract

We evaluated the intraobserver variability of physicians aided by a computerized decision-support system for treatment response assessment (CDSS-T) to identify patients who show complete response to neoadjuvant chemotherapy for bladder cancer, and the effects of the intraobserver variability on physicians' assessment accuracy. A CDSS-T tool was developed that uses a combination of deep learning neural network and radiomic features from computed tomography (CT) scans to detect bladder cancers that have fully responded to neoadjuvant treatment. Pre- and postchemotherapy CT scans of 157 bladder cancers from 123 patients were collected. In a multireader, multicase observer study, physician-observers estimated the likelihood of pathologic T0 disease by viewing paired pre/posttreatment CT scans placed side by side on an in-house-developed graphical user interface. Five abdominal radiologists, 4 diagnostic radiology residents, 2 oncologists, and 1 urologist participated as observers. They first provided an estimate without CDSS-T and then with CDSS-T. A subset of cases was evaluated twice to study the intraobserver variability and its effects on observer consistency. The mean areas under the curves for assessment of pathologic T0 disease were 0.85 for CDSS-T alone, 0.76 for physicians without CDSS-T and improved to 0.80 for physicians with CDSS-T (P = .001) in the original evaluation, and 0.78 for physicians without CDSS-T and improved to 0.81 for physicians with CDSS-T (P = .010) in the repeated evaluation. The intraobserver variability was significantly reduced with CDSS-T (P < .0001). The CDSS-T can significantly reduce physicians' variability and improve their accuracy for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.
Keywords: bladder cancer; treatment response assessment; intraobserver variability; radiomics; observer performance study; decision support systems bladder cancer; treatment response assessment; intraobserver variability; radiomics; observer performance study; decision support systems

Share and Cite

MDPI and ACS Style

Hadjiiski, L.M.; Cha, K.H.; Cohan, R.H.; Chan, H.-P.; Caoili, E.M.; Davenport, M.S.; Samala, R.K.; Weizer, A.Z.; Alva, A.; Kirova-Nedyalkova, G.; et al. Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support. Tomography 2020, 6, 194-202. https://doi.org/10.18383/j.tom.2020.00013

AMA Style

Hadjiiski LM, Cha KH, Cohan RH, Chan H-P, Caoili EM, Davenport MS, Samala RK, Weizer AZ, Alva A, Kirova-Nedyalkova G, et al. Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support. Tomography. 2020; 6(2):194-202. https://doi.org/10.18383/j.tom.2020.00013

Chicago/Turabian Style

Hadjiiski, Lubomir M., Kenny H. Cha, Richard H. Cohan, Heang-Ping Chan, Elaine M. Caoili, Matthew S. Davenport, Ravi K. Samala, Alon Z. Weizer, Ajjai Alva, Galina Kirova-Nedyalkova, and et al. 2020. "Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support" Tomography 6, no. 2: 194-202. https://doi.org/10.18383/j.tom.2020.00013

APA Style

Hadjiiski, L. M., Cha, K. H., Cohan, R. H., Chan, H. -P., Caoili, E. M., Davenport, M. S., Samala, R. K., Weizer, A. Z., Alva, A., Kirova-Nedyalkova, G., Shampain, K., Meyer, N., Barkmeier, D., A Woolen, S., Shankar, P. R., Francis, I. R., & Palmbos, P. L. (2020). Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support. Tomography, 6(2), 194-202. https://doi.org/10.18383/j.tom.2020.00013

Article Metrics

Back to TopTop