Multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography with and without computerized decision support

Lubomir Hadjiiski, Monika Joshi, Ajjai Alva, Heang Ping Chan, Richard H. Cohan, Elaine M. Caoili, Galina Kirova-Nedyalkova, Matthew S. Davenport, Prasad R. Shankar, Isaac R. Francis, Kenny H. Cha, Ravi K. Samala, Phillip L. Palmbos, Alon Z. Weizer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We evaluated whether a computerized decision support system for bladder cancer treatment response assessment (CDSS-T) can assist physicians from different institutions in identifying patients who have complete response after neoadjuvant chemotherapy. Pre- and post-chemotherapy CTU scans of 96 patients (114 pre- and post-treatment lesion pairs) were collected retrospectively. The pathological cancer stage after treatment was collected as the reference standard of response to treatment. 24% of the lesion pairs had T0 cancer stage (complete response) after chemotherapy. Our CDSST that combined DL-CNN and radiomics features was trained to distinguish between T0 and <T0 cases. Five abdominal radiologists and 3 oncologists participated in the observer study. One radiologist and one oncologist were from external institutions. All physicians estimated the likelihood of stage T0 disease after treatment by viewing each pre-post-treatment CTU pair displayed side by side on a specialized graphical user interface. The observer provided an estimate without CDSS-T first and then might revise the estimate, if preferred, after the CDSS-T score was displayed. The observers' estimates with and without CDSS-T were analyzed with multi-reader, multi-case (MRMC) methodology. The AUC for prediction of T0 disease after treatment was 0.85±0.04 for the CDSS-T alone. The performance of all but one observers increased with the aid of CDSS-T. The average AUC for the observers was 0.77 (range: 0.69-0.83) without CDSS-T, and increased to 0.80 (range: 0.72-0.86), (p = 0.006) with CDSS-T. The CDSS-T could improve the performance of radiologists and oncologists from different institutions in identifying patients who fully responded to treatment. There was no apparent difference in the performance of the physicians from different institutions.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
PublisherSPIE
ISBN (Electronic)9781510640238
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11597
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
CountryUnited States
CityVirtual, Online
Period2/15/212/19/21

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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