Artificial intelligence helps physicians better assess the effectiveness
of bladder cancer treatment
Date:
April 22, 2022
Source:
Michigan Medicine - University of Michigan
Summary:
In a small but multi-institutional study, an artificial
intelligence- based system improved providers' assessments of
whether patients with bladder cancer had complete response to
chemotherapy before a radical cystectomy (bladder removal surgery).
FULL STORY ==========================================================================
In a small but multi-institutional study, an artificial intelligence-based system improved providers' assessments of whether patients with bladder
cancer had complete response to chemotherapy before a radical cystectomy (bladder removal surgery).
==========================================================================
Yet the researchers caution that AI isn't a replacement for human
expertise and that their tool shouldn't be used as such.
"If you use the tool smartly, it can help you," said Lubomir Hadjiyski,
Ph.D., a professor of radiology at the University of Michigan Medical
School and the senior author of the study.
When patients develop bladder cancer, surgeons often remove the entire
bladder in an effort to keep the cancer from returning or spreading to
other organs or areas. More evidence is building, though, that surgery
may not be necessary if a patient has zero evidence of disease after chemotherapy.
However, it's difficult to determine whether the lesion left after
treatment is simply tissue that's become necrotic or scarred as a result
of treatment or whether cancer remains. The researchers wondered if AI
could help.
"The big question was when you have such an artificial device next to you,
how is it going to affect the physician?" Hadjiyski said. "Is it going to
help? Is it going to confuse them? Is it going to raise their performance
or will they simply ignore it?" Fourteen physicians from different
specialties -- including radiology, urology and oncology -- as well as
two fellows and a medical student looked at pre- and post-treatment scans
of 157 bladder tumors. The providers gave ratings for three measures that assessed the level of response to chemotherapy as well as a recommendation
for the next treatment to be done for each patient (radiation or surgery).
==========================================================================
Then the providers looked at a score calculated by the computer. Lower
scores indicated a lower likelihood of complete response to chemo and
vice versa for higher scores. The providers could revise their ratings or
leave them unchanged. Their final ratings were compared against samples of
the tumors taken during their bladder removal surgeries to gauge accuracy.
Across different specialties and experience levels, providers saw
improvements in their assessments with the AI system. Those with less experience had even more gains, so much so that they were able to make diagnoses at the same level as the experienced participants.
"That was the distinct part of that study that showed interesting
observations about the audience," Hadjiyski said.
The tool helped providers from academic institutions more than those
that worked at health centers focused solely on clinical care.
The study is part of an NIH-funded project, led by Hadjiyski and
Ajjai Alva, M.D., an associate professor of internal medicine at U-M,
to develop and evaluate biomarker-based tools for treatment response
decision support of bladder cancer.
Over the course of more than two decades of conducting AI-based studies
to assess different types of cancer and their treatment response,
Hadjiyski says he's observed that machine learning tools can be useful
as a second opinion to assist physicians in decision making, but they
can also make mistakes.
"One interesting thing that we figured out is that the computer makes
mistakes on a different subset of cases than a radiologist would," he
added. "Which means that if the tool is used correctly, it gives a chance
to improve but not replace the physician's judgment." Other authors
include Di Sun, Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine
M. Caoili, Wesley T. Kerr, 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 and Martha Matuszak of U-M; Yousef Zakharia, Rohan Garje and Dean
Elhag of the University of Iowa; Monika Joshi and Lauren Pomerantz of Pennsylvania State University; Kenny H. Cha of the Center for Devices
and Radiological Health at the U.S. Food and Drug Administration and
Galina Kirova-Nedyalkova of the Acibadem City Clinic at Tokuda Hospital
in Sofia, Bulgaria.
========================================================================== Story Source: Materials provided by
Michigan_Medicine_-_University_of_Michigan. Original written by Mary
Clare Fischer. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. 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. Computerized Decision Support for
Bladder Cancer Treatment Response Assessment in CT Urography:
Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty
Study. Tomography, 2022; 8 (2): 644 DOI: 10.3390/tomography8020054 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220422094310.htm
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