Software provides tool to catch cancer on digital pathology slides
Software developed by researchers at the University of Michigan (UM) in Ann Arbor was able to separate malignancy from background tissue on digital pathology slides, according to a paper published in the January issue of Analytical Cellular Pathology.
The program, Spatially Invariant Vector Quantization (SIVQ), was able to identify malignancy in digital slides of micropapillary urothelial carcinoma, a type of bladder cancer whose features can vary widely from case to case and present diagnostic challenges.
“Being able to pick out cancer from background tissue is a key test for this type of software tool,” said UM informatics fellow Jason Hipp, MD, PhD, in a statement. “This is the type of validation that has to happen before digital pathology tools can be widely used in a clinical setting.”
A group of human pathologists first pinpointed the cancer on the slides by hand, the results of which became the model for grading SIVQ’s results. Researchers then systematically tested which settings within the program produced the most accurate results–which can serve as a blueprint for optimizing the software to detect other types of cancer and disease.
SIVQ is different from other pattern recognition software in that it bases matches on a set on concentric rings rather than square blocks. This allows for features to be identified no matter how they’re rotated or if they’re flipped.
When diagnosing from tissue slides, different pathologists–or even the same pathologist at different times–may come to different conclusions based on a number of factors, including whether a slide is viewed at high or low magnification, or whether the pathologist is fatigued from examining dozens of other slides that day, according to the researchers.
Still, the authors stressed the program isn’t intended to replace the skill and art of human pathologists, but to provide an additional resource.
“Not only do our findings show that SIVQ has the potential to be a useful tool in surgical pathologists’ toolkits when optimized to aid detection of such a highly variable disease, but the case is an excellent example for how the same approach might be applied to a variety of clinical areas,” said Ulysses Balis, MD, director of the division of pathology informatics at UM, in a statement.
Since the computer-aided analysis of micropapillary urothelial carcinoma might contribute to patient care, the group is making all of its primary data freely available to other physicians and researchers at UM’s online digital imaging repository.
To visit the repository, click here.
The program, Spatially Invariant Vector Quantization (SIVQ), was able to identify malignancy in digital slides of micropapillary urothelial carcinoma, a type of bladder cancer whose features can vary widely from case to case and present diagnostic challenges.
“Being able to pick out cancer from background tissue is a key test for this type of software tool,” said UM informatics fellow Jason Hipp, MD, PhD, in a statement. “This is the type of validation that has to happen before digital pathology tools can be widely used in a clinical setting.”
A group of human pathologists first pinpointed the cancer on the slides by hand, the results of which became the model for grading SIVQ’s results. Researchers then systematically tested which settings within the program produced the most accurate results–which can serve as a blueprint for optimizing the software to detect other types of cancer and disease.
SIVQ is different from other pattern recognition software in that it bases matches on a set on concentric rings rather than square blocks. This allows for features to be identified no matter how they’re rotated or if they’re flipped.
When diagnosing from tissue slides, different pathologists–or even the same pathologist at different times–may come to different conclusions based on a number of factors, including whether a slide is viewed at high or low magnification, or whether the pathologist is fatigued from examining dozens of other slides that day, according to the researchers.
Still, the authors stressed the program isn’t intended to replace the skill and art of human pathologists, but to provide an additional resource.
“Not only do our findings show that SIVQ has the potential to be a useful tool in surgical pathologists’ toolkits when optimized to aid detection of such a highly variable disease, but the case is an excellent example for how the same approach might be applied to a variety of clinical areas,” said Ulysses Balis, MD, director of the division of pathology informatics at UM, in a statement.
Since the computer-aided analysis of micropapillary urothelial carcinoma might contribute to patient care, the group is making all of its primary data freely available to other physicians and researchers at UM’s online digital imaging repository.
To visit the repository, click here.
Evan Godt, Writer
Evan joined TriMed in 2011, writing primarily for Health Imaging. Prior to diving into medical journalism, Evan worked for the Nine Network of Public Media in St. Louis. He also has worked in public relations and education. Evan studied journalism at the University of Missouri, with an emphasis on broadcast media.