Artificial intelligence to detect colorectal cancer

Date:
November 2, 2021
Source:
Tulane University
Summary:
A researcher found that artificial intelligence can accurately
detect and diagnose colorectal cancer from tissue scans as well
or better than pathologists, according to a new study.



FULL STORY ==========================================================================
A Tulane University researcher found that artificial intelligence can accurately detect and diagnose colorectal cancer from tissue scans as
well or better than pathologists, according to a new study in the journal Nature Communications.


==========================================================================
The study, which was conducted by researchers from Tulane, Central South University in China, the University of Oklahoma Health Sciences Center,
Temple University, and Florida State University, was designed to test
whether AI could be a tool to help pathologists keep pace with the rising demand for their services.

Pathologists evaluate and label thousands of histopathology images on
a regular basis to tell whether someone has cancer. But their average
workload has increased significantly and can sometimes cause unintended misdiagnoses due to fatigue.

"Even though a lot of their work is repetitive, most pathologists
are extremely busy because there's a huge demand for what they do but
there's a global shortage of qualified pathologists, especially in many developing countries" said Dr. Hong-Wen Deng, professor and director
of the Tulane Center of Biomedical Informatics and Genomics at Tulane University School of Medicine.

"This study is revolutionary because we successfully leveraged
artificial intelligence to identify and diagnose colorectal cancer
in a cost-effective way, which could ultimately reduce the workload
of pathologists." To conduct the study, Deng and his team collected
over 13,000 images of colorectal cancer from 8,803 subjects and 13
independent cancer centers in China, Germany and the United States. Using
the images, which were randomly selected by technicians, they built a
machine assisted pathological recognition program that allows a computer
to recognize images that show colorectal cancer, one of the most common
causes of cancer related deaths in Europe and America.

"The challenges of this study stemmed from complex large image sizes,
complex shapes, textures, and histological changes in nuclear staining,"
Deng said.

"But ultimately the study revealed that when we used AI to diagnose
colorectal cancer, the performance is shown comparable to and even
better in many cases than real pathologists." The area under the
receiver operating characteristic (ROC) curve or AUC is the performance measurement tool that Deng and his team used to determine the success of
the study. After comparing the computer's results with the work of highly experienced pathologists who interpreted data manually, the study found
that the average pathologist scored at .969 for accurately identifying colorectal cancer manually. The average score for the machine-assisted
AI computer program was .98, which is comparable if not more accurate.

Using artificial intelligence to identify cancer is an emerging technology
and hasn't yet been widely accepted. Deng's hope is that the study will
lead to more pathologists using prescreening technology in the future
to make quicker diagnoses.

"It's still in the research phase and we haven't commercialized
it yet because we need to make it more user friendly and test and
implement in more clinical settings. But as we develop it further,
hopefully it can also be used for different types of cancer in
the future. Using AI to diagnose cancer can expedite the whole
process and will save a lot of time for both patients and clinicians." ========================================================================== Story Source: Materials provided by Tulane_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Gang Yu, Kai Sun, Chao Xu, Xing-Hua Shi, Chong Wu, Ting Xie,
Run-Qi Meng,
Xiang-He Meng, Kuan-Song Wang, Hong-Mei Xiao, Hong-Wen
Deng. Accurate recognition of colorectal cancer with semi-supervised
deep learning on pathological images. Nature Communications, 2021;
12 (1) DOI: 10.1038/ s41467-021-26643-8 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211102180535.htm

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