AI may detect earliest signs of pancreatic cancer

Date:
April 26, 2022
Source:
Cedars-Sinai Medical Center
Summary:
An artificial intelligence (AI) tool can accurately predicted who
would develop pancreatic cancer based on what their CT scan images
looked like years prior to being diagnosed with the disease. The
findings may help prevent death through early detection of one of
the most challenging cancers to treat.



FULL STORY ==========================================================================
An artificial intelligence (AI) tool developed by Cedars-Sinai
investigators accurately predicted who would develop pancreatic cancer
based on what their CT scan images looked like years prior to being
diagnosed with the disease. The findings, which may help prevent death
through early detection of one of the most challenging cancers to treat,
are published in the journal Cancer Biomarkers.


========================================================================== "This AI tool was able to capture and quantify very subtle, early signs
of pancreatic ductal adenocarcinoma in CT scans years before occurrence
of the disease. These are signs that the human eye would never be able to discern," said Debiao Li, PhD, director of the Biomedical Imaging Research Institute, professor of Biomedical Sciences and Imaging at Cedars-Sinai,
and senior and corresponding author of the study. Li is also the Karl
Storz Chair in Minimally Invasive Surgery in Honor of George Berci, MD.

Pancreatic ductal adenocarcinoma is not only the most common type of
pancreatic cancer, but it's also the most deadly. Less than 10% of people diagnosed with the disease live more than five years after being diagnosed
or starting treatment. But recent studies have reported that finding
the cancer early can increase survival rates by as much as 50%. There
currently is no easy way to find pancreatic cancer early, however.

People with this type of cancer may experience symptoms such as general abdominal pain or unexplained weight loss, but these symptoms are often
ignored or overlooked as signs of the cancer since they are common in
many health conditions.

"There are no unique symptoms that can provide an early diagnosis
forpancreatic ductal adenocarcinoma," said Stephen J. Pandol, MD,
director of Basic and Translational Pancreas Research and program
director of the Gastroenterology Fellowship Program at Cedars-Sinai,
and another author of the study. "This AI tool may eventually be used to
detect early disease in people undergoing CT scans for abdominal pain or
other issues." The investigators reviewed electronic medical records
to identify people who were diagnosed with the cancer within the last
15 years and who underwent CT scans six months to three years prior to
their diagnosis. These CT images were considered normal at the time they
were taken. The team identified 36 patients who met these criteria, the majority of whom had CT scans done in the ER because of abdominal pain.

The AI tool was trained to analyze these pre-diagnostic CT images from
people with pancreatic cancer and compare them with CT images from 36
people who didn't develop the cancer. The investigators reported that the
model was 86% accurate in identifying people who would eventually be found
to have pancreatic cancer and those who would not develop the cancer.

The AI model picked up on variations on the surface of the pancreas
between people with cancer and healthy controls. These textural
differences could be the result of molecular changes that occur during
the development of pancreatic cancer.

"Our hope is this tool could catch the cancer early enough to make it
possible for more people to have their tumor completely removed through surgery," said Touseef Ahmad Qureshi, PhD, a scientist at Cedars-Sinai
and first author of the study.

The investigators are currently collecting data from thousands of
patients at healthcare sites throughout the U.S. to continue to study
the AI tool's prediction capability.

Funding: The study was funded by the Board of Counselors of Cedars-Sinai Medical Center, the Cedars-Sinai Samuel Oschin Comprehensive Cancer
Institute and the National Institutes of Health under award number
R01 CA260955.


========================================================================== Story Source: Materials provided by Cedars-Sinai_Medical_Center. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Touseef Ahmad Qureshi, Srinivas Gaddam, Ashley Max Wachsman,
Lixia Wang,
Linda Azab, Vahid Asadpour, Wansu Chen, Yibin Xie, Bechien Wu,
Stephen Jacob Pandol, Debiao Li. Predicting pancreatic ductal
adenocarcinoma using artificial intelligence analysis of
pre-diagnostic computed tomography images. Cancer Biomarkers,
2022; 33 (2): 211 DOI: 10.3233/CBM- 210273 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220426153718.htm

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