How using data science and big data can diagnose cancer

1. Cancer – Your Data May Save You
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In recent work, WPC Healthcare used an approach to diagnosing omentum cancer that yielded
a 93.4% area–under-the-curve using only 12 biomarkers of the over 50,000 provided, creating a
32.9% improvement. In an era of big data, being able to use smarter modeling approaches may
lead to faster predictions and better accuracy. We believe the WPC Healthcare approach could
significantly improve detection for cancers in the areas of breast, colon, endometrium, kidney,
lung, ovary, prostrate, and uterus.
There is much hype around data science capabilities and one of the most positive impacts it is
having is around the area of cancer diagnosis. According to the CDC, cancer is the second
leading cause of death in 2016 making the early diagnosis and treatment a priority when it
comes to saving lives and improving quality of life for many.
Omentum cancer is the 5th leading cause of cancer. Often difficult to diagnose until advanced
stages, omentum cancer is treatable if detected early and is commonly linked to ovarian cancer,
which generally has a 45 percent survival rate after 5 years, according to the American Cancer
Society. In 80 percent of ovarian cancer patients the disease has spread to the omentum before
the condition is diagnosed.
The omentum is a pad of fat cells that covers the stomach and intestines. Ovarian cancer tends
to spread to the omentum quickly. When ovarian cancer has spread to the abdomen, it is
considered the beginning of Stage III, according to the American Cancer Society. Known as a
silent killer, ovarian cancer forms tumors that can easily spread quietly throughout the body.
This is because its symptoms often mimic those of other diseases or do not present themselves
until after the cancer spreads. This is why early detection is key for this form of cancer but
diagnosing is difficult.

2. The use of biomarkers in clinical research as well as clinical practice has become so
commonplace that their use in clinical trials is now accepted almost without
question. Biomarkers have been studied for a long time and repeatedly shown to correctly
predict clinical outcomes across a variety of treatments and populations. As an example, a
research group reduced down the hundreds of thousands of biomarkers to just over 50,000
that had the most variation. Even with this reduction, the current state-of-the-art algorithm for
omentum cancer detection achieved 70.3% area-under-the-curve (variation on accuracy). WPC
Healthcare was able to significantly improve that benchmark - check out this link.
In an era of big data, being able to use smarter modeling approaches may lead to faster
predictions and better accuracy. For the most part, medicine has gone from “super tiny
datasets” to “really large datasets” overnight. In many cases, more data most likely means
more noise, weakening the signal. Not developing adequate strategies to amplify the signal and
decreasing the noise, generally leads to the models having too much of the wrong data and
produces outcomes with lower than expected prediction quality. It is important to note, that
WPC Healthcare routinely beats start-of-the-art benchmark in many areas of healthcare
(clinical, financial, and operational). While we thoroughly enjoy developing innovative solutions
to our most difficult healthcare problems in the modern era, we spend a significant amount of
time evangelizing experts of healthcare to the power of “well developed Data Science” and the
many benefits it has to offer, both to patients and providers. – Damian Mingle Chief Data
Scientist WPC Healthcare
Cutting through the noise to find the most meaningful data leads to better outcomes for
patients and provider. Where are you settling for less than stellar results? How do you use data
to solve complex problems? Where are you struggling to cut through the noise? We would love
to hear your thoughts or questions.
Ruth Smith Director of Business Development WPC Healthcare 615-594-9130
rsmith@wpchealthcare.com #data #cancer #analytics #healthcare #bigdata