ARTICLE
April 6, 2018

Artificial Intelligence and Cancer Care

 

Artificial Intelligence (AI) is the biggest thing on the horizon for diagnostic imaging and radiation oncology. It could change the way cancer is detected, diagnosed, and treated. It may even restructure how insurers reimburse treatment centers.

But before we tackle this topic we need to understand how this new AI software is different from the old.

AI: Not Your Dad’s CAD

Most medical professionals are familiar with CAD - computer assisted diagnosis. This software program was launched in the late 90’s. The “machine learning” based software sifted through data sets that provided an example and an answer. It left some of its users disappointed.

Ajay Kohli, MD, and Saurabh Jha, MD, explain why they think CAD failed them in mammography. “[The] computer is trained on samples with known pathology (truth) and then tested for its ability to predict the likelihood of malignancies in a test sample (truth and lies). Despite the allure of [machine learning], the pedagogy is not neutral. Because the computer sees more cancers during its training than its test, there is verification bias, and the specificity drifts.”

CAD relied on predetermined answers to learn and develop an algorithm. Today’s AI (often referred to as CAD 2.0) does not. The software is based on “deep learning” algorithms.

Paul Chang, PhD, explains the distinction in Radiology Today. “[The] major difference is that we don’t have to calculate the important features in the examples that it should use for making decisions. AI can identify patterns in images with high reliability and may find patterns that are more complex than humans can find.”

Geneva University Hospitals in Switzerland found that their AI software was able to distinguish lung nodules just by referencing patient images with “[an] algorithm that considers textural image features” as well as demographics and other clinical data.

In the Coming Age of AI, Data is King

Jordan Johnson is the Director of Compliance at Churchill Consulting, a division of CruxQS™,  and former Director of Radiation Oncology at Thompson Cancer Survival Center Cumberland Medical. He believes that the future of radiation oncology lies not in the new equipment but in the wealth of patient images, clinical data, and test results that physicians have at their fingertips.

“We’ve been sitting on all this data from CT, MRI, PET scans, pathology, etc. We have hundreds of years’ worth of data, but we really haven’t done anything with it.”

sea of data imageIt’s easy to understand why: it’s a lot of data for a human mind to organize and interpret.

In his article, How Artificial Intelligence Will Change Medical Imaging, Dave Fornell likens reading this sea of information to 'drinking from a firehose.'  However, AI can sift through medical records and present the information in a concise, digestible format.

“That’s how artificial intelligence can change things,” says Jordan. “AI gives us the ability to extract that information.”

AI’s Impact on Patient Care

According to Jordan, the information AI extracts makes diagnosis and detection relatively predictable. “What we’re finding with patients is that they aren’t really all that different. We can see patterns and how we can positively affect their treatment. Just look at radiomics and Moffit Cancer Center. Radiomics will be the biggest advancement in radiation oncology and radiology.”

Radiomics is a form of AI and is described as “a field of medical study that aims to extract large amount of quantitative features from medical images using data-characterization algorithms.”

Robert Gillies, Ph.D., chair of Moffitt’s Department of Cancer Imaging and Metabolism explains his work (published in eLife). “The core belief of radiomics is that images aren’t pictures, they’re data. We have to treat them as data. Right now, we extract about 1300 different quantitative features from any volume of interest.”

Inputting this data in their radiomic algorithm allows scientists to “quantitate different features of tumors, such as intensity, shape, size and texture.” Combined with other crucial clinical information they can “predict active biological pathways, clinical outcomes, and potential effective therapies.”

How All This Data and AI will Affect Healthcare

AI’s ability to determine a treatment plan and set a standard deviation has a broad appeal to insurers and government programs. There are some initiatives being enforced with the hopes of lowering healthcare cost while optimizing patient outcomes.

For example the Centers for Medicare & Medicaid (CMS) are driving physicians to participate in their Appropriate Use Criteria (AUC). The program’s goal is to “promote the use of [AUC] in advanced diagnostic imaging services” and assist medical professionals in making “the most appropriate treatment decisions for specific clinical conditions.”

Jordan expounds on how he thinks this program will be utilized to standardize care and lower costs. AI & Healthcare

“Physicians will be required to report every exam they do, and CMS is going to collect that data. It’ll be a database of codes, treatments, exams, and diagnosis. Based on that information they will know what the most common treatment is, what is the overall standard deviation curve should look like. With this information they can determine what they should pay for the optimal patient outcome.”

Other industry experts are anticipating a change in reimbursements. Fornell predicts that we will see a trend from “fee-for-service” to “fee-for-outcomes.” Studies show that standardized care and AUC can provide a cost savings to the healthcare industry. It won’t be long until insurers adopt the same model.

 

Investments Shifting to Software

Cancer centers might be getting a lot more familiar with our software application specialist. More big companies are getting into the software business. Varian’s recent acquisition of Mobius Medical Systems may be their entrance into the AI software arena and Philips is already touting its Illumeo software as providing “contextual relevance” and “adaptive intelligence.”

Jordan predicts that the move into the software market will only grow in the next five years. “There’s just not as much money in capital equipment anymore. Facilities will probably stop replacing their linacs every few years. They’ll start utilizing more of the software to make their current equipment more effective.”

It’s hard to tell how much of the industry will be impacted by artificial intelligence and how soon. However, it’s clear to see that change is coming very soon and the future is closer than we think.

 

Related Articles: 

Unplanned Breaks in Radiation Treatment Heighten Risk of Cancer Recurrence

The Four Essential Prongs of a Cancer Survivorship Program

How Linear Markers used in Radiation Oncology Treatment Planning can Affect Accuracy and Efficacy

 
 
Jonathan McCullough
Product Manager
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