Study: AI may help in predicting Recurrence of Colorectal Cancer

In a multinational study led by a Mayo Clinic research team using artificial intelligence (AI), investigators developed an algorithm to improve the prediction of colorectal cancer recurrence.
A prognostic model using QuantCRC was developed to predict recurrence-free survival (Unsplash)
A prognostic model using QuantCRC was developed to predict recurrence-free survival (Unsplash)

In a multinational study led by a Mayo Clinic research team using artificial intelligence (AI), investigators developed an algorithm to improve the prediction of colorectal cancer recurrence.

Excluding skin cancers, colorectal cancer is the third most common cancer diagnosed in the U.S., according to the American Cancer Society.

Rish Pai, M.D., Ph.D., a pathologist at Mayo Clinic in Arizona and senior author, developed QuantCRC, a deep-learning segmentation algorithm, to identify different regions within tumors using nearly 6,500 digital slide images.

Fifteen parameters were recorded from each image of colorectal cancer and compared to the findings in the pathology report and health records.

A prognostic model using QuantCRC was developed to predict recurrence-free survival (Unsplash)
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The large number of tumors that are analyzed allowed us to learn which features were most predictive of tumor behavior (Unsplash)
The large number of tumors that are analyzed allowed us to learn which features were most predictive of tumor behavior (Unsplash)

The investigators used biospecimens of colorectal cancers from the Colon Cancer Family Registry participating locations in Australia, Canada and the U.S., including Mayo Clinic, to make up the internal training cohort. They validated the results with an external cohort of locations not participating in the Colon Cancer Family Registry in Canada and the U.S.

"QuantCRC can identify different regions within the tumor and extract quantitative data from these regions," says Dr. Pai. "The algorithm converts an image into a set of numbers that is unique to that tumor. The large number of tumors that we analyzed allowed us to learn which features were most predictive of tumor behavior. We can now apply what we have learned to new colon cancers to predict how the tumor will behave."

Dr. Pai says he hopes this study will be of value to patients with colon cancer, pathologists who look at colon cancer specimens, and oncologists who treat colon cancer.

A prognostic model using QuantCRC was developed to predict recurrence-free survival (Unsplash)
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The algorithm can identify a subset of patients who may not need to receive chemotherapy, given the low probability of recurrence. It also can help identify those patients at high risk of recurrence that may benefit from more intensive treatment or follow-up (Unsplash)
The algorithm can identify a subset of patients who may not need to receive chemotherapy, given the low probability of recurrence. It also can help identify those patients at high risk of recurrence that may benefit from more intensive treatment or follow-up (Unsplash)

"For patients with colon cancer, the algorithm gives oncologists another tool to help guide therapy and follow-up," says Dr. Pai.

The team of researchers from Australia, Canada and the U.S. concluded that QuantCRC provides a powerful addition to routine pathologic reporting of colorectal cancer. A prognostic model using QuantCRC could improve prediction of recurrence-free survival.

As a next step in his research, Dr. Pai says he plans to use QuantCRC to better understand mechanisms of tumor recurrence and see if it can predict the response to certain treatments, like immunotherapy. (NS/Newswise)

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