Artificial Intelligence Medical Imaging to Determine Which Rectal Cancer Patients Need Surgery

Researchers plan to test artificial intelligence (AI) medical imaging to determine which rectal cancer patients need surgery - or can avoid it.

Building on its successes in applying AI to medical imaging to enhance treatment of other diseases, a research team, led by Case Western Reserve University (Cleveland, OH, USA), will test its approach with rectal cancer patients. Specifically, the researchers hope to provide reliable guidance regarding whether patients need to have surgery as part of their treatment.

Currently, clinicians do not have a reliable way to predict which rectal cancer patients would respond favorably to treatments such as chemotherapy or radiation, so most patients have to undergo invasive surgery to remove the rectum and surrounding tissue. Previous research has reported that up to 30% of people diagnosed with rectal cancer have surgery they didn’t need, and often experience effects that hamper the daily life of the patient post the surgery. Those effects can include the need for a colostomy bag, even if temporary, and possible changes in everything from sexual function and infection to mental health, according to previous research.

In the new study, the researchers will work from imaging data from more than 2,000 rectal cancer patients who had been treated at hospitals over the last five years, and test their AI on about 450 to 500 patients. They will tetrospectively test their radiomics to determine if it could have shown which patients would benefit from chemoradiation therapy and which wouldn’t, requiring the surgery. Radiomics refers to the growing number of AI-driven methods to extract a large number of features from medical images using data-characterization algorithms. The features can then help uncover tumors and other characteristics usually invisible to the naked eye. Throughout this project, the research team will design and validate new types of radiomic tools to capture aspects of rectal tumors related to chemoradiation response. The team has already made significant strides in using the tools to predict treatment response to rectal cancer.

“In too many cases, patients are being overtreated,” said lead researcher Satish Viswanath, an assistant professor of biomedical engineering who is leading the work as a member of the Center for Computational Imaging and Personalized Diagnostics (CCIPD). “Instead, if our AI technology is successful, we could tell the clinician right up front—based on a routine MRI (magnetic resonance imaging) scan—if a patient will do well with only chemoradiation and then can be observed, without having this serious surgery.”

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