Artificial Intelligence Shows Disparities in White and Black Prostate Cancer Patients

Artificial Intelligence Shows Disparities in White and Black Prostate Cancer Patients
Artificial Intelligence Shows Disparities in White and Black Prostate Cancer Patients

According to The Daily, Case Western Reserve University researchers revealed cellular distinctions between black and white patients with prostate cancer, using artificial intelligence.

The team reported in a recent study that AI analysis of digitized images of cancer tissues show critical variations between black and white male prostate cancer patients. The findings indicate that the new population-specific information could significantly improve care for black men with prostate cancer.

“On one level, we’re simply trying to understand and answer this question: ‘Are there biological differences in the disease, in cancer, that are a function of your ethnicity or your race?’” said Anant Madabhushi in a press release. He also serves as professor II of biomedical engineering at Case Western Reserve and senior author on the study in Clinical Cancer Research, a journal of the American Association for Cancer Research.

The CDC says that prostate cancer is the most common cancer among men in the US, aside from non-melanoma skin cancer. Also one of the leading causes of death among men of all races, more than 75,000 newly diagnosed patients have to undergo prostate cancer-related surgical procedures each year, but 30 to 40 percent of patients will have the cancer return.

An imperative aspect of managing and eradicating prostate cancer, providers need to identify which men are at higher risk of disease recurrence following prostate surgery and recognize which patients would benefit from adjuvant therapy.

The three studies was performed with almost 400 male patients with the disease. Researchers developed and trained an AI model to look for patterns not only from images of the tumor itself but at tissue outside of the tumor, called the stroma.

This method tracked how well the patients responded to chemotherapy, immunotherapy, or in some cases, whether cancer would return or how long a patient might live. Scientists were able to retroactively see visual signals in tissue slides from their initial diagnosis to determine which patients would have to deal with the cancer coming back.

“We were able to look at, and actually measure, hundreds of thousands, even millions, of cancer cells to see features that a human could never see—including structural characteristics,” Hersh Bhargava, PhD student at the University of California-San Francisco and lead author said in the report.

However, the patient pool in this study was about 80 percent white and 18 percent black, so the model was slightly biased.

“The model was skewed toward the majority population,” Madabhushi said. “Once we found the variations, applying the model to all would be doing a disservice to that one population.”

After creating a race-specific AI model, researchers found that there was a six-fold increase in accurately determining which black patients would have prostate cancer recurrence. The results suggest that there are biological variants among patients of different races that must be considered in prostate cancer care.

“It’s clear from the existing scientific literature that there are racial disparities in all cancers, but it appears that especially in prostate cancer that those differences can’t be explained by access to care or socioeconomic status—but rather that there is a biological component to how the cancers manifest differently between black and white patients,” said Bhargava in the report.

Earlier studies have pointed out racial and ethnic gaps in breast cancer and oral cancer, often are linked to an individual’s social determinants of health. However, the cellular differences found in this particular study imply that there is a research bias at the human level, which can show up in AI algorithms, the study said.

“Even as we do this groundbreaking research, we can’t allow ourselves to get trapped into trusting these models blindly, so we need to question whether we are considering all populations (and) ask how diverse our research pool is,” said Madabhushi in the study.

Find more articles on prostate cancer here.