A group of Japanese researchers has announced that it has developed a deep-learning model to predict the cancer onset risk from fatty liver images. The team, led by the University of Tokyo, created the model by using some of digital images of fatty liver tissues collected from 46 people who developed liver cancer within seven years of biopsy and 639 others who did not. For the artificial intelligence application project, the researchers gathered such images from a total of 2,432 people who had undergone the live tissue examination at nine medical institutions in the country. The model has proved that it can predict cancer onset risk with 82.3% accuracy, which compares with 78.2% for biopsy-based manual analyses, the researchers said. They also saw AI judge cell dysplasia and declining fat deposition despite the progression of fat liver as cancer risk factors and predict a high probability of mild fibrosis developing into cancer.

Pulmonary nodules appear as small spots on the lungs on chest imaging. They have become a much more common finding as CT has gained favor over X-rays for chest imaging. "A nodule would appear on somewhere between 5% to 8% of chest X-rays," said study senior author Anil Vachani, M.D., director of clinical research in the section of Interventional Pulmonology and Thoracic Oncology at the Perelman School of Medicine, University of Pennsylvania in Philadelphia. "Chest CT is such a sensitive test, you'll see a small nodule in upwards of a third to a half of cases. We've gone from a problem that was relatively uncommon to one that affects 1.6 million people in the U.S. every year."

As a hereditary disease, breast cancer has affected hundreds of families throughout the state. Annually, an average of 1,190 women are diagnosed with breast cancer in Hawaiʻi. As October approaches in recognition of National Breast Cancer Awareness Month, new public impact research from the University of Hawaiʻi Cancer Center is using artificial intelligence (AI) to improve risk assessment for breast cancer to aid in prevention and early detection, improving breast cancer outcomes for women all over the world. To reduce unnecessary imaging for breast cancer and costs associated with it, UH Cancer Center Researcher John Shepherd and his colleagues found that AI is able to distinguish between the mammograms of women who are more likely to develop breast cancer later on, and those who are not. The study was published in Radiology.