Researchers at Karolinska Institutet and Karolinska University Hospital in Sweden have compared the ability of three artificial intelligence (AI) algorithms to identify breast cancer based on previously taken mammograms. The best algorithm proved to be as accurate as the average radiologist. The results, published in JAMA Oncology, may lead the way in reorganising breast cancer screening for the future. "This is the first independent comparison conducted to assess the accuracy of several different AI algorithms," says study author Fredrik Strand, a researcher at the Department of Oncology-Pathology at Karolinska Institutet and a radiologist at Karolinska University Hospital. "We can demonstrate that one of the three algorithms is significantly better than the others and that it equals the accuracy of the average radiologist."
A shortage of senior radiologists around the UK is causing delays for patients, and affecting cancer and other medical care. Radiologists' leaders say the situation is unacceptable and must be tackled by ministers. Figures suggest their workload of reading and interpreting scans has increased by 30% between 2012 and 2017. But the number of consultant radiologists in England has gone up by just 15% in that time. The number of these senior posts in Scotland, Wales and Northern Ireland has remained static over that period.
Authors from the Center for Data Science at New York University were Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Thibault Févry, and Kyunghyun Cho, who is also on the faculty of NYU's Courant Institute of Mathematical Sciences. Also authors were Kara Ho at SUNY Downstate College of Medicine; Masha Zorin in the Department of Computer Science and Technology at the University of Cambridge in the United Kingdom; and Stanisław Jastrzębski from Jagiellonian University in Poland, and Joe Katsnelson in the Department of Information Technology, NYU Langone Health.
A new framework combining machine learning and radiomics will help distinguish between low- and high-risk prostate cancer, according to new research published in Scientific Reports. "By rigorously and systematically combining machine learning with radiomics, our goal is to provide radiologists and clinical personnel with a sound prediction tool that can eventually translate to more effective and personalized patient care," said lead author Gaurav Pandey, PhD, of the Icahn School of Medicine at Mount Sinai, in a prepared statement. AI continues to be an instrumental tool in the diagnosis of many cancers, including cervical, uterine and lung. Pandey and colleagues developed the method to allow for radiologists to accurately identify treatment options for prostate cancer patients. This may decrease the chance for unnecessary clinical intervention.
Of all cancers worldwide, lung cancer is the deadliest. It takes more than 1.7 million lives per year -- more than breast, prostate and colorectal cancer combined. Part of the problem is that the majority of cancers aren't caught until later stages, when interventions tend to be less successful. Google is determined to change that, and with its new AI-based tool, it hopes to make lung cancer prediction more accurate and more accessible. To screen for lung cancer, radiologists typically view hundreds of images from a single CT scan.