WEDNESDAY, March 13, 2019 (HealthDay News) -- The term artificial intelligence (AI) might bring to mind robots or self-driving cars. But one group of researchers is using a type of AI to improve lung cancer screening. Screening is important for early diagnosis and improved survival odds, but the current lung cancer screening method has a 96 percent false positive rate. But in the new study, investigators were able to reduce false findings of lung cancer without missing any actual cases. A low-dose CT scan is the standard diagnostic test for people at high risk of lung cancer.
Cancer is a worldwide issue. Statistics show that 17 million cases of the disease were diagnosed across the globe last year alone. Depressingly, the same research suggests there will be 27.5 million new cancer cases diagnosed each year by 2040. Although the stats don't necessarily spell good news, it's important to note that diagnosis, treatment, and in turn, patient outcomes have improved significantly. If we look back at the 1970s, less than a quarter of people with the disease survived.
One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.
Thyroid nodules are small lumps that form within the thyroid gland and are quite common in the general population, with a prevalence as high as 67%. The great majority of thyroid nodules are not cancerous and cause no symptoms. However, there are currently limited guidelines on what to do with a nodule when the risk of cancer is uncertain. A new study from The Sidney Kimmel Cancer Center - Jefferson Health investigates whether a non-invasive method of ultrasound imaging, combined with a Google-platform machine-learning algorithm, could be used as a rapid and inexpensive first screen for thyroid cancer. "Currently, ultrasounds can tell us if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy or not," says Elizabeth Cottril, MD, an otolaryngologist at Thomas Jefferson University, and clinical leader of the study.
CHICAGO --- Deep learning - a form of artificial intelligence - was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, reports a new study from Google and Northwestern Medicine. This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists. Deep learning is a technique that teaches computers to learn by example.