Lung cancer is a common and often aggressive form of cancer. As it is difficult for doctors to detect it early on, people with lung cancer need to receive the best, most targeted therapy in order to make a positive outlook more likely. Immunotherapy is an option, but how can doctors know who will benefit? According to the National Cancer Institute, lung and bronchus cancer is the second most widespread type of cancer among people in the United States, accounting for 12.9% of all new cancer cases. This form of cancer often has no noticeable symptoms in its early stages, which can mean that doctors are unable to detect it at first.

As scientists gain a deeper understanding of cancer, we have begun to tailor treatments to specific types of the disease. By putting a cancer tumor through advanced molecular testing, we can find out what proteins and genes are specific to this particular tumor and match it to the most effective treatment. The results are significant improvements in the survival rates of those patients fortunate enough to have a type of cancer for which a treatment is available. The trouble is, too few patients are so fortunate. Despite a great deal of cancer research conducted over the past few decades, only a handful of targeted therapies have reached clinical practice--between 8-and-15 percent of cancer patients in the US are eligible to receive targeted therapy.

A team of researchers from the University of Central Florida have developed an artificial intelligence system to detect cancerous tumors normally missed by radiologists, the university stated in a blog post. The team has trained a computer to identify minute particles of lung cancer in CT scans, which are otherwise difficult for radiologists to pinpoint. The researchers claim that the system is about 95% accurate at identifying cancer versus 65% by humans, the blog added. The AI uses an algorithm similar to facial-recognition software that scans thousands of faces to identify and match a particular pattern. "We used the brain as a model to create our system. You know how connections between neurons in the brain strengthen during development and learn? We used that blueprint, if you will, to help our system understand how to look for patterns in the CT scans and teach itself how to find these tiny tumors," said Rodney LaLonde, a doctoral candidate, in the blog post.

MD Anderson Cancer Center is sitting on 23 petabytes of data, including more than 2 billion diagnostic radiology images, generated by its massive IT infrastructure. But Chris Belmont, vice president and CIO, isn't intimidated by the amount of data--he's just scared of staring at it too long. "Our biggest fear when we decided to move into Big Data was that, like many healthcare organizations, we'd have a two-year data'ingestion' process where we'd keep thinking about that massive set of data, and connect all our systems big and small together, go get even more data from external sources, and then eventually offer our users an add-on tool and tell them to go at it," Belmont says. "By the time we'd be done ingesting all that data, the time to change the game in terms of costs or population health would have already passed." MD Anderson, the Houston-based health system devoted to cancer care, isn't the type of organization to let time slip by.