Imaging studies are an important part of screening and diagnosis for some cancers, lung, and breast in particular. Such studies have led to more lung and breast cancers being diagnosed at a smaller size compared to what was found prior to the advent of screening programs. One important research question that is currently being explored is whether the use of artificial intelligence to aid in diagnosis can improve the performance of radiologists alone. Let's take a look at what we know so far. According to the American Cancer Society (ACS), approximately one in eight women will be diagnosed with breast cancer in their lifetime.
While there has been controversy over when and how often women should be screened for breast cancer using mammograms, studies consistently show that screening can lead to earlier detection of the disease, when it's more treatable. So improving how effectively mammograms can detect abnormal growths that could be cancerous is a priority in the field. AI could play a role in accomplishing that--computer-based machine learning might help doctors to read mammograms more accurately. In a study published Jan. 1 in Nature, researchers from Google Health, and from universities in the U.S. and U.K., report on an AI model that reads mammograms with fewer false positives and false negatives than human experts. The algorithm, based on mammograms taken from more than 76,000 women in the U.K. and more than 15,000 in the U.S., reduced false positive rates by nearly 6% in the U.S., where women are screened every one to two years, and by 1.2% in the U.K., where women are screened every three years.
A 2009 study of 102 breast cancer patients at two Boston health centers found that one in four were affected by the "process of care" failures such as inadequate physical examinations and incomplete diagnostic tests. That's one of the reasons that of the half a million deaths worldwide caused by breast cancer, an estimated 90 percent are the result of metastasis. But researchers at the Naval Medical Center San Diego and Google AI, a division within Google dedicated to artificial intelligence (AI) research, have developed a promising solution employing cancer-detecting algorithms that autonomously evaluate lymph node biopsies. Their AI system -- dubbed Lymph Node Assistant, or LYNA -- is described in a paper titled "Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection," published in The American Journal of Surgical Pathology. In tests, it achieved an area under the receiver operating characteristic (AUC) -- a measure of detection accuracy -- of 99 percent.
Researchers have developed machine learning software that can accurately diagnose a patient's breast cancer risk 30 times faster than doctors, based on mammogram results and personal medical history. The system could help doctors give better diagnoses the first time around -- which means fewer mammogram callbacks and false positives. "This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram," said one of the researchers, Stephen Wong, from Houston Methodist Research Institute. "This has the potential to decrease unnecessary biopsies." A mammogram is a breast X-ray that aims to spot any potentially cancerous cells before symptoms arise.
Houston, Texas, USA: Google on Friday claimed that its AI algorithm can assist doctors in metastatic breast cancer detection with 99 percent accuracy, according to their papers published in the Archives of Pathology and Laboratory Medicine and The American Journal of Surgical Pathology. The algorithm technology, known as Lymph Node Assistant, or LYNA, is taught to check the abnormality in the pathology slides and accurately pinpoint the location of both cancers and other suspicious regions since some of the potential risks are too small to be spotted by the doctors. In their latest research, Google applied LYNA to a de-identified dataset from both Camelyon Challenge and an independent dataset from the Naval Medical Center San Diego for picking up the cancer cells from the tissue images. Metastatic tumors -- cancerous cells which break away from their tissue of origin, travel through the body through the circulatory or lymph systems, and form new tumors in other parts of the body -- are notoriously difficult to detect. A 2009 study of 102 breast cancer patients at two Boston health centers found that one in four were affected by the "process of care" failures such as inadequate physical examinations and incomplete diagnostic tests.