Lungs cancer is one of the most common cancer among men and second most in women. In U.S alone lung cancer is responsible for more then 29 percent of cancer deaths. The general symptoms of lung cancer are coughing in blood, pain in chest and shortness in breath.
For the prevention of this cancer and to detect it as early as possible, a team of researches from New York has developed an artificial intelligence tool that analyzes patients’ lung tissue. It also specify the cancer types, and even identify the altered genes driving abnormal cell growth.
The study has published in the journal Nature Medicine. This AI system has created by team of researchers led by Aristotelis Tsirigos at New York University (NYU) School of Medicine. Researchers also contend that the AI tool can identify genetic changes in patients’ tumors.
“Such genetic changes or mutations often cause the abnormal growth seen in cancer, but can also change a cell’s shape and interactions with its surroundings, providing visual clues for automated analysis,” stated a prepared statement issued by the university.
The Artificial Intelligence system can distinguish between adenocarcinoma and squamous cell carcinoma, two lung cancer types that experienced pathologists at times struggle to parse without confirmatory tests with a mind bobbling accuracy of 97 percent.
The researchers uses deep convolutional neural network. CNN use a variation of multilayer perceptrons designed to require minimal preprocessing. It is most commonly applied for analyzing visual imagery recommender systems and natural language processing.
Team trained the AI with images from a database of cancer diagnoses to accurately and automatically classify them into adenocarcinoma and squamous cell carcinoma—the most prevalent subtypes of lung cancer—or normal lung tissue. The database was obtained from The Cancer Genome Atlas.
The system can also determine whether abnormal versions of six genes linked to lung cancer – including EGFR, KRAS and TP53 – were present in cells by only analyzing images.
Determinations of which genes are changed in each tumor has become vital due to increased use of targeted therapies that work against cancer cells with specific genetic mutations. For example, 20 percent of patients with adenocarcinoma have mutations in the gene EGFR, which can be treated with drug therapies.
The researchers has trained the neural network to predict the 10 most commonly mutated genes in adenocarcinoma and found that six of them can be predicted from pathology images, with an accuracy that ranged from 73 to 86 percent, depending on the gene.
“In our study, we were excited to improve on pathologist-level accuracies, and to show that AI can discover previously unknown patterns in the visible features of cancer cells and the tissues around them,” said co-author author Narges Razavian, PhD, assistant professor at NYU School of Medicine. “The synergy between data and computational power is creating unprecedented opportunities to improve both the practice and the science of medicine.
But the genetic tests currently used to confirm the presence of mutations can take weeks to return results, say the study authors.
“Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors,” state the authors. “These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type.”
The current team trained a deep convolutional neural network, Google’s Inception v3, to analyze slide images obtained from The Cancer Genome Atlas, a database of images where cancer diagnoses had already been determined. That let the researchers to measure how well their program could be trained to accurately and automatically classify normal versus diseased tissue.
“In our study, we were excited to improve on pathologist-level accuracies, and to show that AI can discover previously unknown patterns in the visible features of cancer cells and the tissues around them,” adds Narges Razavian, co-corresponding author and assistant professor in the departments of Radiology and Population Health. “The synergy between data and computational power is creating unprecedented opportunities to improve both the practice and the science of medicine.”
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