oncology


Google's AI boosts accuracy of lung cancer diagnosis, study shows - STAT

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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.


Artificial intelligence better than humans at spotting lung cancer

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The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%. These errors typically delay the diagnosis of lung cancer until the disease has reached an advanced stage when it becomes too difficult to treat. New research may safeguard against these errors.


Google's lung cancer detection AI outperforms 6 human radiologists

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Google AI researchers working with Northwestern Medicine created an AI model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience. When analyzing a single CT scan, the model detected cancer 5% more often on average than a group of six human experts and was 11% more likely to reduce false positives. Humans and AI achieved similar results when radiologists were able to view prior CT scans. When it came to predicting the risk of cancer two years after a screening, the model was able to find cancer 9.5% more often compared to estimated radiologist performance laid out in the National Lung Screening Test (NLST) study. Detailed in research published today in Nature Medicine, the end-to-end deep learning model was used to predict whether a patient has lung cancer, generating a patient lung cancer malignancy risk score and identifying the location of the malignant tissue in the lungs.


Artificial intelligence system spots lung cancer before radiologists

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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.


Artificial intelligence system spots lung cancer before radiologists

#artificialintelligence

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.


A.I. Took a Test to Detect Lung Cancer. It Got an A.

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The process, known as deep learning, is already being used in many applications, like enabling computers to understand speech and identify objects so that a self-driving car will recognize a stop sign and distinguish a pedestrian from a telephone pole. In medicine, Google has already created systems to help pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect eye disease in people with diabetes. "We have some of the biggest computers in the world," said Dr. Daniel Tse, a project manager at Google and an author of the journal article. "We started wanting to push the boundaries of basic science to find interesting and cool applications to work on." In the new study, the researchers applied artificial intelligence to CT scans used to screen people for lung cancer, which caused 160,000 deaths in the United States last year, and 1.7 million worldwide.


Google trained its AI to predict lung cancer

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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.


End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

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D.A., A.P.K., S.B. and B.C. developed the network architecture and data/modeling infrastructure, training and testing setup. D.A. and A.P.K. created the figures, wrote the methods and performed additional analysis requested in the review process. D.P.N. and J.J.R. provided clinical expertise and guidance on the study design. G.C and S.S. advised on the modeling techniques. M.E., S.S., J.J.R., B.C., W.Y. and D.A. created the datasets, interpreted the data and defined the clinical labels.


Pfizer, Concerto HealthAI join up for precision medicine partnership

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Pfizer is working with Boston-based Concerto HealthAI for a collaboration that will apply Concerto's eurekaHealth artificial intelligence technology to precision oncology research. WHY IT MATTERS The partnership will use the AI platform to gain faster actionable insights for Pfizer's investigational therapies and commercialized therapeutics for treatment of solid tumors and hematologic malignancies, officials said. Romesh Wadhwani, executive chairman of Concerto HealthAI said his company's technology would be valuable to Pfizer in "finding and acting on meaningful insights in key cancer subpopulations." Much of the data for the initiative will come from clinical practices participating in the American Society of Clinical Oncology's CancerLinQ initiative; researchers will explore potential study designs for real-world data to develop therapeutics that are both pre- and post-approval, according to Concerto HealthAI, building on similar work done in collaboration with the U.S. Food and Drug Administration. A joint steering committee from both companies will oversee the work, with initial research expected early next year.


Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

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Skin cancer is the most common malignancy in fair-skinned populations, and melanoma accounts for the majority of skin cancer–related deaths worldwide [1x[1]Schadendorf, D., van Akkooi, A.C., Berking, C., Griewank, K.G., Gutzmer, R., Hauschild, A. et al. Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. The CNN deconstructed digital images of skin lesions and generated its own diagnostic criteria for melanoma detection during training. Several follow-up publications by other authors have demonstrated dermatologist-level skin cancer classification by using deep neural networks (CNN) [4x[4]Marchetti, M.A., Codella, N.C., Dusza, S.W., Gutman, D.A., Helba, B., Kalloo, A. et al. Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.