Resources: Page 227

Both CEUS, CTA work for assessing hepatic artery blocks
By Kate Madden Yee
Monday, November 27 | 3:20 p.m.-3:30 p.m. | SSE10-03 | Room E353CBoth contrast-enhanced ultrasound (CEUS) and CT angiography (CTA) are helpful tools for evaluating hepatic artery obstruction in liver transplant patients, according to research being presented on Monday afternoon.
November 6, 2017
SWE, MRE help detect fibrosis in patients with fatty liver disease
By Kate Madden Yee
Monday, November 27 | 3:20 p.m.-3:30 p.m. | SSE08-03 | Room E352Ultrasound shear-wave elastography (SWE), MR elastography (MRE), and transient elastography are all effective methods for diagnosing advanced fibrosis in patients with nonalcoholic fatty liver disease, according to researchers from the University of Pittsburgh.
November 6, 2017
Breast ultrasound's cancer detection rate similar after DBT, mammo
By Kate Madden Yee
Monday, November 27 | 3:00 p.m.-3:10 p.m. | SSE01-01 | Arie Crown TheaterScreening breast ultrasound's cancer detection rate in women with dense tissue is comparable after digital mammography versus after digital breast tomosynthesis (DBT), according to a study conducted at Brown University.
November 6, 2017
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Cancer detection rate of screening ultrasound increases over time
By Kate Madden Yee
Monday, November 27 | 12:15 p.m.-12:45 p.m. | BR225-SD-MOA5 | Lakeside, BR Community, Station 5Over time, the cancer detection rate of screening breast ultrasound increases -- without an increase in biopsies, researchers at Elizabeth Wende Breast Care in Rochester, NY, have found.
November 6, 2017
Road to RSNA 2017: Artificial Intelligence Preview
By Erik L. Ridley
Our next destination on the Road to RSNA is a stop in artificial intelligence (AI) for a preview of this year's presentations on AI in medical imaging, including machine learning, deep learning, and computer-aided detection/diagnosis. If you thought AI had taken off at RSNA 2016, you haven't seen anything yet.
November 5, 2017
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Breast MRI neural networks predict recurrence scores
By Kate Madden Yee
Friday, December 1 | 11:20 a.m.-11:30 a.m. | SST01-06 | Room E450BResearchers in New York have found that deep-learning networks can be trained with breast MRI data to predict Oncotype DX recurrence scores.
November 5, 2017
Deep learning can predict stenosis on fast SPECT-MPI
By Erik L. Ridley
Friday, December 1 | 11:00 a.m.-11:10 a.m. | SST02-04 | Room E450AResearchers have found that deep learning can improve the detection of potentially significant ischemic defects on raw, high-speed SPECT myocardial perfusion imaging (MPI) studies.
November 5, 2017
Deep learning can quantify fat around the heart
By Erik L. Ridley
Friday, December 1 | 10:30 a.m.-10:40 a.m. | SST02-01 | Room E450AA deep-learning algorithm can rapidly segment and quantify the volume of thoracic fat surrounding the heart, according to researchers from Cedars-Sinai.
November 5, 2017
Deep learning may sharply increase specificity of CCTA
By Erik L. Ridley
Thursday, November 30 | 11:50 a.m.-12:00 p.m. | SSQ02-09 | Room S502ABThe combination of a deep-learning algorithm and visual stenosis grading could significantly boost the specificity of coronary CT angiography (CCTA) for detecting functionally significant stenosis, a Dutch team has found.
November 5, 2017
3D CADv predicts recurrence of pulmonary nodules on CT
By Abraham Kim
Thursday, November 30 | 11:40 a.m.-11:50 a.m. | SSQ18-08 | Room S403BResearchers from Japan have demonstrated that 3D computer-aided detection and volumetry (CADv) software applied to CT scans can predict the resurgence of malignant lung nodules.
November 5, 2017
Machine learning can determine onset of stroke symptoms
By Erik L. Ridley
Wednesday, November 29 | 3:50 p.m.-4:00 p.m. | SSM12-06 | Room S404CDA South Korean research team will describe the potential of machine learning for the crucial task of determining when acute ischemic stroke patients began experiencing symptoms.
November 5, 2017
Machine learning forecasts survival in glioma patients
By Erik L. Ridley
Wednesday, November 29 | 3:10 p.m.-3:20 p.m. | SSM12-02 | Room S404CDMachine learning using MRI radiomic features may be able to predict the survival of patients with gliomas, according to this study from a research group in Taiwan.
November 5, 2017
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