Showing papers in "Academic Radiology in 2019"

TL;DR: Canadian medical students' perceptions of the impact of AI on radiology, and their influence on the students' preference for radiology specialty are investigated to ensure radiology is perceived as a viable long-term career choice.

TL;DR: A review of the prevalence, causes, and impact of burnout among radiology faculty and trainees, and a discussion on strategies for overcoming burnout and promoting overall health and well-being among radiologists are presented.

TL;DR: The Association of University Radiologists Radiology Research Alliance Task Force on Noninterpretive Skills presents a review of several innovative teaching methods, which include the use of audience response technology, long-distance teaching, the flipped classroom, and active learning.

TL;DR: DBT performed significantly better than FFDM in the merged view classification of mass and ARD lesions, suggesting that the information extracted by the CNN from DBT images may be more relevant to lesion malignancy status than the information extracts from FFDM images.

TL;DR: The study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with molecular subtypes of breast cancer subtypes.

TL;DR: A systematic review of the peer-reviewed literature on automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications should help prepare radiologists to better evaluate automated segmentsation tools and apply them not only to research, but eventually to clinical practice.

TL;DR: Deep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI and may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.

TL;DR: In a multivariable model, both baseline PRMfSAD and PRMemPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRmEMPH.

TL;DR: The 3D printing manufacturing method to create instruments in percutaneous procedures is feasible and sustained drug release up to the 5-day limit of testing is confirmed.

TL;DR: It is proposed that three-dimensional imaging features from fat-suppressed T2-weighted imaging could be used as candidate biomarkers for preoperative prediction of histopathological grades of soft tissue sarcomas noninvasively.

TL;DR: A combined ML and TA approach appears as a feasible tool to predict histopathological EPE on biparametric MR images.

TL;DR: Findings suggest that GABA levels decline with age in humans and are associated with declines in fluid processing ability.

TL;DR: The results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image, and this work was the first to evaluate the performance of using radiomics method to classify lung cancer Histological sub types based on non enhanced computed tomographic images.

TL;DR: An automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on the previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness is proposed.

TL;DR: The state of research on perceptual and interpretive error in radiology is reviewed, and avenues for further error examination, and strategies for mitigating these errors are discussed.

TL;DR: CT-based radiomic features of stage Ⅱ CRC are associated with microsatellite instability status and combining analysis of clinical features and CT features could improve predictive efficacy and could potentially select the patients for individualized therapy noninvasively.

TL;DR: HP gas MRI biomarkers are expected to provide sensitive outcome measures that can be used in disease surveillance as well as interventional studies involving novel CF therapies, and emerging HP gas imaging techniques such as multiple breath washout imaging are introduced.

TL;DR: These methods are summarized and the benefits and drawbacks of each method for performing localization of lymph nodes in the axilla are described.

TL;DR: The baseline LDCT lung cancer screening showed subsolid nodules accounted for the majority of lung cancer, and 5 mm in size would be recommended as the positive result threshold.

TL;DR: The MRI-derived DOI was valuable for the preoperative staging of oral tongue cancer and the optimal measurement method should be selected on a case-by-case basis.

TL;DR: Inclusion of multiple radiomic features, automatically extracted from magnetic resonance images, in a lesion signature significantly improved the ability to distinguish between benign lesions and luminal A breast cancers, compared to using maximum linear size alone.

TL;DR: AWE on magnetic resonance vessel wall imaging is significantly and independently associated with aneurysm rupture and may become a promising imaging marker to predict aneurYSm behavior and identify high-risk aneuryms.

TL;DR: The Association of Program Directors in Radiology (APDR) is concerned that pass/fail reporting of the USMLE Step 1 score would take away an objective measure of medical student's knowledge and the incentive to acquire as much of it as possible.

TL;DR: CDSS-T improves physician performance for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

TL;DR: A novel convolutional neural network derived pixel-wise mammographic breast evaluation using a CNN architecture can stratify breast cancer risk, independent of the BD.

TL;DR: Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm, which was superior to the SVM algorithm for model construction.

TL;DR: Introduction of BREAST into national training programs appears to have an important impact in promoting diagnostic efficacy amongst radiologists and radiology registrars undergoing mammographic readings.

TL;DR: A comprehensive review on gaming in radiology education offers insight into the importance of gaming, types of games and principles utilized in gaming, as well as applications that are inherent in artificial intelligence and continued medical education.

TL;DR: High-risk women with greater than minimal BPE at screening MRI have increased risk of future breast cancer.

TL;DR: The ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.