Publications by authors named "R Graham Barr"

1,832 Publications

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Editorial: Growing Up in a Digital World - Social and Cognitive Implications.

Front Psychol 2021 30;12:745788. Epub 2021 Sep 30.

Department of Psychology, Georgetown University, Washington, DC, United States.

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http://dx.doi.org/10.3389/fpsyg.2021.745788DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514693PMC
September 2021

The Burden of Late Effects and Related Risk Factors in Adolescent and Young Adult Cancer Survivors: A Scoping Review.

Cancers (Basel) 2021 Sep 28;13(19). Epub 2021 Sep 28.

Cancer Care Alberta, Alberta Health Services, Holy Cross Centre, Department of Cancer Epidemiology and Prevention Research, 5th Floor, BOX ACB, 2210-2 St. SW, Calgary, AB T2S 3C3, Canada.

Risk factors associated with late effects in survivors of adolescent and young adult (AYA) cancer are poorly understood. We conducted a systematic scoping review to identify cohort studies published in English from 2010-2020 that included: (1) cancer survivors who were AYAs (age 15-39 years) at diagnosis and (2) outcomes of subsequent malignant neoplasms (SMNs), chronic conditions, and/or late mortality (>5 years postdiagnosis). There were 652 abstracts identified and, ultimately, 106 unique studies were included, of which 23, 34, and 54 studies related to the risk of SMNs, chronic conditions, and mortality, respectively. Studies investigating late effects among survivors of any primary cancer reported that AYA cancer survivors were at higher risk of SMN, chronic conditions, and all-cause mortality compared to controls. There was an indication that the following factors increased risk: radiation exposure (n = 3) for SMNs; younger attained age (n = 4) and earlier calendar period of diagnosis (n = 3) for chronic conditions; and non-Hispanic Black or Hispanic (n = 5), low socioeconomic status (n = 3), and earlier calendar period of diagnosis (n = 4) for late mortality. More studies including the full AYA age spectrum, treatment data, and results stratified by age, sex, and cancer type are needed to advance knowledge about late effects in AYA cancer survivors.
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http://dx.doi.org/10.3390/cancers13194870DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8508204PMC
September 2021

Bi-modal Transfer Learning for Classifying Breast Cancers via Combined B-mode and Ultrasound Strain Imaging.

IEEE Trans Ultrason Ferroelectr Freq Control 2021 Oct 11;PP. Epub 2021 Oct 11.

Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model that is based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet & ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in US images.
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http://dx.doi.org/10.1109/TUFFC.2021.3119251DOI Listing
October 2021

Use of lumason/sonovue in contrast-enhanced ultrasound of the kidney for characterization of renal masses-a meta-analysis.

Authors:
Richard G Barr

Abdom Radiol (NY) 2021 Oct 8. Epub 2021 Oct 8.

Department of Radiology, Northeastern Ohio Medical University, Rootstown, OH, USA.

Indeterminate renal masses are a common clinical problem. CEUS has several advantages to characterize both cystic and solid renal masses including thin slice thickness, excellent background subtraction, and real-time imaging with a high frame rate. The ultrasound contrast agents are not nephrotoxic and can be used in patients with renal insufficiency and obstruction. The Bosniak classification has been developed for use in CT and MRI. A CEUS Bosniak classification has not yet been developed. This meta-analysis reviews the results of renal mass characterization using Lumason/Sonovue in characterizing renal solid and cystic masses. For complex cystic renal lesions (419 patients; 436 lesions), the pooled sensitivity and specificity of CEUS were 95% (95% CI: 91%, 99%) and 84% (95% CI: 77%, 90%) and for solid lesions (331 patients; 341 lesions), the pooled sensitivity and specificity of CEUS were 98% (95% CI: 95%, 100%) and 78% (95% CI: 68%, 88%), respectively.
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http://dx.doi.org/10.1007/s00261-021-03295-2DOI Listing
October 2021

A case of acanthosis nigricans-like mycosis fungoides.

JAAD Case Rep 2021 Oct 8;16:144-148. Epub 2021 Sep 8.

Department of Dermatology, University of California, Irvine, Irvine, California.

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http://dx.doi.org/10.1016/j.jdcr.2021.08.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484728PMC
October 2021
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