BookR: School of Health Core Facilities Booking
PubMed Publications| Keyword search (4,163 papers available) |  |
"Gauthier S" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Impact of a national dementia research consortium: The Canadian Consortium on Neurodegeneration in Aging (CCNA) | Chertkow H; Phillips N; Rockwood K; Anderson N; Andrew MK; Bartha R; Beaudoin C; Bélanger N; Bellec P; Belleville S; Bergman H; Best S; Bethell J; Bherer L; Black S; Borrie M; Camicioli R; Carrier J; Cashman N; Chan S; Crowshoe L; Cuello C; Cynader M; Dang-Vu T; Das S; Dixon RA; Ducharme S; Einstein G; Evans AC; Fahnestock M; Feldman H; Ferland G; Finger E; Fisk JD; Fogarty J; Fon E; Gan-Or Z; Gauthier S; Greenwood C; Henri-Bellemare C; Herrmann N; Hogan DB; Hsiung R; Itzhak I; Jacklin K; Lanctôt K; Lim A; MacKenzie I; Masellis M; Maxwell C; McAiney C; McGilton K; McLaurin J; Mihailidis A; Mohades Z; Montero-Odasso M; Morgan D; Naglie G; Nygaard H; O' Connell M; Petersen R; Pilon R; Rajah MN; Rapoport M; Roach P; Robillard JM; Rogaeva E; Rosa-Neto P; Rylett J; Sadavoy J; St George-Hyslop P; Seitz D; Smith E; Stefanovic B; Vedel I; Walker JD; Wellington C; Whitehead V; Wittich W; | 39636028 HKAP |
| 2 | Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis | Vianna P; Calce SI; Boustros P; Larocque-Rigney C; Patry-Beaudoin L; Luo YH; Aslan E; Marinos J; Alamri TM; Vu KN; Murphy-Lavallée J; Billiard JS; Montagnon E; Li H; Kadoury S; Nguyen BN; Gauthier S; Therien B; Rish I; Belilovsky E; Wolf G; Chassé M; Cloutier G; Tang A; | 37787678 ENCS |
| 3 | CCCDTD5: Clinical role of neuroimaging and liquid biomarkers in patients with cognitive impairment | Brisson M; Brodeur C; Létourneau-Guillon L; Masellis M; Stoessl J; Tamm A; Zukotynski K; Ismail Z; Gauthier S; Rosa-Neto P; Soucy JP; | 33532543 PERFORM |
| 4 | Recommendations of the 5th Canadian Consensus Conference on the diagnosis and treatment of dementia. | Ismail Z, Black SE, Camicioli R, Chertkow H, Herrmann N, Laforce R, Montero-Odasso M, Rockwood K, Rosa-Neto P, Seitz D, Sivananthan S, Smith EE, Soucy JP, Vedel I, Gauthier S, CCCDTD5 participants | 32725777 PERFORM |
| 5 | Topographical distribution of Aβ predicts progression to dementia in Aβ positive mild cognitive impairment | Pascoal TA, Therriault J, Mathotaarachchi S, Kang MS, Shin M, Benedet AL, Chamoun M, Tissot C, Lussier F, Mohaddes S, Soucy JP, Massarweh G, Gauthier S, Rosa-Neto P, | 32582834 PERFORM |
| 6 | Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer's disease. | Hadoux X, Hui F, Lim JKH, Masters CL, Pébay A, Chevalier S, Ha J, Loi S, Fowler CJ, Rowe C, Villemagne VL, Taylor EN, Fluke C, Soucy JP, Lesage F, Sylvestre JP, Rosa-Neto P, Mathotaarachchi S, Gauthier S, Nasreddine ZS, Arbour JD, Rhéaume MA, Beaulieu S, Dirani M, Nguyen CTO, Bui BV, Williamson R, Crowston JG, van Wijngaarden P | 31530809 PERFORM |
| 7 | The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study. | Chertkow H, Borrie M, Whitehead V, Black SE, Feldman HH, Gauthier S, Hogan DB, Masellis M, McGilton K, Rockwood K, Tierney MC, Andrew M, Hsiung GR, Camicioli R, Smith EE, Fogarty J, Lindsay J, Best S, Evans A, Das S, Mohaddes Z, Pilon R, Poirier J, Phillips NA, MacNamara E, Dixon RA, Duchesne S, MacKenzie I, Rylett RJ | 31309917 PSYCHOLOGY |
| 8 | Brain perfusion during rapid-eye-movement sleep successfully identifies amnestic mild cognitive impairment. | Brayet P, Petit D, Baril AA, Gosselin N, Gagnon JF, Soucy JP, Gauthier S, Kergoat MJ, Carrier J, Rouleau I, Montplaisir J | 28522082 PERFORM |
| 9 | Quantification of brain cholinergic denervation in Alzheimer's disease using PET imaging with [18F]-FEOBV. | Aghourian M, Legault-Denis C, Soucy JP, Rosa-Neto P, Gauthier S, Kostikov A, Gravel P, Bédard MA | 28894304 PERFORM |
| 10 | Amyloid and tau signatures of brain metabolic decline in preclinical Alzheimer's disease. | Pascoal TA, Mathotaarachchi S, Shin M, Park AY, Mohades S, Benedet AL, Kang MS, Massarweh G, Soucy JP, Gauthier S, Rosa-Neto P, Alzheimer’s Disease Neuroimaging Initiative | 29396637 PERFORM |
| Title: | Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis | ||||
| Authors: | Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/37787678/ | ||||
| DOI: | 10.1148/radiol.230659 | ||||
| Publication: | Radiology | ||||
| Keywords: | |||||
| PMID: | 37787678 | Category: | Date Added: | 2023-10-03 | |
| Dept Affiliation: | ENCS | ||||
Description: |
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of ? statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue. |
