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Article ; Online: Unleashing the Power of Deep Learning for Breast Cancer Detection through Open Mammography Datasets.

Cadrin-Chênevert, Alexandre

Radiology. Artificial intelligence

2023 Volume 5, Issue 2, Page(s) e220294

Language	English
Publishing date	2023-02-22
Publishing country	United States
Document type	Journal Article
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.220294
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Moving from ImageNet to RadImageNet for Improved Transfer Learning and Generalizability.

Cadrin-Chênevert, Alexandre

Radiology. Artificial intelligence

2022 Volume 4, Issue 5, Page(s) e220126

Language	English
Publishing date	2022-08-10
Publishing country	United States
Document type	Journal Article
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.220126
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Article ; Online: Toward a More Quantitative and Specific Representation of Normality.

Cadrin-Chênevert, Alexandre

Radiology. Artificial intelligence

2021 Volume 3, Issue 2, Page(s) e210005

Language	English
Publishing date	2021-03-03
Publishing country	United States
Document type	Journal Article ; Comment
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.2021210005
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Article ; Online: Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.

Yamga, Eric / Mullie, Louis / Durand, Madeleine / Cadrin-Chenevert, Alexandre / Tang, An / Montagnon, Emmanuel / Chartrand-Lefebvre, Carl / Chassé, Michaël

Frontiers in digital health

2023 Volume 5, Page(s) 1142822

Abstract: Background: Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and ... ...

Abstract	Background: Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment. Methods: We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes. Results: Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set. Conclusions: We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.
Language	English
Publishing date	2023-04-11
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2023.1142822
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Article ; Online: Tackling the Radiological Society of North America Pneumonia Detection Challenge.

Pan, Ian / Cadrin-Chênevert, Alexandre / Cheng, Phillip M

AJR. American journal of roentgenology

2019 Volume 213, Issue 3, Page(s) 568–574

Abstract: OBJECTIVE. ...

Abstract	OBJECTIVE.
MeSH term(s)	Algorithms ; Awards and Prizes ; Deep Learning ; Humans ; North America ; Pneumonia/diagnostic imaging ; Societies, Medical
Language	English
Publishing date	2019-05-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	82076-3
ISSN	1546-3141 ; 0361-803X ; 0092-5381
ISSN (online)	1546-3141
ISSN	0361-803X ; 0092-5381
DOI	10.2214/AJR.19.21512
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Ue I Zs.57: Show issues

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bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Upgrade Rate of Atypical Ductal Hyperplasia: Ten Years Experience and Predictive Factors.

Gagnon, Nickolas / Martel, Elise / Cadrin-Chênevert, Alexandre / Ledoux, Elisabeth / Racicot, Caroline / Villiard, Roselyne

The Journal of surgical research

2021 Volume 266, Page(s) 311–318

Abstract: Background: Atypical ductal hyperplasia (ADH) is a benign epithelial proliferative lesion with histologic features resembling those seen in low grade ductal carcinoma in situ (DCIS). Surgical excision of the biopsy site is the standard management ... ...

Abstract	Background: Atypical ductal hyperplasia (ADH) is a benign epithelial proliferative lesion with histologic features resembling those seen in low grade ductal carcinoma in situ (DCIS). Surgical excision of the biopsy site is the standard management approach. The objective of this study was to determine the upgrade rate from ADH on stereotactic breast biopsies to DCIS or invasive carcinoma (IC) in our institution. We also sought to identify clinical, pathologic and radiologic predictive factors associated with risk of upgrade. Materials and methods: Clinical charts, mammograms and pathology reports were reviewed for all women with a stereotactic breast biopsy showing ADH and subsequent surgery at our institution between 2008 and 2018. When available, mammograms were re-reviewed by a radiologist for this study. Results: 295 biopsies were analyzed in 290 patients. Mean age was 56 y old. Upgrade rate was 10.5% of which 7.5% were DCIS and 3.1% IC. Mammograms were reviewed by a radiologist in 161 patients from 2013 to 2018. In this subset of patients, the rate of upgrade was 8.7% (4.35% DCIS and 4.35% IC). A statistically significant difference he largest size of the microcalcification clusters on mammogram was observed between the upgraded and the non-upgraded subgroups (14.2 mm versus 8.9 mm, P = 0.03) CONCLUSIONS: The evaluation of the largest size of microcalcification clusters on mammogram as a cut-off feature could be considered to choose between an observational versus a surgical approach. This large series provides contemporary data to assist informed decision making regarding the treatment of our patients.
MeSH term(s)	Adult ; Aged ; Biopsy/statistics & numerical data ; Breast/pathology ; Breast/surgery ; Breast Neoplasms/diagnosis ; Breast Neoplasms/surgery ; Carcinoma, Intraductal, Noninfiltrating/diagnosis ; Carcinoma, Intraductal, Noninfiltrating/surgery ; Female ; Humans ; Middle Aged ; Retrospective Studies
Language	English
Publishing date	2021-05-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	80170-7
ISSN	1095-8673 ; 0022-4804
ISSN (online)	1095-8673
ISSN	0022-4804
DOI	10.1016/j.jss.2021.03.063
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Zs.A 178: Show issues

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Jg. 1995 - 2021: Lesesall (1.OG)
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Article: Impact of Fasting Status on the Use of Klotho as a Biomarker.

Paquette, Jean-Sébastien / Gerard, Ngueta / Djade, Codjo Djignefa / Cadrin-Chênevert, Alexandre / Martel, Elise / Boudreault, Samuel / Pelletier, Mathieu

The journal of applied laboratory medicine

2021 Volume 6, Issue 5, Page(s) 1276–1280

Abstract: Background: Klotho is a protein secreted physiologically in humans. It acts like a hormone that regulates many biological processes. It is also a novel serological biomarker that is increasingly used as a predictive factor for several physiological and ... ...

Abstract	Background: Klotho is a protein secreted physiologically in humans. It acts like a hormone that regulates many biological processes. It is also a novel serological biomarker that is increasingly used as a predictive factor for several physiological and psychological conditions. Surprisingly, there is no consensus about the fasting state of the patient who is tested for klotho. Most studies are done on fasting patients, although others are done without concern about fasting status. There is a lack of evidence about this variable in klotho serological testing. Performing fasting tests on patients can be deleterious and can affect compliance. We investigated the effect of fasting status on klotho serological value. Methods: We conducted an observational study in which klotho serology was evaluated in a fasting state and 2 h after a meal. In total, 35 participants came to the laboratory without having eaten for 10 h. Blood samples were taken on arrival at our laboratory and 2 h after eating a standardized meal. Results: The mean age of our participants was 32.7 years old. There were 13 men and 22 women. In the fasting state, the klotho value was 1060.5 pg/mL (SD: 557.5 pg/mL). At 2 h after the meal, the klotho value was 1077.5 pg/mL (SD: 576.9 pg/mL). Statistical tests showed no difference before and after a meal in our study (P = 0.2425). Conclusions: Our results suggest that it is not necessary to perform klotho serology in a fasting state.
MeSH term(s)	Adult ; Biomarkers ; Fasting ; Female ; Humans ; Male
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-02-03
Publishing country	England
Document type	Journal Article ; Observational Study
ISSN	2576-9456
ISSN	2576-9456
DOI	10.1093/jalm/jfaa234
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Article ; Online: Identifying COVID-19 phenotypes using cluster analysis and assessing their clinical outcomes

Yamga, Eric / Mullie, Louis / Durand, Madeleine / Cadrin-Chenevert, Alexandre / Tang, An / Montagnon, Emmanuel / Chartrand-Lefebvre, Carl / Chassé, Michaël

medRxiv

Abstract: Multiple clinical phenotypes have been proposed for COVID-19, but few have stemmed from data-driven methods. We aimed to identify distinct phenotypes in patients admitted with COVID-19 using cluster analysis, and compare their respective characteristics ... ...

Abstract	Multiple clinical phenotypes have been proposed for COVID-19, but few have stemmed from data-driven methods. We aimed to identify distinct phenotypes in patients admitted with COVID-19 using cluster analysis, and compare their respective characteristics and clinical outcomes. We analyzed the data from 547 patients hospitalized with COVID-19 in a Canadian academic hospital from January 1, 2020, to January 30, 2021. We compared four clustering algorithms: K-means, PAM (partition around medoids), divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 hours of admission to train our algorithm. We then conducted survival analysis to compare clinical outcomes across phenotypes and trained a classification and regression tree (CART) to facilitate phenotype interpretation and phenotype assignment. We identified three clinical phenotypes, with 61 patients (17%) in Cluster 1, 221 patients (40%) in Cluster 2 and 235 (43%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile, but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Mortality, mechanical ventilation and ICU admission risk were all significantly different across phenotypes. We conducted a phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. Further research is needed to determine how to properly incorporate those phenotypes in the management of patients with COVID-19.
Keywords	covid19
Language	English
Publishing date	2022-05-29
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2022.05.27.22275708
Database	COVID19

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Article ; Online: Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Tanguay, William / Acar, Philippe / Fine, Benjamin / Abdolell, Mohamed / Gong, Bo / Cadrin-Chênevert, Alexandre / Chartrand-Lefebvre, Carl / Chalaoui, Jean / Gorgos, Andrei / Chin, Anne Shu-Lei / Prénovault, Julie / Guilbert, François / Létourneau-Guillon, Laurent / Chong, Jaron / Tang, An

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

2022 Volume 74, Issue 2, Page(s) 326–333

Abstract: Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in ... ...

Abstract	Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software.
MeSH term(s)	Humans ; Artificial Intelligence ; Ecosystem ; Canada ; Radiology ; Radiologists ; Software
Language	English
Publishing date	2022-11-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	418190-6
ISSN	1488-2361 ; 0846-5371 ; 0008-2902
ISSN (online)	1488-2361
ISSN	0846-5371 ; 0008-2902
DOI	10.1177/08465371221135760
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Article ; Online: Deep Learning: An Update for Radiologists.

Cheng, Phillip M / Montagnon, Emmanuel / Yamashita, Rikiya / Pan, Ian / Cadrin-Chênevert, Alexandre / Perdigón Romero, Francisco / Chartrand, Gabriel / Kadoury, Samuel / Tang, An

Radiographics : a review publication of the Radiological Society of North America, Inc

2021 Volume 41, Issue 5, Page(s) 1427–1445

Abstract: Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image ... ...

Abstract	Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data. These networks have numerous applications in radiology, particularly in image classification, object detection, semantic segmentation, and instance segmentation. The authors provide an update on a recent primer on deep learning for radiologists, and they review terminology, data requirements, and recent trends in the design of CNNs; illustrate building blocks and architectures adapted to computer vision tasks, including generative architectures; and discuss training and validation, performance metrics, visualization, and future directions. Familiarity with the key concepts described will help radiologists understand advances of deep learning in medical imaging and facilitate clinical adoption of these techniques.
MeSH term(s)	Deep Learning ; Diagnostic Imaging ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Neural Networks, Computer ; Radiologists
Language	English
Publishing date	2021-09-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	603172-9
ISSN	1527-1323 ; 0271-5333
ISSN (online)	1527-1323
ISSN	0271-5333
DOI	10.1148/rg.2021200210
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