Publications by authors named "Vallieres M"

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason   8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients.

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Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.

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Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies.

Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights.

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Surface chemistry plays an important role in the indoor environment owing to the large indoor surface to volume ratio. This study explores the photoreactivity of surfaces painted with a photoactive paint in the presence of NO. Two types of experiments are performed; illumination of painted surfaces with a nitrate deposit and illumination of painted surfaces in the presence of gaseous NO.

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By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables.

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This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images.

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Article Synopsis
  • Machine learning can work well, but it often struggles to make accurate predictions on new data, which is called out-of-sample generalizability.
  • To solve this problem, researchers are using a method called Federated ML that allows computers to share information about how well they're learning without actually sharing the data itself.
  • In a big study with 71 locations around the world, scientists created a model to help detect brain tumors more accurately, showing a significant improvement compared to older methods and hoping to help with rare illnesses and data sharing in healthcare.
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Carbon monoxide (CO) is the second most abundant identified product of dissolved organic matter (DOM) photodegradation after CO, but its formation mechanism remains unknown. Previous work showed that aqueous photodegradation of methoxy-substituted aromatics (ArOCH) produces CO considerably more efficiently than aromatic carbonyls. Following on this precedent, we propose that the methoxy aromatic groups of lignin act as the C source for the photochemical formation of CO from terrestrial DOM via a two-step pathway: formal hydrolytic demethylation to methanol and methanol oxidation to CO.

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Introduction: There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain.

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Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma.

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Background And Purpose: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

Materials And Methods: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma ( = 113).

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A vast majority of studies in the radiomics field are based on contours originating from radiotherapy planning. This kind of delineation ( Gross Tumor Volume, GTV) is often larger than the true tumoral volume, sometimes including parts of other organs ( trachea in Head and Neck, H&N studies) and the impact of such over-segmentation was little investigated so far. In this paper, we propose to evaluate and compare the performance between models using two contour types: those from radiotherapy planning, and those specifically delineated for radiomics studies.

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Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology.

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This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images.

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Microscopic energy deposition distributions from ionizing radiation vary depending on biological target size and are used to predict the biological effects of an irradiation. Ionizing radiation is thought to kill cells or inhibit the cell cycle mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients.

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Purpose: Radiomic texture calculation requires discretizing image intensities within the region-of-interest. FBN (fixed-bin-number), FBS (fixed-bin-size) and FBN and FBS with intensity equalization (FBNequal, FBSequal) are four discretization approaches. A crucial choice is the voxel intensity (Hounsfield units, or HU) binning range.

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Background: People with Alzheimer disease and related dementias often display disruptive behaviors (eg, aggression, wandering, and restlessness), which increase family caregivers' burden of care. However, there are few tools currently available to help these caregivers manage disruptive behaviors. Mobile apps could meet this need, but to date little is known about them.

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Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.

Materials And Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients.

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The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data.

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Purpose: Both CT and PET radiomics is considered as a potential prognostic biomarker in head and neck cancer. This study investigates the value of fused pre-treatment functional imaging (18F-FDG PET/CT) radiomics for modeling of local recurrence of head and neck cancers.

Materials And Methods: Firstly, 298 patients have been divided into a training set (n = 192) and verification set (n = 106).

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Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment.

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Introduction Lack of access to mental health services for persons who are deaf and communicate in LSQ and who present mental health issues has been noted by service providers. However, very few studies have examined the needs of this population in Quebec. Objective The purpose of this study was to explore the needs with regards to services of persons who are deaf and communicate in LSQ and who have mental health issues.

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Unlabelled: Adjuvant radiotherapy after prostatectomy was recently challenged by early salvage radiotherapy, which highlighted the need for biomarkers to improve risk stratification. Therefore, we developed an MRI ADC map-derived radiomics model to predict biochemical recurrence (BCR) and BCR-free survival (bRFS) after surgery. Our goal in this work was to externally validate this radiomics-based prediction model.

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Unlabelled: Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making.

Purpose: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma.

Study Type: Retrospective.

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