Portrait of Martin Vallières

Martin Vallières

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université Sherbrooke, Department of Computer Science
Research Topics
Medical Machine Learning
Website
Google Scholar
LinkedIn
X

Biography

Martin Vallières is an assistant professor in the Department of Computer Science at Université de Sherbrooke and a Canada CIFAR AI Chair since April 2020.

He received a PhD in medical physics from McGill University in 2017, and completed postdoctoral training in France and the U.S. in 2018 and 2019.

Vallières is an expert in the field of radiomics and machine learning in oncology. Over the course of his career, he has developed multiple prediction models for different types of cancers. His main research interest is now focused on the graph-based modelling of heterogeneous medical data for improved precision medicine.

Current Students

Hakima Laribi
PhD - Université de Sherbrooke
Principal supervisor :
Dan Poenaru
Website
Github
Olivier Lefebvre
PhD - Université de Sherbrooke
Principal supervisor :
Dan Poenaru
Github

Publications

Derivation and validation of indices incorporating vasopressor dose and blood pressure values over time
Alain Gervais
François Lamontagne
Jean-Baptiste Michaud
KJ Neill
Adhikari
Jean-Michel Pagé
Marie-Hélène Masse
Michael O Harhay
Michael Chassé
Félix Lamontagne
Katia Laforge
Alexandra Fortin
Marc-André Leclair
Simon Lévesque
Marie-Pier Domingue
Neda Momenzadeh
Martin Vallières
Ruxandra Pinto
Maxime Morin-Lavoie
Francis Carter … (see 2 more)
Félix Camirand Lemyre
MD MSc. François Lamontagne
Rationale The blood pressure value below which the benefits of vasopressors clearly outweigh their disadvantages is uncertain. Objectives Th… (see more)e main objective of this analysis was to investigate the statistical properties and potential utility of indices estimating the vasopressor dose-rates as a function of blood pressure values over time. Methods In this single-center observational study, we collected blood pressure values from intensive care unit (ICU) monitors and norepinephrine dose-rates from infusion pumps corresponding to a derivation and a validation cohort. Patients included in each cohort were 18 years or older and received norepinephrine in the ICU. We defined and derived indices corresponding to vasopressor therapy above (>65 mmHg) and below (60 mmHg) targets. We report the distribution of both indices over time from both cohorts as well as their associations with hospital mortal
2024-09-23
medRxiv (preprint)
doi.org
Derivation and validation of indices incorporating vasopressor dose and blood pressure values over time
Alain Gervais
François Lamontagne
Jean-Baptiste Michaud
KJ Neill
Adhikari
Jean-Michel Pagé
Marie-Hélène Masse
Michael O. Harhay
Michael Chassé
Félix Lamontagne
Katia Laforge
Alexandra Fortin
Marc-André Leclair
Simon Lévesque
Marie-Pier Domingue
Neda Momenzadeh
Martin Vallières
Ruxandra Pinto
Maxime Morin-Lavoie
Francis Carter … (see 2 more)
Félix Camirand Lemyre
MD MSc. François Lamontagne
Rationale The blood pressure value below which the benefits of vasopressors clearly outweigh their disadvantages is uncertain. Objectives Th… (see more)e main objective of this analysis was to investigate the statistical properties and potential utility of indices estimating the vasopressor dose-rates as a function of blood pressure values over time. Methods In this single-center observational study, we collected blood pressure values from intensive care unit (ICU) monitors and norepinephrine dose-rates from infusion pumps corresponding to a derivation and a validation cohort. Patients included in each cohort were 18 years or older and received norepinephrine in the ICU. We defined and derived indices corresponding to vasopressor therapy above (>65 mmHg) and below (60 mmHg) targets. We report the distribution of both indices over time from both cohorts as well as their associations with hospital mortal
2024-09-23
medRxiv (preprint)
doi.org
Development of Error Passing Network for Optimizing the Prediction of VO$_2$ peak in Childhood Acute Leukemia Survivors
Nicolas Raymond
Hakima Laribi
Maxime Caru
Mehdi Mitiche
Valerie Marcil
Maja Krajinovic
Daniel Curnier
Daniel Sinnett
Martin Vallières
Approximately two-thirds of survivors of childhood acute lymphoblastic leukemia (ALL) cancer develop late adverse effects post-treatment. Pr… (see more)ior studies explored prediction models for personalized follow-up, but none integrated the usage of neural networks to date. In this work, we propose the Error Passing Network (EPN), a graph-based method that leverages relationships between samples to propagate residuals and adjust predictions of any machine learning model. We tested our approach to estimate patients’ \vo peak, a reliable indicator of their cardiac health. We used the EPN in conjunction with several baseline models and observed up to 12.16% improvement in the mean average percentage error compared to the last established equation predicting \vo peak in childhood ALL survivors. Along with this performance improvement, our final model is more efficient considering that it relies only on clinical variables that can be self-reported by patients, therefore removing the previous need of executing a resource-consuming physical test.
2024-07-24
Proceedings of the fifth Conference on Health, Inference, and Learning (published)
proceedings.mlr.press
Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling
Mahdi A. L. Loutfi
Teodora Boblea Podasca
Alex Zwanenburg
Taman Upadhaya
Jorge Barrios
David R Raleigh
William C. Chen
Dante P. I. Capaldi
Hong Zheng
Olivier Gevaert
Jinglan Wu
Alvin C. Silva
Paul J. Zhang
Harrison X. Bai
Jan Seuntjens
Steffen Löck
Patrick O. Richard
Olivier Morin
Caroline Reinhold
Martin Lepage … (see 1 more)
Martin Vallières
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational r… (see more)equirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features. Materials and Methods: 89,714 radiomic features were extracted from five cancer datasets: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). Features were categorized by computational complexity into morphological, intensity, texture, linear filters, and nonlinear filters. Models were trained and evaluated on each complexity level using the area under the curve (AUC). The most informative features were identified, and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was developed to facilitate radiomic studies. Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning the Hounsfield unit range improved results for CECT-based renal cell carcinoma (AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the optimal radiomics complexity level for specific medical outcomes, potentially simplifying the use of radiomics in predictive modeling across various contexts.
2024-07-05
ArXiv (preprint)
doi.org
arxiv.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (see 26 more)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical … (see more)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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
2024-02-01
Radiology (published)
doi.org
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Kocak
Tugba Akinci D’Antonoli
Nathaniel Mercaldo
Angel Alberich-Bayarri
Bettina Baessler
Ilaria Ambrosini
Anna E. Andreychenko
Spyridon Bakas
Regina G. H. Beets-Tan
Keno Bressem
Irene Buvat
Roberto Cannella
Luca Alessandro Cappellini
Armando Ugo Cavallo
Leonid L. Chepelev
Linda Chi Hang Chu
Aydin Demircioglu
Nandita M. deSouza
Matthias Dietzel
Salvatore Claudio Fanni … (see 40 more)
Andrey Fedorov
Laure S. Fournier
Valentina Giannini
Rossano Girometti
Kevin B. W. Groot Lipman
Georgios Kalarakis
Brendan S. Kelly
Michail E. Klontzas
Dow-Mu Koh
Elmar Kotter
Ho Yun Lee
Mario Maas
Luis Marti-Bonmati
Henning Müller
Nancy Obuchowski
Fanny Orlhac
Nikolaos Papanikolaou
Ekaterina Petrash
Elisabeth Pfaehler
Daniel Pinto dos Santos
Andrea Ponsiglione
Sebastià Sabater
Francesco Sardanelli
Philipp Seeböck
Nanna M. Sijtsema
Arnaldo Stanzione
Alberto Traverso
Lorenzo Ugga
Martin Vallières
Lisanne V. van Dijk
Joost J. M. van Griethuysen
Robbert W. van Hamersvelt
Peter van Ooijen
Federica Vernuccio
Alan Wang
Stuart Williams
Jan Witowski
Zhongyi Zhang
Alex Zwanenburg
Renato Cuocolo
2024-01-17
Insights into Imaging (published)
doi.org