Publications

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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
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 … (voir 26 de plus)
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 … (voir plus)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 (publié)
doi.org
The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review
Mohamed Elahmedi
Riya Sawhney
Elena Guadagno
Fabio Botelho
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (publié)
doi.org
The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review.
Mohamed Elahmedi
Riya Sawhney
Elena Guadagno
Fabio Botelho
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (publié)
doi.org
Transposable elements regulate thymus development and function
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Côté
Juliette Humeau
Eric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen R. Richie
Sébastien Lemieux
Pierre Thibault
Claude Perreault
2024-02-01
bioRxiv (prépublication)
doi.org
Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis.
Waseem Abu-Ashour
Sherif Emil
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (publié)
doi.org