Martin Vallières

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… (voir plus)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 (publié)

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

Jing Wu

Alvin C. Silva

Paul J. Zhang

Harrison X. Bai

Jan Seuntjens

Steffen Löck

Patrick O. Richard

Olivier Morin

Caroline Reinhold

Martin Lepage … (voir 1 de plus)

Martin Vallières

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational r… (voir plus)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 (prépublication)

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 … (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

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 … (voir 40 de plus)

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 (publié)

doi.org

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge

Vincent Andrearczyk

Valentin Oreiller

Sarah Boughdad

Catherine Cheze Le Rest

Olena Tankyevych

Hesham M. Elhalawani

Mario Jreige

John O. Prior

Martin Vallières

Dimitris Visvikis

Mathieu Hatt

Adrien Depeursinge

2023-12-01

Medical Image Analysis (publié)

doi.org

Electric Power Fuse Identification With Deep Learning

Simon Giard-Leroux

Guillaume Cléroux

Shreyas Sunil Kulkarni

François Bouffard

Martin Vallières

As part of arc flash studies, survey pictures of electrical installations need to be manually analyzed. A challenging task is to identify fu… (voir plus)se types, which can be determined from physical characteristics, such as shape, color, and size. To automate this process using deep learning techniques, a new dataset of fuse pictures from past arc flash projects and data from the web was created. Multiple experiments were performed to train a final model, reaching an average precision of 91.06% on the holdout set, which confirms its potential for identification of fuse types in new photos. By identifying fuse types using physical characteristics only, the need to take clear pictures of the label text is eliminated, allowing pictures to be taken away from danger, thereby improving the safety of workers. All the resources needed to repeat the experiments are openly accessible, including the code and datasets.

2023-11-01

IEEE Transactions on Industrial Informatics (publié)

doi.org

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Vincent Andrearczyk

Valentin Oreiller

Moamen A. Abobakr

Azadeh Akhavanallaf

Panagiotis Balermpas

Sarah Boughdad

Leo Capriotti

Joel Castelli

Catherine Cheze Le Rest

Pierre Decazes

Ricardo Correia

D. El-Habashy

Hesham M. Elhalawani

C. Fuller

Mario Jreige

Yomna Khamis

Agustina La Greca Saint-Esteven

A. Mohamed

M. Naser

John O. Prior … (voir 11 de plus)

Su Ruan

Stephanie Tanadini-Lang

Olena Tankyevych

Yazdan Salimi

Martin Vallières

Pierre Véra

Dimitris Visvikis

K. Wahid

Habib Zaidi

Mathieu Hatt

Adrien Depeursinge

2023-03-18

Lecture Notes in Computer Science (publié)

doi.org

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Simon A. Keek

Manon Beuque

Sergey Primakov

Henry C. Woodruff

Avishek Chatterjee

Janita E. van Timmeren

Martin Vallières

Lizza E. L. Hendriks

Johannes Kraft

Nicolaus Andratschke

Steve E. Braunstein

Olivier Morin

Philippe Lambin

Introduction There is a cumulative risk of 20–40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) en… (voir plus)ables 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. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.

2022-07-13

Frontiers in Oncology (publié)

doi.org

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma

Elizabeth George

Elizabeth Flagg

Kuan-chun Chang

H. Bai

H. Aerts

Martin Vallières

D. Reardon

R.Y. Huang

BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-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 (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

2022-04-28

American Journal of Neuroradiology (publié)

doi.org

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma

Elizabeth George

Elizabeth Flagg

Kuan-chun Chang

H. Bai

H. Aerts

Martin Vallières

D. Reardon

R.Y. Huang

BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-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 (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

2022-04-28

American Journal of Neuroradiology (published)

doi.org

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Martin Vallières

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Martin Vallières

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