Brennan Nichyporuk

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Brennan Nichyporuk

Douglas Arnold

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties,...).

2022-12-04

Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)

openreview.net

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Anjun Hu

Jean-Pierre R. Falet

Brennan Nichyporuk

Changjian Shui

Douglas Arnold

Sotirios A. Tsaftaris

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (see more)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

2022-11-15

ArXiv (preprint)

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Brennan Nichyporuk

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, wh… (see more)ere unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.

2022-10-31

ArXiv (preprint)

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2022-09-26

Nature Communications (published)

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

Amar Kumar

Anjun Hu

Brennan Nichyporuk

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

2022-08-03

ArXiv (preprint)

Information Gain Sampling for Active Learning in Medical Image Classification

Raghav Mehta

Changjian Shui

Brennan Nichyporuk

2022-08-01

ArXiv (preprint)

GP.2 Deep learning prediction of response to disease modifying therapy in primary progressive multiple sclerosis

JR Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

M Sormani

D Precup

DL Arnold

Background: Only one disease modifying therapy (DMT), ocrelizumab, was found to slow disability progression in primary progressive multiple … (see more)sclerosis (PPMS). Modeling the conditional average treatment effect (CATE) using deep learning could identify individuals more responsive to DMTs, allowing for predictive enrichment to increase the power of future clinical trials. Methods: Baseline clinical and MRI data were acquired as part of three placebo-controlled randomized clinical trials: ORATORIO (ocrelizumab), OLYMPUS (rituximab) and ARPEGGIO (laquinimod). Data from ORATORIO and OLYMPUS was separated into a training (70%) and testing (30%) set, while ARPEGGIO served as additional validation. An ensemble of multitask multilayer perceptrons was trained to predict the rate of disability progression on both treatment and placebo to estimate CATE. Results: The model could separate individuals based on their predicted treatment effect. The top 25% of individuals predicted to respond most have a larger effect size (HR 0.442, p=0.0497) than the entire group (HR 0.787, p=0.292). The model could also identify responders to laquinimod. A simulated study where the 50% most responsive individuals are randomized would require 6-times less participants to detect a significant effect. Conclusions: Individuals with PPMS who respond favourably to DMTs can be identified using deep learning based on their baseline clinical and imaging characteristics.

2022-06-24

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques (published)

Metrics Reloaded - A new recommendation framework for biomedical image analysis validation

Annika Reinke

Lena Maier-Hein

Evangelia Christodoulou

Ben Glocker

Patrick Scholz

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

Michael Alexander Riegler

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

Ali Emre Kavur

TIM RÄDSCH

Minu D. Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (see 48 more)

Spyridon Bakas

Peter Bankhead

Arriel Benis

M. Jorge Cardoso

Veronika Cheplygina

Beth A Cimini

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Fred A Hamprecht

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

Charles Kahn

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

Hannes Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

Maarten van Smeden

Carole H. Sudre

Ronald M. Summers

Abdel A. Taha

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

Paul F Jaeger

Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There ar… (see more)e major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.

2022-05-09

MIDL.io/2022/Conference/Short (published)

openreview.net

Deep Learning Prediction of Response to Disease Modifying Therapy in Primary Progressive Multiple Sclerosis (P1-1.Virtual)

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2022-05-03

Neurology (published)

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Brennan Nichyporuk

Douglas Arnold

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties).

2022-02-28

MIDL.io/2022/Conference (published)

openreview.net

Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning

JR Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Jan Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2021-11-01

medRxiv (preprint)