MRI Research Amsterdam UMC

LEOPARD: Longitudinal evaluation of an MRI-based prediction algorithm for early assessment of treatment response in depression

Major depressive disorder (MDD) is a highly prevalent condition worldwide. It is associated
with increased morbidity and mortality. Symptoms include depressed mood lasting more
than 2 weeks, emotional distress, functional impairment, health problems, and suicide. MDD
is the leading cause of disability10 resulting in a high socioeconomic burden.
Although MDD typically has a relatively good response to antidepressants (ADs), only about
one third of the patients show significant symptom relief in response to the initial treatment4
and 50% have not found an efficacious AD after 1 year. Clinical guidelines recommend 4–8
weeks of treatment before considering an alternate medication in nonresponding patients14.
The guidelines recommend that, if the treatment is ineffective after 1–2 months, a new medication or treatment should be started, after reconsidering the diagnosis. In summary, ineffective pharmacotherapy may cause delay in adequate treatment, persistence of depressive
symp-toms and functional impairment, which could be shortened by better prediction of
therapeutic response. In general guidelines recommend to use a Selective Serotonin
Reuptake Inhibitor (SSRI) as a first step treatment while for a second step treatment a second
SSRI or a Serotonin Norepinephrine Reuptake Inhibitor (SNRI) is often used.
This lengthy process can negatively impact patients’ confidence in pharmacotherapy and reduces treatment adherence. Meanwhile, patients suffer from MDD and might experience serious adverse effects of different drugs without effectively resolving symptoms. adverse effects
include weight gain and insomnia. Thus, a solution is urgently needed that allows faster determination of AD non-response in MDD.
There is growing interest in the development of precision medicine algorithms with the aim of
tailoring treatment strategies to individual patients according to unique biological signatures.
This biomarker-based approach to precision prescribing has the potential to improve
therapeu-tic response, minimize adverse reactions, and by stopping ineffective drugs as early
as possi-ble reduce time to symptomatic relief. Personalized medicine is already
revolutionizing can-cer treatment, in which treatments are tailored to a tumor’s genomic
The application of personalized medicine to psychiatry, however, is more challenging. In contrast to cancer, there is no biological or histological test for definitive psychiatric diagnoses,
because of the inaccessibility of the human brain and the complexity of the link between biology and psychiatric symptoms. For example, the diagnosis of MDD is based on a combination
of symptoms alone, by standard nosology, as reflected in diagnostic manuals, such as the
DSM or the International Classification of Diseases, which does not incorporate any biologi-cal
dimension, nor can guide any treatment selection.
The National Institute of Mental Health (NIMH)’s Research Domain Criteria emphasize biomarker discovery as a clinical research priority by articulating an approach to the
integration of biological and clinical data. The emerging field of psychoradiology, pioneered
by Gong and colleagues17 aims to provide biomarkers based on objective tests in support of
the diag-nostic classifications, as in other parts of medicine. Biomarkers derived from
neuroimaging data are potentially important contributors to the goal of guiding treatment
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selection using clinical and biotyping data. Because of its non-invasive nature, it has great
potential to revolu-tionize clinical psychiatry. Information on brain structure and function may
be used to predict non-response versus response to various treatments. Properties predictive
of treatment re-sponse presented in literature include pre-treatment brain volumes, posttreatment chang-es in regional morphology, gray and white matter patterns at baseline,
presence of increase of subcortical white matter hyperdensities (WMH), lowered DTI
measures of fractional anisotropy (FA) and mean diffusivity (MD)9, and baseline and regional
changes in resting-state functional connectivity (RSFC). Reviews on this topic are available by
Fonseka and colleagues and recently by us.
In DEPREDICT, we will develop a radiomics-based algorithm that allows early (within 2 weeks
after first administration) prediction and / or assessment of the later (non-)response to AD in
patients with MDD. Radiomics is the high-throughput extraction of quantitative fea-tures that
result in the conversion of images into mineable data and the subsequent analysis of these
data for decision support. This is in contrast to the traditional practice of treating medi-cal
images as pictures intended solely for visual interpretation. Radiomic data contain first-,
second-, and higher-order statistics. These data are combined with other patient data and are
mined with sophisticated bioinformatics tools to develop models that may potentially improve
diagnostic, prognostic, and predictive accuracy.
We will first develop the radiomics algorithm based on existing MRI datasets of the brains of
patients with MDD. Whereas existing literature predominantly compares pre-treatment data
between responders and non-responders retrospectively, using a single outcome measure,
DEPREDICT aims to employ advanced radiomics analysis of MRI measurements of the brain
as predictive biomarker in a multivariate predictive solution. There are good indications that
this approach may offer improvement.
The first step towards translation of the DEPREDICT algorithm and put this into psychiatric
practice is to demonstrate reproducibility. Therefore, LEOPARD aims to establish and replicate predictive accuracy of putative biomarkers. The purpose of the LEOPARD study is to test
the effectiveness of the DEPREDICT radiomics algorithm. That is, to determine how successful the algorithm is at predicting who will not, and who will respond to AD he / she is being
treated with, based on quantitative MRI features prior to, and at week 2 of treatment.
Results of LEOPARD will be crucial in defining the strategy of the further development of the
DEPREDICT algorithm for clinical practice. If the algorithm holds sufficient predictive power,
LEOPARD could pave the way for a randomized clinical trial (RCT) aiming to test the benefit of
applying the algorithm (i.e. change medication faster based on DEPREDICT-results) versus
treatment as usual; a final step towards real-world deployment. If proven ef-fective,
deployment of the algorithm could hold for major health and economic benefits.