Jérémie Guedj

Université Paris Cité
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Articles (18)

Year:

2025

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2025

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2025

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2025

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2024

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2024

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2024

Year:

2023

PURPOSE Several studies have raised the hypothesis that immunotherapy may exacerbate the variability in individual lesions, increasing the risk of observing divergent kinetic profiles within the same patient. This questions the use of the sum of the longest diameter to follow the response to immunotherapy. Here, we aimed to study this hypothesis by developing a model that estimates the different sources of variability in lesion kinetics, and we used this model to evaluate the impact of this variability on survival. METHODS We relied on a semimechanistic model to follow the nonlinear kinetics of lesions and their impact on the risk of death, adjusted on organ location. The model incorporated two levels of random effects to characterize both between- and within-patient variability in response to treatment. The model was estimated on 900 patients from a phase III randomized trial evaluating programmed death-ligand 1 checkpoint inhibitor atezolizumab versus chemotherapy in patients with second-line metastatic urothelial carcinoma (IMvigor211). RESULTS The within-patient variability in the four parameters that characterize individual lesion kinetics represented between 12% and 78% of the total variability during chemotherapy. Similar results were obtained during atezolizumab, except for the durability of the treatment effects, for which the within-patient variability was markedly larger than during chemotherapy (40% v 12%, respectively). Accordingly, the occurrence of divergent profile consistently increased over time in patients treated with atezolizumab and was equal to about 20% after 1 year of treatment. Finally, we show that accounting for the within-patient variability provided a better prediction of most at-risk patients than a model relying solely on the sum of the longest diameter. CONCLUSION Within-patient variability provides valuable information for the assessment of treatment efficacy and the detection of at-risk patients.

Year:

2023

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2023

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2023

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2022

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2021

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2021

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2021

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2021

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2021

Year:

2021

Collaborators (10)

Benoit Visseaux

Université Paris Cité

FRANCE
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france Mentré

INSERM

FRANCE
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Sylvain Baize

INSERM

FRANCE
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Peter Czuppon

Lecturer

University of Münster

GERMANY
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Samuel Alizon

-

FRANCE
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Olivier Tenaillon

-

FRANCE
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Solène Desmée

Université de Tours

FRANCE
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Florence Débarre

CNRS

FRANCE
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Laura Bonil

University of Namur

BELGIUM
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Julie Bertrand

University College London

UNITED KINGDOM
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