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Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach

SIMPLE SUMMARY: Unique responses such as hyperprogressive disease (HPD) and a dissociated response (DR) have been reported after immune checkpoint inhibitor (ICI) treatment, and these patterns are difficult to evaluate with conventional methods. The aim of this study was to evaluate radiomics featur...

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Detalles Bibliográficos
Autores principales: Song, Chorog, Park, Hyunjin, Lee, Ho Yun, Lee, Seunghak, Ahn, Joong Hyun, Lee, Se-Hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657103/
https://www.ncbi.nlm.nih.gov/pubmed/34885160
http://dx.doi.org/10.3390/cancers13236050
Descripción
Sumario:SIMPLE SUMMARY: Unique responses such as hyperprogressive disease (HPD) and a dissociated response (DR) have been reported after immune checkpoint inhibitor (ICI) treatment, and these patterns are difficult to evaluate with conventional methods. The aim of this study was to evaluate radiomics features of HPD at the lesion level, and to understand the clinical significance of a dissociated response. Our study revealed organ-specific radiomics features, likely reflecting the organ-specific microenvironment, that can be used to discriminate HPD. In addition, we observed that a dissociated response was associated with poor overall survival. A radiomic lesion-level approach shows great potential for response evaluation of ICI treatment. ABSTRACT: Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment.