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Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 10/2022

11.04.2022 | Original Article

The “digital biopsy” in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT

verfasst von: Lavinia Monaco, Elisabetta De Bernardi, Francesca Bono, Diego Cortinovis, Cinzia Crivellaro, Federica Elisei, Vincenzo L’Imperio, Claudio Landoni, Gregory Mathoux, Monica Musarra, Fabio Pagni, Elia Anna Turolla, Cristina Messa, Luca Guerra

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 10/2022

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Abstract

Purpose

The present pilot study investigates the putative role of radiomics from [18F]FDG PET/CT scans to predict PD-L1 expression status in non-small cell lung cancer (NSCLC) patients.

Methods

In a retrospective cohort of 265 patients with biopsy-proven NSCLC, 86 with available PD-L1 immunohistochemical (IHC) assessment and [18F]FDG PET/CT scans have been selected to find putative metabolic markers that predict PD-L1 status (< 1%, 1–49%, and ≥ 50% as per tumor proportion score, clone 22C3). Metabolic parameters have been extracted from three different PET/CT scanners (Discovery 600, Discovery IQ, and Discovery MI) and radiomics features were computed with IBSI compliant algorithms on the original image and on images filtered with LLL and HHH coif1 wavelet, obtaining 527 features per tumor. Univariate and multivariate analysis have been performed to compare PD-L1 expression status and selected radiomic features.

Results

Of the 86 analyzed cases, 46 (53%) were negative for PD-L1 IHC, 13 (15%) showed low PD-L1 expression (1–49%), and 27 (31%) were strong expressors (≥ 50%). Maximum standardized uptake value (SUVmax) demonstrated a significant ability to discriminate strong expressor cases at univariate analysis (p = 0.032), but failed to discriminate PD-L1 positive patients (PD-L1 ≥ 1%). Three radiomics features appeared the ablest to discriminate strong expressors: (1) a feature representing the average high frequency lesion content in a spherical VOI (p = 0.009); (2) a feature assessing the correlation between adjacent voxels on the high frequency lesion content (p = 0.004); (3) a feature that emphasizes the presence of small zones with similar grey levels inside the lesion (p = 0.003). The tri-variate linear discriminant model combining the three features achieved a sensitivity of 81% and a specificity of 82% in the test. The ability of radiomics to predict PD-L1 positive patients was instead scarce.

Conclusions

Our data indicate a possible role of the [18F]FDG PET radiomics in predicting strong PD-L1 expression; these preliminary data need to be confirmed on larger or single-scanner series.
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Metadaten
Titel
The “digital biopsy” in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT
verfasst von
Lavinia Monaco
Elisabetta De Bernardi
Francesca Bono
Diego Cortinovis
Cinzia Crivellaro
Federica Elisei
Vincenzo L’Imperio
Claudio Landoni
Gregory Mathoux
Monica Musarra
Fabio Pagni
Elia Anna Turolla
Cristina Messa
Luca Guerra
Publikationsdatum
11.04.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 10/2022
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-022-05783-z

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