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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

  • Original Article
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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Medicine and Surgery, University of Milan-Bicocca, Monza, Lombardy, Italy

    Lavinia Monaco, Elisabetta De Bernardi, Claudio Landoni, Gregory Mathoux, Elia Anna Turolla, Cristina Messa & Luca Guerra

  2. Centro di Ricerca Interdipartimentale Bicocca Bioinformatics Biostatistics and Bioimaging Centre – B4, Università Degli Studi Di Milano-Bicocca, Milan, Italy

    Elisabetta De Bernardi

  3. Department of Medicine and Surgery, Pathology, ASST Monza, San Gerardo Hospital, University of Milan-Bicocca, Monza, Italy

    Francesca Bono, Vincenzo L’Imperio & Fabio Pagni

  4. Medical Oncology, ASST Monza, San Gerardo Hospital, Monza, Italy

    Diego Cortinovis

  5. Nuclear Medicine Department, ASST Monza, San Gerardo Hospital, Monza, Lombardy, Italy

    Cinzia Crivellaro, Federica Elisei, Claudio Landoni, Monica Musarra & Luca Guerra

  6. Tecnomed Foundation, University of Milan-Bicocca, Milan, Italy

    Elia Anna Turolla

Authors
  1. Lavinia Monaco
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  2. Elisabetta De Bernardi
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  3. Francesca Bono
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  4. Diego Cortinovis
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Contributions

LM ideated the study and wrote the draft of the manuscript; LM, CC, FE, GM, MM, LG, and CL retrospectively evaluated PET/CT scan detecting the volumes of interest (VOIs) for the radiomic analysis; VL, FB, and FP retrospectively collected and evaluated the lung cancer specimens for histological and immunohistochemical characterization; EDB performed the radiomic and statistical analysis; DC provided the clinical data of the cohort; LG, CM, and EAT supervised the whole project and the work and contributed to the final version of the manuscript; all the authors critically reviewed and approved the final version of the present paper.

Corresponding author

Correspondence to Lavinia Monaco.

Ethics declarations

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of ASST Monza—Unimib (3865).

Competing interests

The authors declare no competing interests.

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Lavinia Monaco and Elisabetta De Bernardia are Co-first authors

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

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Monaco, L., De Bernardi, E., Bono, F. et al. 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. Eur J Nucl Med Mol Imaging 49, 3401–3411 (2022). https://doi.org/10.1007/s00259-022-05783-z

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  • DOI: https://doi.org/10.1007/s00259-022-05783-z

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