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Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy

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

Abstract

Introduction

Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment 18F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy.

Methods

This study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback–Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated.

Results

The mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79–0.94), 0.83 (95%CI 0.71–0.94), and 0.81 (95%CI 0.68–0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68–0.80), 0.74 (95%CI 0.66–0.82), and 0.77 (95%CI 0.69–0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77–0.88), 0.83 (95%CI 0.71–0.94), and 0.80 (95%CI 0.69–0.91) to predict OS.

Conclusion

PET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.

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Funding

This study was funded by the US Public Health Service research grant U01 CA143062 and R01 CA190105 (awarded to Dr. Gillies).

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Authors and Affiliations

  1. Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA

    Wei Mu, Ilke Tunali, Jin Qi & Robert J. Gillies

  2. Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Jhanelle E. Gray & Matthew B. Schabath

  3. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Matthew B. Schabath

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  1. Wei Mu
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Correspondence to Robert J. Gillies.

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Robert James Gillies declared a potential conflict with HealthMyne, Inc. (Investor, Board of Advisors). The remaining authors declare no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board at the University of South Florida (USF) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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

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Mu, W., Tunali, I., Gray, J.E. et al. Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. Eur J Nucl Med Mol Imaging 47, 1168–1182 (2020). https://doi.org/10.1007/s00259-019-04625-9

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