Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer

Abstract

Purpose To study the relationship between standardized 18F-FDG PET/CT radiomic features and clinicopathological variables and programmed death ligand-1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) patients.

Methods 58 NSCLC patients with preoperative 18F-FDG PET/CT scans and postoperative results of PD-L1 expression were retrospectively analysed. A standardized, open-source software was used to extract 86 radiomic features from PET and low-dose CT images. Univariate analysis and multivariate logistic regression were used to find independent predictors of PD-L1 expression. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the ability of variables and their combination in predicting PD-L1 expression.

Results Multivariate logistic regression resulted in the PET radiomic feature GLRLM_LGRE (Odds Rate (OR): 0.300 vs 0.114, 95% confidence interval (CI): 0.096–0.931 vs 0.021–0.616, in NSCLC and adenocarcinoma respectively) and the CT radiomic feature GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074–10.375 vs 1.382–40.755, in NSCLC and adenocarcinoma respectively), being independent predictors of PD-L1 status. In NSCLC group, after adjusting for gender and histology, the PET radiomic feature GLRLM_LGRE (OR: 0.282, 95%CI: 0.085–0.936) remained an independent predictor for PD-L1 status. In the adenocarcinoma group, when adjusting for gender the PET radiomic feature GLRLM_LGRE (OR: 0.115, 95%CI: 0.021–0.631) and the CT radiomic feature GLZLM_SZE (OR: 7.343, 95%CI: 1.285–41.965) remained associated with PD-L1 expression.

Conclusion NSCLC and adenocarcinoma with PD-L1 expression show higher tumour heterogeneity. Heterogeneity-related 18F-FDG PET and CT radiomic features showed good ability to non-invasively predict PD-L1 expression.

Zusammenfassung

Ziel Untersuchung der Beziehung zwischen standardisierten 18F-FDG-PET/CT-Radiomics-Merkmalen, klinisch-pathologischen Variablen und der Expression des Programmed Death Ligand 1 (PD-L1) bei Patienten mit nichtkleinzelligem Bronchialkarzinom (non-small cell lung cancer, NSCLC).

Material und Methoden 58 NSCLC-Patienten mit präoperativen 18F-FDG-PET/CT-Scans und postoperativen Befunden zur PD-L1-Expression wurden retrospektiv analysiert. Eine standardisierte Open-Source-Software wurde verwendet, um 86 Radiomics-Merkmale aus PET- und Niedrigdosis-CT-Bildern zu extrahieren. Univariate Analyse und multivariate logistische Regression wurden verwendet, um unabhängige Prädiktoren für die PD-L1-Expression zu ermitteln. Die Fläche unter der Kurve (AUC) der Receiver-Operating-Characteristic (ROC) -Kurve wurde verwendet, um die Fähigkeit der Variablen und ihrer Kombination bei der Vorhersage der PD-L1-Expression zu vergleichen.

Ergebnisse Die multivariate logistische Regression ergab das PET-Radiomics-Merkmal GLRLM_LGRE (Odds Ratio (OR): 0,300 vs. 0,114, 95%-Konfidenzintervall (KI): 0,096–0,931 vs. 0,021–0,616, bei NSCLC bzw. Adenokarzinom) und das CT-Radiomics-Merkmal GLZLM_SZE (OR: 3,338 vs. 7,504, 95%-KI: 1,074–10,375 vs. 1,382–40,755, bei NSCLC bzw. Adenokarzinom), wobei es sich um unabhängige Prädiktoren für den PD-L1-Status handelt.

In der NSCLC-Gruppe blieb das PET-Radiomics-Merkmal GLRLM_LGRE (OR: 0,282, 95%-KI: 0,085–0,936) nach Berücksichtigung von Geschlecht und Histologie ein unabhängiger Prädiktor für den PD-L1-Status. In der Adenokarzinom-Gruppe waren nach Berücksichtigung des Geschlechts das PET-Radiomics-Merkmal GLRLM_LGRE (OR: 0,115, 95%-KI: 0,021–0,631) und das CT-Radiomics-Merkmal GLZLM_SZE (OR: 7,343, 95%-KI: 1,285–41,965) mit der PD-L1-Expression assoziiert.

Schlussfolgerung NSCLC und Adenokarzinome mit PD-L1-Expression zeigen eine höhere Tumorheterogenität. Heterogenitätsbezogene 18F-FDG-PET- und CT-Radiomics-Merkmale zeigten eine gute Fähigkeit zur nichtinvasiven Vorhersage der PD-L1-Expression.

Schlüsselwörter

PD-L1 - 18F-FDG - PET - PET/CT - NSCLC - Radiomics

Keywords

PD-L1 - 18F-FDG - PET - PET/CT - NSCLC - radiomics

^* The present work was performed in (partial) fulfillment of the requirements for obtaining the degree „Dr. med.“/„Dr. med. dent“.

Publication History

Received: 24 February 2022

Accepted after revision: 04 April 2022

Article published online:
29 June 2022

• References

• 1 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 2021; 71: 209-249
• 2 Duma N, Santana-Davila R, Molina JR. Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clinic proceedings 2019; 94: 1623-1640
• 3 Rittmeyer A, Barlesi F, Waterkamp D. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet (London, England) 2017; 389: 255-265
• 4 Reck M, Rodríguez-Abreu D, Robinson AG. et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. The New England journal of medicine 2016; 375: 1823-1833
• 5 Thunnissen E, Kerr KM, Dafni U. et al. Programmed death-ligand 1 expression influenced by tissue sample size. Scoring based on tissue microarrays' and cross-validation with resections, in patients with, stage I-III, non-small cell lung carcinoma of the European Thoracic Oncology Platform Lungscape cohort. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc 2020; 33: 792-801
• 6 Budak E, Çok G, Akgün A. The Contribution of Fluorine (18)F-FDG PET/CT to Lung Cancer Diagnosis, Staging and Treatment Planning. Molecular imaging and radionuclide therapy 2018; 27: 73-80
• 7 Yamaguchi O, Kaira K, Hashimoto K. et al. Tumor metabolic volume by (18)F-FDG-PET as a prognostic predictor of first-line pembrolizumab for NSCLC patients with PD-L1 ≥ 50. Scientific reports 2020; 10: 14990
• 8 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer (Oxford, England: 1990) 2012; 48: 441-446
• 9 Pyka T, Gempt J, Hiob D. et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. European journal of nuclear medicine and molecular imaging 2016; 43: 133-141
• 10 Pyka T, Bundschuh RA, Andratschke N. et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiation oncology (London, England) 2015; 10: 100
• 11 Kim BS, Kang J, Jun S. et al. Association between immunotherapy biomarkers and glucose metabolism from F-18 FDG PET. European review for medical and pharmacological sciences 2020; 24: 8288-8295
• 12 Jiang M, Sun D, Guo Y. et al. Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result. Academic radiology 2020; 27: 171-179
• 13 Vallières M, Zwanenburg A, Badic B. et al. Responsible Radiomics Research for Faster Clinical Translation. Journal of nuclear medicine: official publication, Society of Nuclear Medicine 2018; 59: 189-193
• 14 Zwanenburg A, Leger S, Vallières M. et al. Image biomarker standardisation initiative. arXiv preprint arXiv 2016;
• 15 Schildhaus HU. Predictive value of PD-L1 diagnostics. Der Pathologe 2018; 39: 498-519
• 16 Nioche C, Orlhac F, Boughdad S. et al. LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer research 2018; 78: 4786-4789
• 17 Pawelczyk K, Piotrowska A, Ciesielska U. et al. Role of PD-L1 Expression in Non-Small Cell Lung Cancer and Their Prognostic Significance according to Clinicopathological Factors and Diagnostic Markers. International journal of molecular sciences 2019; 20
• 18 Munari E, Zamboni G, Lunardi G. et al. PD-L1 Expression Heterogeneity in Non-Small Cell Lung Cancer: Defining Criteria for Harmonization between Biopsy Specimens and Whole Sections. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2018; 13: 1113-1120
• 19 D'Incecco A, Andreozzi M, Ludovini V. et al. PD-1 and PD-L1 expression in molecularly selected non-small-cell lung cancer patients. British journal of cancer 2015; 112: 95-102
• 20 Lee SE, Kim YJ, Sung M. et al. Association with PD-L1 Expression and Clinicopathological Features in 1000 Lung Cancers: A Large Single-Institution Study of Surgically Resected Lung Cancers with a High Prevalence of EGFR Mutation. International journal of molecular sciences 2019; 20
• 21 Jin Y, Shen X, Pan Y. et al. Correlation between PD-L1 expression and clinicopathological characteristics of non-small cell lung cancer: A real-world study of a large Chinese cohort. Journal of thoracic disease 2019; 11: 4591-4601
• 22 Galloway MM. Texture analysis using gray level run lengths. Computer graphics image processing 1975; 4: 172-179
• 23 Xu D-H, Kurani AS, Furst JD. et al. Run-length encoding for volumetric texture. Heart (British Cardiac Society) 2004; 27: 452-458
• 24 Nioche C, Orlhac F, Buvat I. Texture – User Guide Local Image Features Extraction. Accessed November 26, 2021 at: https://www.lifexsoft.org/images/phocagallery/documentation/ProtocolTexture/UserGuide/TextureUserGuide.pdf
• 25 Thibault G, Angulo J, Meyer F. Advanced statistical matrices for texture characterization: application to cell classification. IEEE Transactions on Biomedical Engineering 2013; 61: 630-637
• 26 Bracci S, Dolciami M, Trobiani C. et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. La Radiologia medica 2021; 126: 1425-1433
• 27 Fehrenbacher L, Spira A, Ballinger M. et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet (London, England) 2016; 387: 1837-1846
• 28 Herbst RS, Baas P, Kim DW. et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet (London, England) 2016; 387: 1540-1550