TY - JOUR
T1 - FDG-PET metrics in advanced non-small cell lung cancer (NSCLC)
T2 - a review and meta-analysis
AU - Berkowitz, Aviva C.
AU - Halmos, Balazs
AU - Cheng, Haiying
AU - Huntzinger, Cal
AU - Ohri, Nitin
N1 - Funding Information:
This work was supported by RefleXion Medical, Inc. (Hayward, CA, USA).
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Italian Association of Nuclear Medicine and Molecular Imaging.
PY - 2023
Y1 - 2023
N2 - Purpose: To provide a systematic review and meta-analysis of published literature characterizing the prognostic value of pre-treatment, volume-based FDG-PET metrics in patients with advanced NSCLC. Methods: We conducted a systematic PubMed search to identify studies describing the prognostic value of volume-based PET metrics (total metabolic tumor volume [MTV] and/or total lesion glycolysis [TLG]) obtained prior to initiation of first-line systemic therapy for advanced NSCLC. Clinical endpoints examined were progression-free survival (PFS) and overall survival (OS). Hazard ratios for PFS and OS were taken directly from the original reports when available or extracted from survival curves. Inverse variance meta-analyses were performed to assess associations between PET metrics and clinical outcomes. Results: Thirteen publications including 1,047 patients were included in our analysis. Patients from at least 9 studies received chemotherapy, at least 4 studies utilized targeted therapy, and only 1 study included patients treated with immunotherapy. Random effects models demonstrated that high MTV is significantly associated with inferior PFS (HR 2.97, 95% CI 2.21–4.00, p < 0.001) and inferior OS (HR 2.73, 95% CI 2.18–3.41, p < 0.001). High TLG is also significantly associated with inferior PFS (HR 2.13, 95% CI 1.56–2.91, p < 0.001) and inferior OS (HR 2.06, 95% CI 1.75–2.44, p < 0.001). Conclusion: Baseline PET metrics are powerful prognostic factors for advanced NSCLC patients who are treated with chemotherapy or targeted therapy. Further examination of the prognostic value of PET metrics for patients who receive first-line immunotherapy is warranted.
AB - Purpose: To provide a systematic review and meta-analysis of published literature characterizing the prognostic value of pre-treatment, volume-based FDG-PET metrics in patients with advanced NSCLC. Methods: We conducted a systematic PubMed search to identify studies describing the prognostic value of volume-based PET metrics (total metabolic tumor volume [MTV] and/or total lesion glycolysis [TLG]) obtained prior to initiation of first-line systemic therapy for advanced NSCLC. Clinical endpoints examined were progression-free survival (PFS) and overall survival (OS). Hazard ratios for PFS and OS were taken directly from the original reports when available or extracted from survival curves. Inverse variance meta-analyses were performed to assess associations between PET metrics and clinical outcomes. Results: Thirteen publications including 1,047 patients were included in our analysis. Patients from at least 9 studies received chemotherapy, at least 4 studies utilized targeted therapy, and only 1 study included patients treated with immunotherapy. Random effects models demonstrated that high MTV is significantly associated with inferior PFS (HR 2.97, 95% CI 2.21–4.00, p < 0.001) and inferior OS (HR 2.73, 95% CI 2.18–3.41, p < 0.001). High TLG is also significantly associated with inferior PFS (HR 2.13, 95% CI 1.56–2.91, p < 0.001) and inferior OS (HR 2.06, 95% CI 1.75–2.44, p < 0.001). Conclusion: Baseline PET metrics are powerful prognostic factors for advanced NSCLC patients who are treated with chemotherapy or targeted therapy. Further examination of the prognostic value of PET metrics for patients who receive first-line immunotherapy is warranted.
KW - Advanced Non-Small Cell Lung Cancer
KW - FDG-PET
KW - Metabolic Tumor Volume (MTV)
KW - Total Lesion Glycolysis (TLG)
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U2 - 10.1007/s40336-023-00542-y
DO - 10.1007/s40336-023-00542-y
M3 - Article
AN - SCOPUS:85147672226
SN - 2281-5872
JO - Clinical and Translational Imaging
JF - Clinical and Translational Imaging
ER -