2025 Targeted Therapies of Lung Cancer (TTLC) - Posters-PP01.12 Characterizing PET-CT Avidity and Immune Checkpoint Inhibitor-Related Pulmonary Adverse Events in NSCLC Patients

PP01.12 Characterizing PET-CT Avidity and Immune Checkpoint Inhibitor-Related Pulmonary Adverse Events in NSCLC Patients

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The study, conducted by Northwestern University, investigates the effects of immune checkpoint inhibitors (ICIs) on non-small-cell lung cancer (NSCLC) patients, particularly focusing on PET-CT avidity and associated pulmonary adverse events. ICIs, while effective in treating NSCLC, can lead to immune-related adverse events (IRAEs), including new mediastinal and hilar lymphadenopathy with increased standardized uptake values (SUVs) on PET-CT scans. This study aims to characterize these changes and their potential etiologies to improve diagnosis and treatment.<br /><br />The research is based on a retrospective chart review of 119 NSCLC patients who received ICIs between March 2022 and August 2023 at an urban tertiary referral center. It primarily measures the incidence of new PET avidity within six months post-ICI therapy. Etiologies for PET avidity were determined through various diagnostic tools, including tissue samples and imaging studies.<br /><br />Findings revealed that 22.7% of patients developed new PET avidity, with an observed median SUV increase. Bronchoscopies were performed in 44.4% of these cases, identifying a granulomatous response in three patients, pneumonitis in two, and cancer recurrence in seven. The study also noted a correlation between PD-L1 expression levels and risk of PET avidity.<br /><br />Importantly, the study concluded that granular understanding of PET-avid lymphadenopathy post-ICI is crucial for accurate diagnosis and treatment, avoiding unnecessary interventions. It suggests that granulomatous responses might be underreported and advises the development of predictive models incorporating factors like tumor mutational burden (TMB), PD-L1 expression, and SUV changes to better determine lymphadenopathy etiologies. This predictive model could improve clinical management by distinguishing between IRAEs and disease progression.

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Khyati Dasika

Keywords

immune checkpoint inhibitors

non-small-cell lung cancer

PET-CT avidity

pulmonary adverse events

immune-related adverse events

lymphadenopathy

PD-L1 expression

granulomatous response

tumor mutational burden

predictive models