Corrigendum to “eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer” [Lung Cancer 160 (2021) 36–43] (Lung Cancer (2021) 160 (36–43), (S0169500221004827), (10.1016/j.lungcan.2021.07.017))

The authors regret that absolute sensor differences were not calculated “by subtracting sensor values measured after six weeks of treatment from sensor values measured at baseline for each sensor”, as mentioned in the “Statistical analysis” section, page 3 of the Supplementary appendix, but “by subtracting sensor values measured at baseline from sensor values measured after six weeks of treatment for each sensor”. The authors would like to apologise for any inconvenience caused

eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer

Objectives: Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. Materials and Methods: This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. Results: In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89–1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91–1.00). Conclusion: Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy