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.

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient’s entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient’s tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

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".

Virtual pathology education has shown to enhance the students' learning experience. At the Radboud University, an E‐learning platform—called the “PathoDiscovery”—was developed and first used in a course about neoplasm development amongst first year (bio)medical sciences students. The PathoDiscovery incorporates high‐power microscopic images, histological annotations, interactive questions and pre‐programmed feedback.The objective of our study was to develop and evaluate the PathoDiscovery within the “Neoplasm” course focusing on student perceptions of usability and utility. For this study the online feedback on the PathoDiscovery that was obtained anonymously from (bio)medical students over two consecutive academic years was analyzed. The responses of the first year were used to make improvements. After the second year, the feedback of the two academic years was compared. The rating of the E‐learning increased from 6.8 (n = 285) to 7.4 (n = 247) after implementation of feedback obtained in the first year. The students judged the structure as logical (90%). The content was considered easy or just right (57%), matched the learning objectives (76%), and contributed to knowledge development (78%). We conclude that the first experiences with the PathoDiscovery are positive for both students and lecturers; it is an example of a dynamic online learning tool that is easily adaptable and is well suited for a blended learning approach.