PURPOSE: The concept of time toxicity of cancer treatment, defined as proportion of days from physical contact with the health care system, has been suggested as simple, patient-centered measure useful for shared decision making, particularly in incurable cancer. We investigated the extent of health care contacts in clinical practice in Danish patients with stage IV lung cancer starting first-line systemic anticancer therapies. METHODS: This is a nationwide cohort study of newly diagnosed patients with stage IV lung cancer in Denmark who initiated treatment during 2019-2021 and followed for up to 1 year. The time toxicity after treatment initiation was calculated as the proportion and mean cumulative number of days with physical health care system contacts recorded in Danish registries. The remaining days without any physical contact were defined as home days. One-year cumulative mortality was also assessed. RESULTS: We included 4,384 patients with stage IV lung cancer. One year survival was 45% after treatment initiation. Of days alive, the mean cumulative number of days with physical health care contacts was 56 days within 1 year. The corresponding number of home days was 198. Overall, 22% of days alive involved physical contact with the health care system, broadly similar for patients with non–small cell lung cancer (22%) and small cell lung cancer (24%). For specific regimens, the corresponding proportions were chemotherapy (24%), immunotherapy (21%), immunochemotherapy (21%), and targeted therapy (16%). CONCLUSION: More than 1 in 5 days after initiation of systemic treatment for metastatic lung cancer was spent in physical contact with the health care system. This information may aid shared decision making by informing about expected burdens in relation to cancer therapy.

Background: Symptomatic radiation pneumonitis (RP) may be a serious complication after thoracic radiation therapy (RT) for non-small cell lung cancer (NSCLC). This prospective observational study sought to evaluate the utility of a novel radiation-induced lung injury (RILI) grading scale (RGS) for the prediction of RP. Materials and methods: Data of 41 patients with NSCLC treated with thoracic RT of 60–66 Gy were analysed. CT scans were scheduled before RT, one month post-RT, and every three months thereafter for one year. Symptomatic RP was defined as Common Terminology Criteria for Adverse Events grade ≥ 2. RGS grading ranged from 0 to 3. The inter-observer variability of the RGS was assessed by four senior radiologists. CT scans performed 28 ± 10 days after RT were used to analyse the predictive value of the RGS. The change in the RGS severity was correlated to dosimetric parameters. Results: The CT obtained one month post-RT showed RILI in 36 (88%) of patients (RGS grade 0 [5 patients], 1 [25 patients], 2 [6 patients], and 3 [5 patients]). The inter-observer agreement of the RGS grading was high (Kendall’s W coefficient of concordance = 0.80, p < 0.01). Patients with RGS grades 2–3 had a significantly higher risk for development of RP (relative risk (RR): 2.4, 95% CI 1.6–3.7, p < 0.01) and RP symptoms within 8 weeks after RT (RR: 4.8, 95% CI 1.3–17.6, p < 0.01) compared to RGS grades 0–1. The specificity and sensitivity of the RGS grades 2–3 in predicting symptomatic RP was 100% (95% CI 80.5–100%) and 45.4% (95% CI 24.4–67.8%), respectively. Increase in RGS severity correlated to mean lung dose and the percentage of the total lung volume receiving 5 Gy. Conclusions: The RGS is a simple radiologic tool associated with symptomatic RP. A validation study is warranted.

PURPOSE: Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration. MATERIALS AND METHODS: Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated. RESULTS: The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network–based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death. CONCLUSION: This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.