Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer:Comparing Five Machine Learning Approaches

International audiencePURPOSE 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

Fractional exhaled nitric oxide as a potential biomarker for radiation pneumonitis in patients with non-small cell lung cancer:A pilot study

Introduction The aim of the study was to investigate repetitive fractional exhaled nitric oxide (FeNO) measurements during high-dose radiation therapy (HDRT) and to evaluate the use of FeNO to predict symptomatic radiation pneumonitis (RP) in patients being treated for non-small cell lung cancer (NSCLC). Materials and methods A total of 50 patients with NSCLC referred for HDRT were enrolled. FeNO was measured at baseline, weekly during HDRT, one month- and every third month after HDRT for a one-year follow-up period. The mean FeNO(visit 0-6) was calculated using the arithmetic mean of the baseline and weekly measurements during HDRT. Patients with grade ≥ 2 of RP according to the Common Terminology Criteria for Adverse Events (CTCAE) were considered symptomatic. Results A total of 42 patients completed HDRT and weekly FeNO measurements. Grade ≥ 2 of RP was diagnosed in 24 (57%) patients. The mean FeNO(visit 0-6) ± standard deviation in patients with and without RP was 15.0 ± 7.1 ppb (95%CI: 12.0–18.0) and 10.3 ± 3.4 ppb (95%CI: 8.6–11.9) respectively with significant differences between the groups (p = 0.0169, 95%CI: 2.3–2.6). The leave-one-out cross-validated cut-off value of the mean FeNO(visit 0-6) ≥ 14.8 ppb was predictive of grade ≥ 2 RP with a specificity of 71% and a positive predictive value of 78%. Conclusions The mean FeNO(visit 0-6) in patients with symptomatic RP after HDRT for NSCLC was significantly higher than in patients without RP and may serve as a potential biomarker for RP

BAP1 loss by immunohistochemistry predicts improved survival to first-line platinum and pemetrexed chemotherapy for patients with pleural mesothelioma: A validation study

Introduction: Pleural mesothelioma (PM) is an aggressive malignancy with no identified predictive biomarkers. We assessed whether tumor BAP1 status is a predictive biomarker for survival in patients receiving first-line combination platinum and pemetrexed therapy. Methods: PM cases (n = 114) from Aalborg, Denmark, were stained for BAP1 on tissue microarrays. Demographic, clinical, and survival data were extracted from registries and medical records. Surgical cases were excluded. BAP1 status was associated with overall survival (OS) by Cox regression and Kaplan-Meier methods. Results were validated in an independent cohort from Perth, Australia (n = 234). Results: BAP1 loss was found in 62% and 60.3% of all Danish and Australian samples, respectively. BAP1 loss was an independent predictor of OS in multivariate analyses corrected for histological subtype, performance status, age, sex, and treatment (hazard ratio = 2.49, p \u3c 0.001, and 1.48, p = 0.01, respectively). First-line platinum and pemetrexed-treated patients with BAP1 loss had significantly longer median survival than those with retained BAP1 in both the Danish (20.1 versus 7.3 mo, p \u3c 0.001) and Australian cohorts (19.6 versus 11.1 mo, p \u3c 0.01). Survival in patients with BAP1 retained and treated with platinum and pemetrexed was similar as in those with best supportive care. There was a higher OS in patients with best supportive care with BAP1 loss, but it was significant only in the Australian cohort (16.8 versus 8.3 mo, p \u3c 0.01). Conclusions: BAP1 is a predictive biomarker for survival after first-line combination platinum and pemetrexed chemotherapy and a potential prognostic marker in PM. BAP1 in tumor is a promising clinical tool for treatment stratification