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

Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort