(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.
The effects of cardiopulmonary resuscitation (CPR) on patients with advanced cancer remain to be elucidated. We identified a cohort of patients with stage-IV cancer who received in-hospital CPR from the Taiwan Cancer Registry and National Health Insurance claims database, along with a matched cohort without cancer who also received in-hospital CPR. The main outcomes were post-discharge survival and in-hospital mortality. In total, 3,446 stage-IV cancer patients who underwent in-hospital CPR after cancer diagnosis were identified during January 2009–June 2014. A vast majority of the patients did not survive to discharge (n = 2,854, 82.8%). The median post-discharge survival was 22 days; 10.1% (n = 60; 1.7% of all patients) of the hospital survivors received anticancer therapy after discharge. We created 1:1 age–, sex–, Charlson comorbidity index (CCI)–, and year of CPR–matched noncancer and stage-IV cancer cohorts (n = 3,425 in both; in-hospital mortality rate = 82.1% and 82.8%, respectively). Regression analysis showed that the stage-IV cancer cohort had shorter post-discharge survival than did the noncancer cohort. The outcome of patients with advanced cancer was poor. Even among the survivors, post-discharge survival was short, with only few patients receiving further anticancer therapy.
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