Detection of Lung Cancer through Metabolic Changes Measured in Blood Plasma

Introduction: Low-dose computed tomography, the currently used tool for lung cancer screening, is characterized by a high rate of false-positive results. Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. Therefore, this study aims to evaluate whether the metabolic phenotype of blood plasma allows detection of lung cancer. Methods: The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 233 patients with lung cancer and 226 controls. The validity of the model was examined by classifying an independent cohort of 98 patients with lung cancer and 89 controls. Results: The model makes it possible to correctly classify 78%ofpatientswithlungcancerand92%ofcontrols,withan area under the curve of 0.88. Important moreover is the fact that the model is convincing, which is demonstrated by validation in the independent cohort with a sensitivity of 71%,aspecificityof81%,andanareaunderthecurveof0.84. Patients with lung cancer have increased glucose and decreased lactate and phospholipid levels. The limited numberofpatientsinthesubgroupsandtheirheterogeneous