Metallomic Classification of Pulmonary Nodules Using Blood by Deep-Learning-Boosted Synchrotron Radiation X-ray Fluorescence.

ABSTRACT: Ambient air pollution is an important contributor to increasing cases of lung cancer, which is a malignant cancer with the highest mortality among all cancers. It primarily manifests in the form of pulmonary nodules, but not all will develop into lung cancer. Therefore, it is highly desired to distinguish between benign and malignant pulmonary nodules for the early prevention and treatment of lung cancer. Currently, histopathological examination is the gold standard for classifying pulmonary nodules, which is invasive, time-consuming, and labor-intensive. This study proposes a metallomics approach through synchrotron radiation X-ray fluorescence (SRXRF) with a simplified one-dimensional convolutional neural network (1DCNN) to distinguish pulmonary nodules by using serum samples. SRXRF spec

SUBMITTER: Wei C 

PROVIDER: S-EPMC11744391 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature