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

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 spectra of serum samples were obtained and preliminarily analyzed using principal component analysis (PCA). Subsequently, machine learning algorithms (MLs) and 1DCNN were applied to develop classification models. Both MLs and 1DCNN based on full-channel spectra could distinguish patients with benign and malignant pulmonary nodules, but the highest accuracy rate of 96.7% was achieved when using 1DCNN. In addition, it was found that characteristic elements in serum from patients with malignant nodules were different from those in benign nodules, which can serve as the fingerprint metallome profile. The simplified model based on characteristic elements resulted in good performance of sensitivity and F1-score > 91.30%, G-mean, MCC and Kappa > 85.59%, and accuracy = 94.34%. In summary, metallomic classification of benign and malignant pulmonary nodules using serum samples can be achieved through 1DCNN-boosted SRXRF, which is easy to handle and much less invasive compared to histopathological examination.