Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites.
Publication Title
Metabolites
Document Type
Article
Publication Date
10-7-2023
Keywords
lung cancer; machine learning; metabolites; california; pni
Abstract
We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.
Clinical Institute
Neurosciences (Brain & Spine)
Clinical Institute
Cancer
Specialty/Research Institute
Oncology
Specialty/Research Institute
Pulmonary Medicine
DOI
10.3390/metabo13101055
