Data analysis
lung cancer, cancer, malignancy, cancer stage
REGRESSION, CANCER, PATHOLOGY, LUNG CANCER
Each subject was assigned a datasheet containing raw fluorescent readings of the plate wells as a function of time. The fluorescent readings were transformed into values which correlate with the acidity of the sample. We modeled the biological progression of the immunological response and extracted a set of informative features for use in our assay. Machine Learning (ML) models were trained using logistic regression implemented by the publicly available scikit-learn Python library. For cross-validation, a stratified fivefold split was used. We determined the decision boundary by choosing the point on the receiver operating characteristic (ROC) curve with the highest Euclidean distance from the main diagonal. Repeated evaluations with different random, cross validation train/test splits were performed to verify result stability. Our cancer reference method is based on the results from tissue biopsy pathology of lung nodules, which determines whether there is a malignancy, and the lung cancer type. The cancer stage is determined by a physician specialist based on defined medical criteria.