Development of the CyPath Lung cancer/non-cancer classifier
non-cancer, cancer, cancer or non-cancer
REGRESSION, CANCER, EVENTS, LUNG
The second stage of the CyPath Lung assay is the analysis of light scatter and fluorescence signals from the viable single cells identified in the first stage by automated FCM. Logistic regression models describe a relationship between predictor variables and a categorical (in our case binary cancer/non-cancer) response variable. We measure our model’s performance by comparing its prediction of whether a sample is cancer or non-cancer (the response variable) to the known cancer/non-cancer status of the samples. Stepwise regression is a supervised machine learning process by which potentially predictive variables are added and removed and the resulting model examined for goodness of fit (see Additional file Patient characteristics of LSRII validation samplesn: number of samples; SD: standard deviationBased on our earlier slide-based assay results, we anticipated that smoking history (or correlated factors like age) and TCPP signal density (as opposed to fluorescence intensity itself) would be important predictors [Model variables and sample quality metrics from FCM. Combinations of lineage markers can identify subpopulations that single markers alone may not capture. Careful examination of patient samples revealed complex patterns of lineage marker expression in blood and epithelial tubes. Although there were differences between the non-cancer and cancer groups [Once we had reduced the list of potential predictors to those that were most promising, we tested for pairwise interactions between them. One interaction proved informative: adding a negative value proportional to [age x number of events in FVS510-A/log