Does statistical learning confirm the diagnostic significance of the anemia panel in breast cancer?
Inclusion of tests for anemia significantly improves diagnostic accuracy for the breast cancer in comparison to the diagnostic accuracy of the CA 15-3 alone. Application of the random forests also enables the reduction of number of laboratory tests needed for the establishment of diagnosis. Differences in relevant test values between the cancer and control group are small but application of multiparametric statistical learning ensured diagnostic accuracy of 72.0% associated by a sensitivity of 64.7% and specificity of 84.9%.
MeGLA is an effective tumoricidal agent against both parental and MDR cell lines. Its efficacy is maintained in the presence of clinically relevant serum contamination.