This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2–94.6% accuracy, 89.8–91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600–1800 cm−1) and global loss of high wavenumber signal (2800–3200 cm−1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

effects to account for the accumulation of axial conformational strain and the anisotropic lateral coupling between individual dimers. Analysis of the four different MT kinetic phases (growing, catastrophe, shrinking, rescue) shows that catastrophe and rescue correlate with a small change in the number of GTP-dimers and the ruggedness of the MT tip. Contrary to what is hypothesized, our results reveal that exposure of GDP-dimers at the tip does not correlate with an increase in the probability of catastrophe when the ruggedness of the MT tip is below a certain threshold, thereby providing new insights into the microscopic mechanism of catastrophe and rescue.