Quantum cascade laser infrared microscopy differentiates malignant phenotypes in breast histology sections

Quantum Cascade Laser InfRared (QCL-IR) microscopy has the potential to emerge as a unique modality for the diagnostics of histology sections. A pilot experiment is designed to evaluate whether benign and malignant breast histology sections can be differentiated using chemical profiling and without the use of spatial information. The experiment consists of 22 independent samples that were randomly selected from paraffin-embedded blocks with an equal number of benign and malignant labels. Spectral data are normalized and then used for both visualization and classification. Visualization is based on consensus clustering of the spectral signature within each group of the benign or malignant phenotype. Classification has been evaluated with three methods of tree-based, convolutional neural networks, and an encoder module for compression followed by softmax classification. The latter has the best performance with using only 20% of the data for training to arrive at the classification accuracy of 100%. Direct analysis of the spectral signatures indicates that both malignant and benign samples express the similar spectral peaks; however, peaks corresponding to several nucleic acid sequences and protein groups are overexpressed in malignant tissues.