Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

Automated summary

We propose a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies using quantitative phase imaging and localized Raman spectroscopy. The approach involves performing label-free quantitative phase imaging of the tissue slice and then using kernelled structural support vector machine to segment the tissue layers. The segmented areas are used to guide localized Raman spectroscopy measurements for classification of tissue types. The results show that the prior segmentation using quantitative phase imaging improves the classification accuracy of Raman spectra.