Optical coherence tomography (OCT) is a promising method for detecting cancer margins during tumor resection. This study focused on differentiating tumorous from nontumorous tissues in human brain tissues using cross-polarization OCT (CP OCT). The study was performed on fresh ex vivo human brain tissues from 30 patients with high- and low-grade gliomas. Different tissue types that neurosurgeons should clearly distinguish during surgery, such as the cortex, white matter, necrosis and tumorous tissue, were separately analyzed. Based on volumetric CP OCT data, tumorous and normal brain tissue were differentiated using two optical coefficients — attenuation and forward cross-scattering. Compared with white matter, tumorous tissue without necrotic areas had significantly lower optical attenuation and forward cross-scattering values. The presence of particular morphological patterns, such as necrosis and injured myelinated fibers, can lead to dramatic changes in coefficient values and create some difficulties in differentiating between tissues. Color-coded CP OCT maps based on optical coefficients provided a visual assessment of the tissue. This study demonstrated the high translational potential of CP OCT in differentiating tumorous tissue from white matter. The clinical use of CP OCT during surgery in patients with gliomas could increase the extent of tumor resection and improve overall and progression-free survival.

This paper considers valuable visual assessment criteria for distinguishing between tumorous and non-tumorous tissues, intraoperatively, using cross-polarization OCT (CP OCT)—OCT with a functional extension, that enables detection of the polarization properties of the tissues in addition to their conventional light scattering. Materials and Methods: The study was performed on 176 ex vivo human specimens obtained from 30 glioma patients. To measure the degree to which the typical parameters of CP OCT images can be matched to the actual histology, 100 images of tumors and white matter were selected for visual analysis to be undertaken by three “single-blinded” investigators. An evaluation of the inter-rater reliability between the investigators was performed. Application of the identified visual CP OCT criteria for intraoperative use was performed during brain tumor resection in 17 patients. Results: The CP OCT image parameters that can typically be used for visual assessment were separated: (1) signal intensity; (2) homogeneity of intensity; (3) attenuation rate; (4) uniformity of attenuation. The degree of match between the CP OCT images and the histology of the specimens was significant for the parameters “signal intensity” in both polarizations, and “homogeneity of intensity” as well as the “uniformity of attenuation” in co-polarization. A test based on the identified criteria showed a diagnostic accuracy of 87–88%. Intraoperative in vivo CP OCT images of white matter and tumors have similar signals to ex vivo ones, whereas the cortex in vivo is characterized by indicative vertical striations arising from the “shadows” of the blood vessels; these are not seen in ex vivo images or in the case of tumor invasion. Conclusion: Visual assessment of CP OCT images enables tumorous and non-tumorous tissues to be distinguished. The most powerful aspect of CP OCT images that can be used as a criterion for differentiation between tumorous tissue and white matter is the signal intensity. In distinguishing white matter from tumors the diagnostic accuracy using the identified visual CP OCT criteria was 87–88%. As the CP OCT data is easily associated with intraoperative neurophysiological and neuronavigation findings this can provide valuable complementary information for the neurosurgeon tumor resection.

Photodynamic therapy (PDT) is a promising modern approach for cancer therapy with low normal tissue toxicity. This study was focused on a vascular-targeting Chlorine E6 mediated PDT. A new angiographic imaging approach known as M-mode-like optical coherence angiography (MML-OCA) was able to sensitively detect PDT-induced microvascular alterations in the mouse ear tumour model CT26. Histological analysis showed that the main mechanisms of vascular PDT was thrombosis of blood vessels and hemorrhage, which agrees with angiographic imaging by MML-OCA. Relationship between MML-OCA-detected early microvascular damage post PDT (within 24 hours) and tumour regression/regrowth was confirmed by histology. The advantages of MML-OCA such as direct image acquisition, fast processing, robust and affordable system opto-electronics, and label-free high contrast 3D visualization of the microvasculature suggest attractive possibilities of this method in practical clinical monitoring of cancer therapies with microvascular involvement.

We present a non-invasive (albeit contact) method based on optical coherence elastography (oce) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. the method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young's modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic "stiffness spectra") are initially determined by careful comparison of the "gold-standard" histological data and the OCE-based stiffness map for the corresponding tissue regions. After such pre-calibration, the results of morphological segmentation of oce-images demonstrate a striking similarity with the histological results in terms of percentage of the segmented zones. to validate the sensitivity of the oce-method and demonstrate its high correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two antitumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (1) viable, (2) dystrophic, (3) necrotic tumor cells and (4) edema zones very similar to morphological segmentation of histological images. numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen.

A novel machine-learning method to distinguish between tumor and normal tissue in optical coherence tomography (OCT) has been developed. Pre-clinical murine ear model implanted with mouse colon carcinoma CT-26 was used. Structural-image-based feature sets were defined for each pixel and machine learning classifiers were trained using "ground truth" OCT images manually segmented by comparison with histology. The accuracy of the OCT tumor segmentation method was then quantified by comparing with fluorescence imaging of tumors expressing genetically encoded fluorescent protein KillerRed that clearly delineates tumor borders. Because the resultant 3D tumor/normal structural maps are inherently co-registered with OCT derived maps of tissue microvasculature, the latter can be color coded as belonging to either tumor or normal tissue. Applications to radiomics-based multimodal OCT analysis are envisioned.