Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Abstract

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.

Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography

“…However, the absence of a statistically significant difference in the values of elasticity does not allow quantitative differentiation of hyperplastic benign adenoma from non-tumor colon tissues. An increase in the depth of the colorectal adenoma image obtained by C-OCE was not always clearly seen qualitatively, which indicates the need to use structural OCT to differentiate colorectal adenoma from normal colon mucosa, as shown in ( 54 , 57 ) and in our study. At the same time, here we are first to demonstrate the minimum detectable stiffness value for malignant tumor tissue which was identified to be ~500 kPa.…”

“…The proposed solutions in most cases are aimed at optimizing the targeted tissue sampling for subsequent histological examination. In addition, it has already been shown that the use of an additional modality such as cross-polarization OCT for en face mapping allows successful identification of colon tissue neoplasia ( 54 ). The joint use of near-infrared fluorescence with OCT, for example, to visualize the structure and microvasculature of the colon demonstrated the possibility of identifying cases of early stages of colorectal cancer ( 55 ).…”

Spectroscopic optical coherence tomography at 1200 nm for lipid detection

“…Since the scattering and absorption coefficients of a material are wavelength-dependent parameters, the measurement of spectral-dependent OCT intensity profiles [I(λ,z)] can offer material classification based on either scattering or absorption features 23 , 31 , 32 . In this study, we exploited the distinct absorption features of water and lipid containing samples for the classification of materials.…”

Tissue Elasticity as a Diagnostic Marker of Molecular Mutations in Morphologically Heterogeneous Colorectal Cancer

“…Initially, diagnostic tasks in CRC were to differentiate CRC tissue from normal colon tissue and adenoma. Using structural OCT, it has been proven that attenuation coefficient mapping [51] and resorting to machine learning [52] allow for improving quantitative diagnostic results. Further, the application of ultrasound elastography has been successful in visualizing the depth of CRC invasion [53].…”