Spectroscopic optical coherence tomography for classification of colorectal cancer in a mouse model

Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography

Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.

Deep learning classification of ex vivo human colon tissues using spectroscopic OCT

Screening programs for colorectal cancer (CRC) have had a profound impact on the morbidity and mortality of this disease by detecting and removing early cancers and precancerous adenomas with colonoscopy. However, CRC continues to be the third leading cause of cancer-related mortality in both men and woman, partly because of limitations in colonoscopy-based screening. Thus, novel strategies to improve the efficiency and effectiveness of screening colonoscopy are urgently needed. Here, we propose to address this need using an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). The depth resolved images obtained with OCT are analyzed as a function of wavelength to measure optical tissue properties. The optical properties can be used as input to machine learning algorithms as a means to classify adenomatous tissue in the colon. In this study, biopsied tissue samples from the colonic epithelium are analyzed ex vivo using spectroscopic OCT and tissue classifications are generated using a novel deep learning architecture, informed by machine learning methods including LSTM and KNN. The overall classification accuracy obtained was 88.9%, 76.0% and 97.9% in discriminating tissue type for these methods. Further, we apply an approach using false coloring of en face OCT images based on SOCT parameters and deep learning predictions to enable visual identification of tissue type. This study advances the spectroscopic OCT towards clinical utility for analyzing colonic epithelium for signs of adenoma.

Research progress on the application of optical coherence tomography in the field of oncology

Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.