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

Abstract

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.

Keywords: cancer imaging; imaging technique; oncological diseases; optical coherence tomography; tumor diagnoses.

Figure 1

Application of OCT in the field of oncology.

Figure 2

Structural diagrams of three generations of OCT systems. (A) TD-OCT; (B) SD-OCT; (C) SS-OCT (30). Copyright 2022, www.opticsjournal.net.

Figure 3

(A) Schematic of subcutaneous tumor-bearing nude mice dorsal window imaging; (B) Subcutaneous tumors of nude mice with tumors enlarge the skin window chamber images; (C) The zoom-in skin window chamber image in the healthy nude mice; (D) The corresponding enface microvascular image in vivo is shown in (C); (E) Representative tissue cross sectional structural image (gray) and blood flow image (red border); (F) The zoom-in skin window chamber image in the subcutaneous tumor-bearing nude mice; (G) The corresponding enface microvascular image in vivo is shown in (F) (50); Copyright 2021, Wiley. (H) Normal vascular OCT images; (I) OCT images of angiogram and lymphangiography. The dotted line indicates the position of the cross-section image in A-B. (J, K) normal angiography and lymphangiography OCT en-face images. The red arrow indicates a large blood vessel, and the yellow arrow indicates the lymphatic vessel. (L) Melanoma vascular OCT image. (M) OCT images of melanoma angiography and lymphangiography. The dotted line indicates the position of the cross-section image in (E–F). (N, O) Melanoma angiography and lymphangiography OCT en-face image (52). Copyright 2020, American chemistry society.

Figure 4

(A-C) ANN-SVM based image analysis pipeline (53); Copyright 2021, MDPI. (D) The overview of LRAN (58) Copyright 2022, Springer.

Figure 5

(A) Schematic diagram of a commercial OCT; (B) Gastric tube perfusion areas and (C) ROI region OCT grayscale image, cross-sectional OCT image showing vessels shadow (62); Copyright 2018, MDPI. OCTA vascular imaging of (D-F) non-dysplasia and (G-I) dysplasia BE (63). Nanoparticles targeting the hypoxic tumor microenvironment. Copyright 2017, ELSEVIER.

Figure 6

(A) Pretreatment of cerebral surface vascular construction (red) and tumor area (green) in mice. (B) Renders an image in 3D with an attenuation rate threshold mask superimposed on the OCT intensity (blue). (C) Maximum intensity projection after coagulation. (D) Maximum intensity projection after ablation. (E) After overlapping the tumor margins (blue) before ablation, stain the corresponding area with the post-ablation b-scan (gray) (F) H&E stained of the corresponding region (72); Copyright 2019, Theranostics. (G) FF-OCT image of normal and (H) cancerous hepatic cell. (I-K) Boxplot of selected features for the Mean, Kurtosis, and FracDim. mean and kurtosis are not sensitive in distinguishing between normal and cancerous hepatocytes, the mean and kurtosis are not sensitive in distinguishing between normal and cancerous hepatocytes, when the liver becomes cancerous, the value of the fractal parameter increases (88). Copyright 2020, Wiley.