Table 3.

The imaging capabilities of OCT in deep tumors.

Cancer	Authors	Main Findings
Brain cancer	NyúlTóth et al. (2021) (89); Hartmann et al. (2020) (90); Yecies et al. (2019) (10); Tsai et al. (2018) (91)	OCT can not only identify and quantify cerebrovascular morphology and degree of relaxation in vivo but also conduct long-term monitoring of cerebrovascular dynamics in dilated. It can also show hidden brain microanatomy to identify brain tumor margins, improving intraoperative safety.
Breast cancer	Mojahed et al. (2020) (74); Kansal et al. (2020) (75); Yang et al. (2020) (76)	FF-OCT has good diagnostic potential in breast surgery and enables real-time assessment of intraoperative margins.
Bladder cancer	Sung et al. (2021) (78); Xu et al. (2021) (79)	OCT can show the depth and type of invasion of urothelial cancer cells, accurately grading and staging bladder cancer. Assist in intraoperative decision-making through real-time disease staging for more accurate diagnosis, resection, and reduced recurrence rates.
Cervical cancer	Chen et al. (2022) (77); Ren et al. (2021) (92); Placzek et al. (2020) (93); Ma et al. (2019) (94); Zeng et al. (2018) (95)	OCT can identify cervical morphological features and lesions noninvasively in real-time.
Lung cancer	Ding et al. (2021) (81)	Endobronchial OCT (EB-OCT) combined with machine learning algorithms can identify malignant lung nodules at a low cost.
Hepatocellular carcinoma	Zhu et al. (2020) (88)	FF-OCT can quantitatively detect hepatocellular carcinoma without markers.