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Innovative Applications and Challenges of Artificial Intelligence in Surgical Oncology

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DOI: 10.23977/tranc.2025.060102 | Downloads: 11 | Views: 570

Author(s)

Ziyi Wang 1, Jiansheng Wang 2

Affiliation(s)

1 Shaanxi University of Chinese Medicine, Xi'an, China
2 Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

Corresponding Author

Jiansheng Wang

ABSTRACT

This paper reviews the latest advances in the application of Artificial Intelligence (AI) technologies in the domain of surgical oncology, and the challenges they face. The application of AI in surgical oncology encompasses a wide range of medical image analysis, preoperative planning and simulation, surgical navigation and robotic-assisted surgery, as well as postoperative monitoring and follow-up. The utilisation of computer-aided diagnosis (CAD) systems, deep learning models (e.g., U-Net), three-dimensional reconstruction techniques, and virtual reality (VR) has led to a substantial enhancement in the accuracy of tumour detection, the precision of surgical planning, and the safety of surgical operations. Concurrently, the integration of AI with postoperative data analysis and prediction models furnishes patients with personalised recovery plans and an enhanced prognosis. However, the application of AI in surgical oncology still faces challenges such as data privacy and security, algorithm performance validation, and multidisciplinary cooperation. In the future, with the continuous optimisation of the technology and the integration of multimodal data, AI is expected to drive oncology treatment in the direction of greater precision and personalisation, bringing about a revolutionary change in oncology surgery.

KEYWORDS

Artificial intelligence, surgical oncology, machine learning, deep learning, medical imaging, surgical assistance

CITE THIS PAPER

Ziyi Wang, Jiansheng Wang, Innovative Applications and Challenges of Artificial Intelligence in Surgical Oncology. Transactions on Cancer (2025) Vol. 6: 7-15. DOI: http://dx.doi.org/10.23977/tranc.2025.060102.

REFERENCES

[1] Bandyopadhyay, S., et al. (2019). Deep learning-based computer-aided detection for mammography: Diagnostic performance in dense breast tissue. European Journal of Radiology, 116, 11-19. doi.org/10.1016/j.ejrad.2019.04.010
[2] Wang, J., et al. (2021). Detection of lung cancer in low-density pulmonary nodules using a deep learning-based computer-aided detection system in CT images. Radiology, 299(3), 587-595. doi.org/10.1148/radiol.2021202023
[3] Zhou, T., et al. (2020). Deep learning in medical image analysis: A survey. Medical Image Analysis, 64, 101724. doi.org/10.1016/j.media.2020.101724
[4] Zhao, Y., et al. (2020). Lung nodule detection and segmentation in CT images using U-Net deep learning architecture. Journal of Healthcare Engineering, 2020, 8892674. doi.org/10.1155/2020/8892674
[5] Zhang, Y., et al. (2019). Artificial intelligence-based 3D visualization of liver tumors and blood vessels for precise resection planning. Journal of Hepatology, 70(2), 332-338. doi.org/10.1016/j.jhep.2018.09.016
[6] Liu, Y., et al. (2021). Artificial intelligence-based 3D visualization and surgical planning for brain tumor resection: A preliminary study. Neurosurgery, 89(1), 23-30. doi.org/10.1093/neuros/nyab182
[7] Zhou, Y., et al. (2020). Artificial intelligence-based three-dimensional reconstruction for preoperative planning in lung cancer surgery. European Journal of Radiology, 129, 109038. doi.org/10.1016/j.ejrad.2020.109038
[8] Liu, Y., et al. (2020). Virtual reality and augmented reality in cancer surgery: Current applications and future perspectives. Journal of Surgical Oncology, 122(4), 607-618. doi.org/10.1002/jso.26016
[9] Strother, C. M., et al. (2021). Virtual reality and augmented reality in neurosurgery: Current status and future potential. Neurosurgical Review, 44(4), 1245-1253. doi.org/10.1007/s10143-021-01396-x
[10] Ribeiro, A. J., et al. (2019). Intraoperative neuronavigation in brain tumor surgery: A systematic review and meta-analysis. Journal of Neurosurgery, 131(1), 1-9. doi.org/10.3171/2018.10.JNS181457
[11] Rocco, G., et al. (2020). Robot-assisted lobectomy for lung cancer: A multicenter study. Journal of Thoracic and Cardiovascular Surgery, 159(1), 284-290. doi.org/10.1016/j.jtcvs.2019.04.048
[12] Cerfolio, R. J., et al. (2019). Robotic Lobectomy: A 5-Year Experience with 1,000 Consecutive Cases. The Annals of Thoracic Surgery, 108(3), 937-943. doi.org/10.1016/j.athoracsur.2019.02.038
[13] Yang, C., et al. (2021). AI-based analysis for predicting postoperative recurrence in lung cancer patients. Journal of Thoracic Oncology, 16(5), 716-725. doi.org/10.1016/j.jtho.2021.01.033
[14] Wang, H., et al. (2023). AI-driven prediction of postoperative complications in oncological surgery: A machine learning-based approach. Annals of Surgical Oncology, 30(1), 228-236. doi.org/10.1245/s10434-023-12679-4
[15] Wang, J., et al. (2020). Postoperative recurrence prediction in non-small cell lung cancer using machine learning algorithms. Journal of Thoracic Oncology, 15(3), 556-565. doi.org/10.1016/j.jtho.2019.12.015
[16] Chen, Y., et al. (2021). Prediction of postoperative recurrence in breast cancer patients using machine learning-based models. Cancer Research, 81(5), 1143-1152. doi.org/10.1158/0008-5472.CAN-20-2212
[17] Lee, J. K., et al. (2021). Remote monitoring and AI-based prediction of postoperative recurrence in lung cancer. Lung Cancer, 162, 12-18. doi.org/10.1016/j.lungcan.2021.07.004 
[18] Wang, H., et al. (2022). AI-based intelligent follow-up system for colorectal cancer patients: A prospective study. Cancer Medicine, 11(12), 2319-2328. doi.org/10.1002/cam4.4602
[19] Bi, W. L., et al. (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians, 69(2), 127-157. doi.org/10.3322/caac.21552
[20] He, J., et al. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36. doi.org/10.1038/s41591-018-0307-0 
[21] Park, S. H., et al. (2020). Implementation of artificial intelligence in medicine: Status analysis and future directions. Journal of Korean Medical Science, 35(14), e115. doi.org/10.3346/jkms.2020.35.e115
[22] Nagendran, M., et al. (2020). Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 368, m689. doi.org/10.1136/bmj.m689
[23] Kann, B. H., et al. (2020). Artificial intelligence in oncology: Current applications and future directions. Nature Reviews Clinical Oncology, 17(11), 683-701. doi.org/10.1038/s41571-020-0399-6
[24] Bi, W. L., et al. (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians, 69(2), 127-157. doi.org/10.3322/caac.21552

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