Progress and prospects of artificial intelligence in perioperative management of colorectal cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

HUANG Weimin ,  LI Quan

Bazhong Central Hospital, Bazhong, Sichuan 636000, P. R. China;

Corresponding author：

LI Quan, Email: 1191051924@qq.com

Keywords：

artificial intelligence; colorectal cancer; perioperative management

DOI：

10.7507/1007-9424.202505068

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the recent research progress of artificial intelligence (AI) for perioperative management of colorectal cancer (CRC), and to explore its clinical application value and future development direction. Method The relevant research on AI in the perioperative management of CRC surgery from China National Knowledge Infrastructure, Wanfang, PubMed, and Google Scholar databases in the past 5 years was retrieved and reviewed. Results Currently, AI had been applied throughout the entire process related to CRC surgery. Preoperatively, AI-assisted analysis of CT or MRI images facilitated precise tumor staging assessment, prediction of neoadjuvant therapy response, and surgical planning optimization. Intraoperatively, real-time endoscopic vision integrated with AI enabled tumor localization, tracking, and tissue identification accuracy, enhancing procedural safety. Postoperatively, AI-supported rehabilitation protocols optimized early mobilization, enabled continuous complication monitoring, and refined follow-up management, providing personalized intervention strategies for early clinical intervention to improve patient outcomes. Conclusions Current research demonstrates promising outcomes of AI applications in CRC perioperative management, yet reveals a significant imbalance in research focus with predominant investigations concentrated on preoperative assistance. Notably, postoperative domains, including fall prevention, medication error detection, complication mitigation, adjuvant therapy decision support, psychosocial support, recurrence surveillance, and survival follow-up, exhibit marked deficiencies in AI exploration and clinical translation, constituting a critical weakness in establishing comprehensive intelligent support throughout the perioperative continuum. Future research must extend beyond addressing intraoperative AI challenges to prioritize AI-augmented prediction of short-/long-term complications, optimization of personalized rehabilitation pathways, precision adjuvant therapy decision support, intelligent follow-up systems, and applications enhancing postoperative quality of life and long-term survival outcomes.

Citation： HUANG Weimin, LI Quan. Progress and prospects of artificial intelligence in perioperative management of colorectal cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(8): 1050-1056. doi: 10.7507/1007-9424.202505068 Copy

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1. Gibaud B, Jannin P, Morandi X, et al. The role of image guidance in neurosurgery. Lancet Neurol, 2019, 18(1): 83-94.
2. Fuller SC, Strong EB. Computer applications in facial plastic and reconstructive surgery. Curr Opin Otolaryngol Head Neck Surg, 2007, 15(4): 233-237.
3. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
4. Nassiri F, Patil V, Yefet LS, et al. Oncolytic DNX-2401 virotherapy plus pembrolizumab in recurrent glioblastoma: a phase 1/2 trial. Nat Med, 2023, 29(6): 1370-1378.
5. Uchikov P, Khalid U, Kraev K, et al. Artificial intelligence in the diagnosis of colorectal cancer: a literature review. Diagnostics (Basel), 2024, 14(5): 528. doi: 10.3390/diagnostics14050528.
6. Nagtegaal ID, Marijnen CA, Kranenbarg EK, et al. Circumferential margin involvement is still an important predictor of local recurrence in rectal carcinoma: not one millimeter but two millimeters is the limit. Am J Surg Pathol, 2002, 26(3): 350-357.
7. Garcia-Granero E, Frasson M, Esclapez P, et al. Impact of tumor morphology on the accuracy of magnetic resonance imaging for predicting pathological response after neoadjuvant chemoradiotherapy in rectal cancer. Eur J Surg Oncol, 2024, 50(2): 107321. doi: 10.1016/j.ejso.2023.107321.
8. Xu Z, Li Y, Wang Y, et al. A deep learning quantified stroma-immune score to predict survival of patients with stage Ⅱ–Ⅲ colorectal cancer. Cancer Cell Int, 2021, 21(1): 585. doi: 10.1186/s12935-021-02297-w.
9. Liu Z, Liu Z, Anwar Z, et al. Deep learning for tumor delineation on multi-modal MRI in rectal cancer. Med Phys, 2021, 48(5): 2435-2444.
10. Zhou X, Li Y, Zhang L, et al. nnUNet-based automatic segmentation of rectal cancer in MRI for radiotherapy planning. Radiother Oncol, 2022, 167: 45-52.
11. Park YS, Lee CH, Kim JH, et al. Differentiation of focal fatty change in the liver from metastasis: role of T1-weighted in-phase and opposed-phase gradient-echo MR imaging. Radiology, 2017, 284(2): 424-432.
12. Karcaaltincaba M, Akhan O. Imaging of hepatic steatosis and fatty sparing. AJR Am J Roentgenol, 2009, 192(4): 995-1001.
13. Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
14. Pickhardt PJ, Graffy PM, Reeder SB, et al. Quantification of liver fat content with CT and MRI: state of the art. Radiology, 2021, 299(2): 329-337.
15. Lao J, Chen Y, Li ZC, et al. Deep learning for radiomics-based heterogeneity analysis in colorectal cancer. Sci Rep, 2017, 7(1): 11351. doi: 10.1038/s41598-017-11553-x.
16. Wu J, Zhang Q, Zhao Y, et al. Spatiotemporal heterogeneity mapping in colorectal cancer via federated learning. Nat Commun, 2022, 13(1): 4567. doi: 10.1038/s41467-022-32170-x.
17. 李晓燕, 李晨, 陈灏源, 等. 人工智能在结直肠癌数字化病理图像分析中的应用. 协和医学杂志, 2022, 13(4): 542-548.
18. Yuan C, Wang B, Wang H, et al. T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging. Front Immunol, 2025, 16: 1556165. doi: 10.3389/fimmu.2025.1556165.
19. Peng W, Wan L, Tong X, et al. Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time. Eur Radiol, 2024, 34(11): 7438-7449.
20. Horie Y, Yoshio T, Aoyama K, et al. Deep learning detection of colorectal cancer wall invasion on endoscopic ultrasound. Gastrointest Endosc, 2020, 92(4): 831-838.
21. Wang X, Zhao Y, Zhang L, et al. AI-assisted staging of colorectal cancer T categories using CT-based radiomics. Radiology, 2022, 303(2): 351-360.
22. Liang Z, Wang Q, Li X, et al. Automatic T staging of rectal cancer using MRI-based deep learning. J Magn Reson Imaging, 2021, 54(3): 789-798.
23. Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2018, 29(Suppl 4): iv263. doi: 10.1093/annonc/mdy161.
24. Zhang L, Tan J, Han D, et al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today, 2017, 22(11): 1680-1685.
25. Kwak MS, Lee HH, Yang JM, et al. Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images. Front Oncol, 2021, 10: 619803. doi: 10.3389/fonc.2020.619803.
26. Pai RK, Hartman D, Schaeffer DF, et al. Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters. Histopathology, 2021, 79(3): 391-405.
27. Mo S, Zhou Z, Dai W, et al. Development and external validation of a predictive scoring system associated with metastasis of T1-2 colorectal tumors to lymph nodes. Clin Transl Med, 2020, 10(1): 275-287.
28. Kiehl L, Kuntz S, Höhn J, et al. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer, 2021, 157: 464-473.
29. Zhu C, Liang J, Xu M, et al. AI-assisted lymph node staging in rectal cancer using endoscopic ultrasound: a multicenter randomized controlled trial. Endoscopy, 2023, 55(3): 234-241.
30. Wang L, Chen R, Li X, et al. Multimodal fusion of CT and MRI for colorectal cancer staging using deep learning. IEEE Trans Med Imaging, 2021, 40(4): 1234-1245.
31. Zhao X, Xie P, Wang M, et al. Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: a multicentre study. EBioMedicine, 2020, 56: 102780. doi: 10.1016/j.ebiom.2020.102780.
32. Chen YY, Lin CH, Hsu CC, et al. Deep learning for automatic detection of colorectal liver metastases on CT. Radiology, 2022, 305(3): 648-658.
33. Feng S, Zhou M, Huang Z, et al. A machine learning-based prediction model for colorectal liver metastasis. Clin Exp Med, 2025, 25(1): 156. doi: 10.1007/s10238-025-01699-8.
34. Bashir U, Wang C, Smillie R, et al. Deep learning for liver lesion segmentation and classification on staging CT scans of colorectal cancer patients: a multi-site technical validation study. Clin Radiol, 2025, 85: 106914. doi: 10.1016/j.crad.2025.106914.
35. Chizhikova M, López-Úbeda P, Martín-Noguerol T, et al. Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models. Comput Methods Programs Biomed, 2025, 259: 108515. doi: 10.1016/j.cmpb.2024.108515.
36. Abu-Freha N, Afawi Z, Yousef M, et al. A machine learning approach to differentiate stage Ⅳ from stage Ⅰ colorectal cancer. Comput Biol Med, 2025, 191: 110179. doi: 10.1016/j.compbiomed.2025.110179.
37. Guan X, Yu G, Zhang W, et al. An easy-to-use artificial intelligence preoperative lymph node metastasis predictor (LN-MASTER) in rectal cancer based on a privacy-preserving computing platform: multicenter retrospective cohort study. Int J Surg, 2023, 109(3): 255-265.
38. Zhang XY, Wang L, Zhu HT, et al. Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI. Radiology, 2020, 296(1): 56-64.
39. Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 12611. doi: 10.1038/s41598-018-30657-6.
40. Ouyang G, Chen Z, Dou M, et al. Predicting rectal cancer response to total neoadjuvant treatment using an artificial intelligence model based on magnetic resonance imaging and clinical data. Technol Cancer Res Treat, 2023, 22: 15330338231186467. doi: 10.1177/15330338231186467.
41. Argilés G, Tabernero J, Labianca R, et al. Localised colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2020, 31(10): 1291-1305.
42. Bai F, Liao L, Tang Y, et al. RCMIX model based on pre-treatment MRI imaging predicts T-downstage in MRI-cT4 stage rectal cancer. Cancer Lett, 2025, 628: 217871. doi: 10.1016/j.canlet.2025.217871.
43. Shaish H, Aukerman A, Vanguri R, et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol, 2020, 30(11): 6263-6273.
44. Feng L, Liu Z, Li C, et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicentre observational study. Lancet Digit Health, 2022, 4(1): e8-e17. doi: 10.1016/S2589-7500(21)00215-6.
45. Qiu B, Shen Z, Wu S, et al. A machine learning-based model for predicting distant metastasis in patients with rectal cancer. Front Oncol, 2023, 13: 1235121. doi: 10.3389/fonc.2023.1235121.
46. Spohn SKB, Aebersold DM, Albrecht C, et al. Biomarkers in prostate cancer: current status and future directions in radiotherapy-statement from the Prostate Cancer Working Group of the German Society of Radiation Oncology (DEGRO). Strahlenther Onkol, 2025. doi: 10.1007/s00066-025-02388-x.
47. Chevalier O, Dubey G, Benkabbou A, et al. Comprehensive overview of artificial intelligence in surgery: a systematic review and perspectives. Pflugers Arch, 2025, 477(4): 617-626.
48. Caredda C, Lange F, Giannoni L, et al. Digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping. J Biomed Opt, 2025, 30(2): 023513. doi: 10.1117/1.JBO.30.2.023513.
49. Shindoh J, Makuuchi M, Matsuyama Y, et al. Complete removal of the tumor-bearing portal territory decreases local tumor recurrence and improves disease-specific survival of patients with hepatocellular carcinoma. J Hepatol, 2016, 64(3): 594-600.
50. Rickles AS, Dietz DW, Chang GJ, et al. High rate of positive circumferential resection margins following rectal cancer surgery: a call to action. Ann Surg, 2015, 262(6): 891-898.
51. Atallah S, Nassif G, Larach S. Stereotactic navigation for TAMIS-TME: opening the gateway to frameless, image-guided abdominal and pelvic surgery. Surg Endosc, 2015, 29(1): 207-211.
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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Progress and prospects of artificial intelligence in perioperative management of colorectal cancer

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