Keywords
Colorectal cancer cells
Automatic identification
Morphological heterogeneity
Submitted : 2026-02-08
Accepted : 2026-02-23
Published : 2026-03-10
Abstract
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
References
Huang S, Chen G, 2025, Construction of an Interpretable Risk Prediction Model for Colorectal Sessile Serrated Lesions Based on Machine Learning Algorithms. Journal of Xuzhou Medical University, 45(10): 753–759.
Shi Y, Ma C, Tang X, et al., 2025, Ursolic Acid Induces Ferroptosis in Colorectal Cancer Cells via Regulation of System xc− and the miR-214-3p/Stat3/GPX4 Axis. Frontiers in Immunology, 16: 1674321–1674321.
Li R, Qin W, 2025, From Pathological Images to Molecular Prediction: A New Paradigm for MSI Detection in Colorectal Cancer Driven by Multimodal Artificial Intelligence. Chinese Journal of General Surgery, 34(10): 2232–2242.
Xu S, Zhang H, 2025, Advances in the Application of Artificial Intelligence in Immunotherapy for Colorectal Cancer. Journal of Colorectal and Anal Surgery, 31(4): 303–307.
Liu J, Li L, Zhang L, et al., 2025, Epigenetic Regulation of NR5A2 Influences Colorectal Cancer Cell Stemness via a Stemness-Related Transcription Factor NANOG. Cell Communication and Signaling, 23(1): 484–484.
Li J, Li S, Sun W, et al., 2025, Construction and Evaluation of a CT-Based Differential Diagnosis Model for Ulcerative Colitis-Associated Dysplasia and Colorectal Cancer Using Machine Learning Algorithms. Clinical Medicine, 45(8): 74–79.
Li B, Liu G, 2025, Predictive Value of a Machine Learning Model Combining MRI Radiomics With Clinical Features for Liver Metastasis in Colorectal Cancer. Chinese Journal of General Surgery, 34(7): 1410–1420.
Xiang J, Xu J, Fan H, et al., 2025, PARP Inhibitor BMN673 Triggers PARylation-Mediated ATF4-GDF15 Pathway to Drive Autophagy and Ferroptosis in ATM-Deficient Colorectal Cancer Cells. Molecular Biomedicine, 6(1): 113–113.
Wang Z, Wang W, Wang D, et al., 2025, Comparison of Deep Learning and Machine Learning Models in Preoperative Prediction of Mismatch Repair System Status in Colorectal Cancer Based on CT Radiomics. Journal of Molecular Imaging, 48(3): 315–322.
Li Z, Ma R, Wang Y, et al., 2025, Application of a Radiomics Machine Learning Model and Nomogram Based on Spectral CT for Preoperative Differentiation of KRAS Gene Status in Colorectal Cancer. International Journal of Medical Radiology, 48(2): 151–158.