人工智能在乳腺癌患者预后预测中的研究进展
Research Progress of Artificial Intelligence in Breast Cancer Patient Prognosis Prediction
DOI: 10.12677/mos.2025.148562, PDF,    科研立项经费支持
作者: 黄 雯, 蒋卓韵*, 朱志刚：上海理工大学健康科学与工程学院，上海；赵佳琦, 郭芳琪, 李 慧：同济大学附属上海市第四人民医院超声医学科院，上海
关键词: 乳腺癌；人工智能；预后预测；生存期；特征融合；Breast Cancer； Artificial Intelligence； Prognosis Prediction； Survival Analysis； Feature Fusion
摘要: 乳腺癌作为全球女性最常见的恶性肿瘤之一，准确预测其预后情况能为临床决策提供依据，避免过度治疗。人工智能技术被证实在处理复杂医学数据和特征提取方面具有显著优势，将其应用在乳腺癌的预后预测已成为当前热点。该综述就近年来机器学习和深度学习的主流网络对乳腺癌生存期、复发转移等预后预测问题进行了系统性的阐述和总结，写明了各网络在这些任务中的性能。此外，汇总和比较了基于拼接和注意力机制的多模态特征融合技术在乳腺癌预后预测中的应用。最后，对乳腺癌预后预测中的关键问题进行总结，并讨论了未来研究的发展趋势，旨在改善乳腺癌患者的预后效果和生存率。
Abstract: Breast cancer, as one of the most common malignant tumors among women worldwide, requires accurate prognosis prediction to inform clinical decision-making and avoid overtreatment. Artificial intelligence (AI) technologies have demonstrated significant advantages in processing complex medical data and feature extraction, making their application in breast cancer prognosis prediction a current research hotspot. This paper systematically reviews and summarizes recent advances in machine learning and deep learning models for predicting breast cancer prognosis, including survival outcomes, recurrence, and metastasis, while evaluating the performance of various networks in these tasks. In addition, it summarizes and compares the applications of multimodal feature fusion techniques based on concatenation and attention mechanisms in breast cancer prognosis prediction. Finally, key challenges in breast cancer prognosis prediction are discussed, along with future research directions, aiming to improve prognostic outcomes and survival rates for breast cancer patients.
文章引用：黄雯, 蒋卓韵, 朱志刚, 赵佳琦, 郭芳琪, 李慧. 人工智能在乳腺癌患者预后预测中的研究进展[J]. 建模与仿真, 2025, 14(8): 228-239. https://doi.org/10.12677/mos.2025.148562

参考文献

[1] 王昭卜, 黎星, 于鑫淼, 等. 2023年改变早期乳腺癌临床实践的重要研究成果及进展[J]. 中国癌症杂志, 2024, 34(2): 151-160.
[2] Tudor, C. (2022) A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania. Biology, 11, Article 857. [Google Scholar] [CrossRef] [PubMed]
[3] 周昌明, 王泽洲, 郑莹. 2023年美国癌症数据解读及对中国癌症防治的启示[J]. 中国癌症杂志, 2023, 33(2): 117-125.
[4] Ramesh, A., Kambhampati, C., Monson, J. and Drew, P. (2004) Artificial Intelligence in Medicine. Annals of the Royal College of Surgeons of England, 86, 334-338. [Google Scholar] [CrossRef] [PubMed]
[5] Sultan, A.S., Elgharib, M.A., Tavares, T., Jessri, M. and Basile, J.R. (2020) The Use of Artificial Intelligence, Machine Learning and Deep Learning in Oncologic Histopathology. Journal of Oral Pathology & Medicine, 49, 849-856. [Google Scholar] [CrossRef] [PubMed]
[6] Lan, W., Liao, H., Chen, Q., Zhu, L., Pan, Y. and Chen, Y.P. (2024) DeepKEGG: A Multi-Omics Data Integration Framework with Biological Insights for Cancer Recurrence Prediction and Biomarker Discovery. Briefings in Bioinformatics, 25, bbae185. [Google Scholar] [CrossRef] [PubMed]
[7] Tomczak, K., Czerwińska, P. and Wiznerowicz, M. (2015) Review the Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge. Współczesna Onkologia, 1, 68-77. [Google Scholar] [CrossRef] [PubMed]
[8] Ries, L.A.G., Smith, M.A., Gurney, J.G., et al. (1999) Cancer Incidence and Survival among Children and Adolescents: United States SEER Program 1975-1995. National Cancer Institute.
[9] Craven, K.E., Gökmen-Polar, Y. and Badve, S.S. (2021) CIBERSORT Analysis of TCGA and METABRIC Identifies Subgroups with Better Outcomes in Triple Negative Breast Cancer. Scientific Reports, 11, Article No. 4691. [Google Scholar] [CrossRef] [PubMed]
[10] Lee, M. (2023) Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature. Biology, 12, Article 893. [Google Scholar] [CrossRef] [PubMed]
[11] Friedman, J.H. (2001) Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29, 1189-1232. [Google Scholar] [CrossRef] 
[12] Chen, Y., Jia, Z., Mercola, D. and Xie, X. (2013) A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index. Computational and Mathematical Methods in Medicine, 2013, Article ID: 873595. [Google Scholar] [CrossRef] [PubMed]
[13] Lu, H., Wang, H. and Yoon, S.W. (2019) A Dynamic Gradient Boosting Machine Using Genetic Optimizer for Practical Breast Cancer Prognosis. Expert Systems with Applications, 116, 340-350. [Google Scholar] [CrossRef] 
[14] Liu, P., Fu, B., Yang, S.X., Deng, L., Zhong, X. and Zheng, H. (2021) Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer. IEEE Transactions on Biomedical Engineering, 68, 148-160. [Google Scholar] [CrossRef] [PubMed]
[15] Quinlan, J.R. (2014) C4. 5: Programs for Machine Learning. Elsevier.
[16] Wang, K., Makond, B. and Wang, K. (2013) An Improved Survivability Prognosis of Breast Cancer by Using Sampling and Feature Selection Technique to Solve Imbalanced Patient Classification Data. BMC Medical Informatics and Decision Making, 13, Article No. 124. [Google Scholar] [CrossRef] [PubMed]
[17] Liu, Y., Wang, C. and Zhang, L. (2009) Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data. 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, 11-13 June 2009, 1-4. [Google Scholar] [CrossRef] 
[18] Wang, K., Makond, B., Chen, K. and Wang, K. (2014) A Hybrid Classifier Combining SMOTE with PSO to Estimate 5-Year Survivability of Breast Cancer Patients. Applied Soft Computing, 20, 15-24. [Google Scholar] [CrossRef] 
[19] Momenyan, S., Baghestani, A.R., Momenyan, N., Naseri, P. and Akbari, M.E. (2018) Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis. International Journal of Cancer Management, 11, e9176. [Google Scholar] [CrossRef] 
[20] Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M. and Parmar, M. (2024) A Review of Convolutional Neural Networks in Computer Vision. Artificial Intelligence Review, 57, Article No. 99. [Google Scholar] [CrossRef] 
[21] Comes, M.C., La Forgia, D., Didonna, V., Fanizzi, A., Giotta, F., Latorre, A., et al. (2021) Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs. Cancers, 13, Article 2298. [Google Scholar] [CrossRef] [PubMed]
[22] Wetstein, S.C., de Jong, V.M.T., Stathonikos, N., Opdam, M., Dackus, G.M.H.E., Pluim, J.P.W., et al. (2022) Deep Learning-Based Breast Cancer Grading and Survival Analysis on Whole-Slide Histopathology Images. Scientific Reports, 12, Article No. 15102. [Google Scholar] [CrossRef] [PubMed]
[23] Pan, L., Peng, Y., Li, Y., Wang, X., Liu, W., Xu, L., et al. (2024) SELECTOR: Heterogeneous Graph Network with Convolutional Masked Autoencoder for Multimodal Robust Prediction of Cancer Survival. Computers in Biology and Medicine, 172, Article ID: 108301. [Google Scholar] [CrossRef] [PubMed]
[24] 韩珺琪. 基于乳腺X线摄影及超声深度学习模型预测乳腺癌预后价值[D]: [硕士学位论文]. 青岛: 青岛大学, 2024.
[25] 曹广硕, 黄瑞章, 陈艳平, 等. 基于多模态学习的乳腺癌生存预测研究[J]. 计算机工程, 2024, 50(1): 296-305.
[26] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M. and Monfardini, G. (2009) The Graph Neural Network Model. IEEE Transactions on Neural Networks, 20, 61-80. [Google Scholar] [CrossRef] [PubMed]
[27] Palmal, S., Saha, S., Arya, N. and Tripathy, S. (2024) CAGCL: Predicting Short-and Long-Term Breast Cancer Survival with Cross-Modal Attention and Graph Contrastive Learning. IEEE Journal of Biomedical and Health Informatics, 28, 7382-7391. [Google Scholar] [CrossRef] [PubMed]
[28] Gao, J., Lyu, T., Xiong, F., Wang, J., Ke, W. and Li, Z. (2022) Predicting the Survival of Cancer Patients with Multimodal Graph Neural Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19, 699-709. [Google Scholar] [CrossRef] [PubMed]
[29] 薛婧瑶. 基于深度学习的乳腺癌患者生存期预测方法[D]: [硕士学位论文]. 北京: 北京邮电大学, 2024.
[30] Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., et al. (2020) Deep Learning Radiomics Can Predict Axillary Lymph Node Status in Early-Stage Breast Cancer. Nature Communications, 11, Article No. 1236. [Google Scholar] [CrossRef] [PubMed]
[31] Dufter, P., Schmitt, M. and Schütze, H. (2022) Position Information in Transformers: An Overview. Computational Linguistics, 48, 733-763. [Google Scholar] [CrossRef] 
[32] Chen, Z., Zhang, J. and Tao, D. (2022) Recurrent Glimpse-Based Decoder for Detection with Transformer. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, 18-24 June 2022, 5250-5259. [Google Scholar] [CrossRef] 
[33] Luo, Y., Wang, S., Liu, J., et al. (2025) PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images. arXiv: 2503.17970.
[34] Mbaye, N.M., Danziger, M., Toussaint, A., et al. (2024) Multimodal BEHRT: Transformers for Multimodal Electronic Health Records to Predict Breast Cancer Prognosis. medRxiv.
[35] Wu, X., Li, Y., Chen, J., Chen, J., Zhang, W., Lu, X., et al. (2025) Multimodal Recurrence Risk Prediction Model for HR+/HER2-Early Breast Cancer Following Adjuvant Chemo-Endocrine Therapy: Integrating Pathology Image and Clinicalpathological Features. Breast Cancer Research, 27, Article No. 27. [Google Scholar] [CrossRef] [PubMed]
[36] Wang, Z., Gao, Q., Yi, X., Zhang, X., Zhang, Y., Zhang, D., et al. (2023) Surformer: An Interpretable Pattern-Perceptive Survival Transformer for Cancer Survival Prediction from Histopathology Whole Slide Images. Computer Methods and Programs in Biomedicine, 241, Article ID: 107733. [Google Scholar] [CrossRef] [PubMed]
[37] Boehm, K.M., El Nahhas, O.S.M., Marra, A., Waters, M., Jee, J., Braunstein, L., et al. (2025) Multimodal Histopathologic Models Stratify Hormone Receptor-Positive Early Breast Cancer. Nature Communications, 16, Article No. 2106. [Google Scholar] [CrossRef] [PubMed]
[38] Palmal, S., Arya, N., Saha, S. and Tripathy, S. (2023) Breast Cancer Survival Prognosis Using the Graph Convolutional Network with Choquet Fuzzy Integral. Scientific Reports, 13, Article No. 14757. [Google Scholar] [CrossRef] [PubMed]
[39] Othman, N.A., Abdel-Fattah, M.A. and Ali, A.T. (2023) A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction. Big Data and Cognitive Computing, 7, Article 50. [Google Scholar] [CrossRef] 
[40] Arya, N. and Saha, S. (2021) Multi-Modal Advanced Deep Learning Architectures for Breast Cancer Survival Prediction. Knowledge-Based Systems, 221, Article ID: 106965. [Google Scholar] [CrossRef] 

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