MRI评估直肠癌淋巴结转移和肿瘤沉积应用进展
Application Advances in MRI-Based Evaluation of Rectal Cancer Lymph Node Metastasis and Tumor Deposits
DOI: 10.12677/acm.2025.15123630, PDF,   
作者: 赵 欢, 罗银灯*：重庆医科大学附属第二医院，放射科，重庆
关键词: 直肠癌；淋巴结转移；肿瘤沉积；磁共振；Rectal Cancer； Lymph Node Metastasis； Tumor Deposits； Magnetic Resonance Imaging
摘要: 直肠癌是全球癌症相关死亡的主要原因之一，精准的术前分期对于制定个体化治疗策略至关重要。在TNM分期系统中，淋巴结转移(LNM)与肿瘤沉积(TD)都是N分期的关键组成部分，分期可以综合反映肿瘤严重程度和发展阶段，指导个性化治疗策略并且预测癌症预后。磁共振成像(MRI)因其优异的软组织分辨能力，已成为直肠癌术前分期的主要影像学工具。本文系统论述了多模态MRI技术以及新兴的影像组学和深度学习算法在直肠癌淋巴结转移和肿瘤沉积评估中的应用价值。同时，本文还分析了基于MRI的预测模型在临床实践中的挑战与未来发展方向，旨在为放射科医师和结直肠癌多学科团队提供全面的参考依据，推动直肠癌精准分期的发展。
Abstract: Colorectal cancer is one of the leading causes of cancer-related mortality worldwide, and accurate preoperative staging is crucial for developing individualized treatment strategies. In the TNM staging system, lymph node metastasis (LNM) and tumor deposits (TD) are key components of the N stage. This staging reflects the severity and progression of the tumor, guiding personalized treatment strategies and predicting cancer prognosis. Magnetic resonance imaging (MRI), with its superior soft tissue resolution, has become the primary imaging modality for preoperative staging of rectal cancer. This article systematically discusses the application of multimodal MRI techniques, as well as emerging radiomics and deep learning algorithms, in the evaluation of rectal cancer lymph node metastasis and tumor deposits. Additionally, the challenges and future directions of MRI-based predictive models in clinical practice are analyzed. The aim of this review is to provide comprehensive reference material for radiologists and colorectal cancer multidisciplinary teams, advancing the development of precise rectal cancer staging.
文章引用：赵欢, 罗银灯. MRI评估直肠癌淋巴结转移和肿瘤沉积应用进展[J]. 临床医学进展, 2025, 15(12): 2087-2093. https://doi.org/10.12677/acm.2025.15123630

参考文献

[1] Siegel, R.L., Giaquinto, A.N. and Jemal, A. (2024) Cancer Statistics, 2024. CA: A Cancer Journal for Clinicians, 74, 12-49. [Google Scholar] [CrossRef] [PubMed]
[2] Nougaret, S., Gormly, K., Lambregts, D.M.J., Reinhold, C., Goh, V., Korngold, E., et al. (2025) MRI of the Rectum: A Decade into DISTANCE, Moving to Distanced. Radiology, 314, e232838. [Google Scholar] [CrossRef] [PubMed]
[3] Bhutiani, N., Peacock, O., Uppal, A., Hu, C., Bednarski, B.K., Taggart, M.W., et al. (2024) The Prognostic Impact of Tumor Deposits in Colorectal Cancer: More than Just N1c. Cancer, 130, 4052-4060. [Google Scholar] [CrossRef] [PubMed]
[4] Weiser, M.R. (2018) AJCC 8th Edition: Colorectal Cancer. Annals of Surgical Oncology, 25, 1454-1455. [Google Scholar] [CrossRef] [PubMed]
[5] Taylor, F.G.M., Quirke, P., Heald, R.J., Moran, B., Blomqvist, L., Swift, I., et al. (2011) Preoperative High-Resolution Magnetic Resonance Imaging Can Identify Good Prognosis Stage I, II, and III Rectal Cancer Best Managed by Surgery Alone: A Prospective, Multicenter, European Study. Annals of Surgery, 253, 711-719. [Google Scholar] [CrossRef] [PubMed]
[6] Horvat, N., Carlos Tavares Rocha, C., Clemente Oliveira, B., Petkovska, I. and Gollub, M.J. (2019) MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management. Radio Graphics, 39, 367-387. [Google Scholar] [CrossRef] [PubMed]
[7] Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., et al. (2017) A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60-88. [Google Scholar] [CrossRef] [PubMed]
[8] Al-Sukhni, E., Milot, L., Fruitman, M., Beyene, J., Victor, J.C., Schmocker, S., et al. (2012) Diagnostic Accuracy of MRI for Assessment of T Category, Lymph Node Metastases, and Circumferential Resection Margin Involvement in Patients with Rectal Cancer: A Systematic Review and Meta-Analysis. Annals of Surgical Oncology, 19, 2212-2223. [Google Scholar] [CrossRef] [PubMed]
[9] Park, J.S., Jang, Y.J., Choi, G.S., Park, S.Y., et al. (2014) Accuracy of Preoperative MRI in Predicting Pathology Stage in Rectal Cancers: Node-for-Node Matched Histopathology Validation of MRI Features. Diseases of the Colon & Rectum, 57, 32-38. [Google Scholar] [CrossRef] [PubMed]
[10] Langman, G., Patel, A. and Bowley, D.M. (2015) Size and Distribution of Lymph Nodes in Rectal Cancer Resection Specimens. Diseases of the Colon & Rectum, 58, 406-414. [Google Scholar] [CrossRef] [PubMed]
[11] Zhou, M., Chen, M., Luo, M., Chen, M. and Huang, H. (2024) Pathological Prognostic Factors of Rectal Cancer Based on Diffusion-Weighted Imaging, Intravoxel Incoherent Motion, and Diffusion Kurtosis Imaging. European Radiology, 35, 979-988. [Google Scholar] [CrossRef] [PubMed]
[12] Yin, H., Liu, W., Xue, Q., Song, C., Ren, J., Li, Z., et al. (2024) The Value of Restriction Spectrum Imaging in Predicting Lymph Node Metastases in Rectal Cancer: A Comparative Study with Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging. Insights into Imaging, 15, Article No. 302. [Google Scholar] [CrossRef] [PubMed]
[13] 康立清, 郭素引, 赵梦, 刘凤海, 邢荣格, 姜国胜, 李国策. IVIM-DWI联合DCE-MRI诊断直肠癌盆腔淋巴结转移的价值[J]. 磁共振成像, 2019, 10(8): 583-588.
[14] Yang, Y.S., Feng, F., Qiu, Y.J., Zheng, G.H., Ge, Y.Q. and Wang, Y.T. (2021) High-Resolution MRI-Based Radiomics Analysis to Predict Lymph Node Metastasis and Tumor Deposits Respectively in Rectal Cancer. Abdominal Radiology, 46, 873-884. [Google Scholar] [CrossRef] [PubMed]
[15] Zheng, Y., Chen, X., Zhang, H., Ning, X., Mao, Y., Zheng, H., et al. (2024) Multiparametric MRI-Based Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Rectal Cancer: A Two-Center Study. European Journal of Radiology, 178, Article 111591. [Google Scholar] [CrossRef] [PubMed]
[16] Ao, W., Wu, S., Wang, N., Mao, G., Wang, J., Hu, J., et al. (2025) Novel Deep Learning Algorithm Based MRI Radiomics for Predicting Lymph Node Metastases in Rectal Cancer. Scientific Reports, 15, Article No. 12089. [Google Scholar] [CrossRef] [PubMed]
[17] Xia, W., Li, D., He, W., Pickhardt, P.J., Jian, J., Zhang, R., et al. (2024) Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI. Radiology: Artificial Intelligence, 6, e230152. [Google Scholar] [CrossRef] [PubMed]
[18] Zheng, P., Chen, Q., Li, J., Jin, C., Kang, L. and Chen, D. (2020) Prognostic Significance of Tumor Deposits in Patients with Stage III Colon Cancer: A Nomogram Study. Journal of Surgical Research, 245, 475-482. [Google Scholar] [CrossRef] [PubMed]
[19] Agger, E., Jörgren, F., Jöud, A., Lydrup, M.L. and Buchwald, P. (2023) Negative Prognostic Impact of Tumor Deposits in Rectal Cancer: A National Study Cohort. Annals of Surgery, 278, e526-e533. [Google Scholar] [CrossRef] [PubMed]
[20] Song, J.S., Chang, H.J., Kim, D.Y., Kim, S.Y., Baek, J.Y., Park, J.W., et al. (2011) Is the N1c Category of the New American Joint Committee on Cancer Staging System Applicable to Patients with Rectal Cancer Who Receive Preoperative Chemoradiotherapy? Cancer, 117, 3917-3924. [Google Scholar] [CrossRef] [PubMed]
[21] Hu, X., Xie, S., Yang, Z., Zhao, X., Gong, L., Yang, P., et al. (2025) Aggressive Characteristics of Tumor Deposits in Colorectal Cancer Highlight the Need for Staging Refinement in Patients with 0-3 Metastatic Lymph Nodes. International Journal of Cancer, 156, 1826-1839. [Google Scholar] [CrossRef] [PubMed]
[22] Nagtegaal, I.D., Knijn, N., Hugen, N., Marshall, H.C., Sugihara, K., Tot, T., et al. (2017) Tumor Deposits in Colorectal Cancer: Improving the Value of Modern Staging—A Systematic Review and Meta-Analysis. Journal of Clinical Oncology, 35, 1119-1127. [Google Scholar] [CrossRef] [PubMed]
[23] Marjasuo, S.T., Lehtimäki, T.E., Koskenvuo, L.E. and Lepistö, A.H. (2024) Impact of Mesorectal Extranodal Tumor Deposits in Magnetic Resonance Imaging on Outcome of Rectal Cancer Patients. European Journal of Surgical Oncology, 50, Article 108337. [Google Scholar] [CrossRef] [PubMed]
[24] Yuan, Y., Chen, X.L., Li, Z.L., Chen, G.W., et al. (2022) The Application of Apparent Diffusion Coefficients Derived from Intratumoral and Peritumoral Zones for Assessing Pathologic Prognostic Factors in Rectal Cancer. European Radiology, 32, 5106-5118. [Google Scholar] [CrossRef] [PubMed]
[25] Hong, Y., Song, G., Jia, Y., Wu, R., He, R. and Li, A. (2022) Predicting Tumor Deposits in Patients with Rectal Cancer: Using the Models of Multiple Mathematical Parameters Derived from Diffusion-Weighted Imaging. European Journal of Radiology, 157, Article 110573. [Google Scholar] [CrossRef] [PubMed]
[26] Xu, Q., Xu, Y., Wang, J., Sun, H., Lin, J. and Xie, S. (2023) Distinguishing Mesorectal Tumor Deposits from Metastatic Lymph Nodes by Using Diffusion-Weighted and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Rectal Cancer. European Radiology, 33, 4127-4137. [Google Scholar] [CrossRef] [PubMed]
[27] Ao, W., Wang, N., Chen, X., Wu, S., Mao, G., Hu, J., et al. (2024) Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome. Academic Radiology, 32, 1451-1464. [Google Scholar] [CrossRef] [PubMed]
[28] Fu, C., Shao, T., Hou, M., Qu, J., Li, P., Yang, Z., et al. (2023) Preoperative Prediction of Tumor Deposits in Rectal Cancer with Clinical-Magnetic Resonance Deep Learning-Based Radiomic Models. Frontiers in Oncology, 13, Article 1078863. [Google Scholar] [CrossRef] [PubMed]
[29] Lambregts, D.M.J., Bogveradze, N., Blomqvist, L.K., Fokas, E., Garcia-Aguilar, J., Glimelius, B., et al. (2022) Current Controversies in TNM for the Radiological Staging of Rectal Cancer and How to Deal with Them: Results of a Global Online Survey and Multidisciplinary Expert Consensus. European Radiology, 32, 4991-5003. [Google Scholar] [CrossRef] [PubMed]
[30] Yang, R., Zhao, H., Wang, X., Ding, Z., Tao, Y., Zhang, C., et al. (2023) Magnetic Resonance Imaging Radiomics Modeling Predicts Tumor Deposits and Prognosis in Stage T3 Lymph Node Positive Rectal Cancer. Abdominal Radiology, 48, 1268-1279. [Google Scholar] [CrossRef] [PubMed]
[31] Wang, X., Cheng, W., Dou, X., Tan, F., Yan, S., Zhou, Z., et al. (2023) The New ‘coN’ Staging System Combining Lymph Node Metastasis and Tumour Deposit Provides a More Accurate Prognosis for TNM Stage III Colon Cancer. Cancer Medicine, 12, 2538-2550. [Google Scholar] [CrossRef] [PubMed]
[32] Pyo, D.H., Kim, S.H., Ha, S.Y., Yun, S.H., Cho, Y.B., Huh, J.W., et al. (2023) Revised Nodal Staging Integrating Tumor Deposit Counts with Positive Lymph Nodes in Patients with Stage III Colon Cancer. Annals of Surgery, 277, e825-e831. [Google Scholar] [CrossRef] [PubMed]
[33] Sassun, R., Sileo, A., Ng, J.C., Violante, T., Gomaa, I., Mandrekar, J., et al. (2025) Validated Integration of Tumor Deposits in N Staging for Prognostication in Colon Cancer. JAMA Surgery, 160, 408-414. [Google Scholar] [CrossRef] [PubMed]
[34] Cohen, R., Shi, Q., Meyers, J., Jin, Z., Svrcek, M., Fuchs, C., et al. (2021) Combining Tumor Deposits with the Number of Lymph Node Metastases to Improve the Prognostic Accuracy in Stage III Colon Cancer: A Post Hoc Analysis of the CALGB/SWOG 80702 Phase III Study (Alliance). Annals of Oncology, 32, 1267-1275. [Google Scholar] [CrossRef] [PubMed]
[35] Lord, A.C., D’Souza, N., Shaw, A., Rokan, Z., Moran, B., Abulafi, M., et al. (2022) MRI-Diagnosed Tumor Deposits and EMVI Status Have Superior Prognostic Accuracy to Current Clinical TNM Staging in Rectal Cancer. Annals of Surgery, 276, 334-344. [Google Scholar] [CrossRef] [PubMed]
[36] Huang, H., Xu, W., Feng, L., Zhong, M., Ye, Y., Liu, Y., et al. (2025) Development and Evaluation of the mrTE Scoring System for MRI-Detected Tumor Deposits and Extramural Venous Invasion in Rectal Cancer. Abdominal Radiology, 50, 3950-3961. [Google Scholar] [CrossRef] [PubMed]

为你推荐