影像组学在鼻咽癌诊治中的应用研究进展
Research Progress on the Application of Radiomics in the Diagnosis and Treatment of Nasopharyngeal Carcinoma
DOI: 10.12677/acrem.2025.134058, PDF,   
作者: 苏舒宇：右江民族医学院研究生学院，广西 百色；瞿申红*：广西壮族自治区人民医院耳鼻咽喉头颈外科，广西 南宁
关键词: 鼻咽癌；影像组学；疗效预测；预后；Nasopharyngeal Carcinoma； Imageomics； Efficacy Prediction； Prognosis
摘要: 鼻咽癌(Nasopharyngeal Carcinoma, NPC)具有显著的地域分布特征，早期诊断困难且晚期患者预后较差，放射治疗是鼻咽癌的核心治疗手段，但面临肿瘤异质性高、传统影像评估主观性强等挑战。影像组学作为一种新兴技术，能够高通量提取医学影像中的深层定量特征，为鼻咽癌的精准诊疗提供了新方式。本文对影像组学在鼻咽癌诊治方面的研究进展进行了综述，并展望了其未来发展方向。
Abstract: Nasopharyngeal Carcinoma (NPC) exhibits distinct regional distribution characteristics. Early diagnosis is difficult and the prognosis for advanced-stage patients is poor. Radiotherapy is the core treatment method for NPC. However, it faces challenges such as high tumor heterogeneity and subjective nature of traditional imaging assessment. Imaging genetics, as an emerging technology, can extract deep quantitative features from medical images in a high-throughput manner, providing a new approach for the precise diagnosis and treatment of NPC. This article reviews the research progress of imaging genetics in the diagnosis and treatment of NPC and looks forward to its future development directions.
文章引用：苏舒宇, 瞿申红. 影像组学在鼻咽癌诊治中的应用研究进展[J]. 亚洲急诊医学病例研究, 2025, 13(4): 419-427. https://doi.org/10.12677/acrem.2025.134058

参考文献

[1] Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., et al. (2024) Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 74, 229-263. [Google Scholar] [CrossRef] [PubMed]
[2] Zhang, Y., Rumgay, H., Li, M., Cao, S. and Chen, W. (2023) Nasopharyngeal Cancer Incidence and Mortality in 185 Countries in 2020 and the Projected Burden in 2040: Population-Based Global Epidemiological Profiling. JMIR Public Health and Surveillance, 9, e49968. [Google Scholar] [CrossRef] [PubMed]
[3] Siak, P.Y., Khoo, A.S., Leong, C.O., Hoh, B. and Cheah, S. (2021) Current Status and Future Perspectives about Molecular Biomarkers of Nasopharyngeal Carcinoma. Cancers, 13, Article 3490. [Google Scholar] [CrossRef] [PubMed]
[4] Bossi, P., Chan, A.T., Licitra, L., Trama, A., Orlandi, E., Hui, E.P., et al. (2021) Nasopharyngeal Carcinoma: ESMO-EURACAN Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up. Annals of Oncology, 32, 452-465. [Google Scholar] [CrossRef] [PubMed]
[5] Alabi, R.O., Elmusrati, M., Leivo, I., Almangush, A. and Mäkitie, A.A. (2024) Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects. International Journal of Medical Informatics, 188, Article ID: 105464. [Google Scholar] [CrossRef] [PubMed]
[6] Tang, F.H., Chu, C.Y.W. and Cheung, E.Y.W. (2021) Radiomics AI Prediction for Head and Neck Squamous Cell Carcinoma (HNSCC) Prognosis and Recurrence with Target Volume Approach. BJR|Open, 3, Article ID: 20200073. [Google Scholar] [CrossRef] [PubMed]
[7] Ai, Q., King, A.D., So, T.Y., Lam, W.K.J., Mo, F.K.F., Tse, I.O.L., et al. (2020) MRI of Benign Hyperplasia in the Nasopharynx: Is There an Association with Epstein-Barr Virus? Clinical Radiology, 75, 711.e13-711.e18. [Google Scholar] [CrossRef] [PubMed]
[8] Wong, L.M., King, A.D., Ai, Q.Y.H., Lam, W.K.J., Poon, D.M.C., Ma, B.B.Y., et al. (2020) Convolutional Neural Network for Discriminating Nasopharyngeal Carcinoma and Benign Hyperplasia on MRI. European Radiology, 31, 3856-3863. [Google Scholar] [CrossRef] [PubMed]
[9] Zhang, H., Hu, L., Qin, F., Chang, J., Zhong, Y., Dou, W., et al. (2024) Synthetic MRI and Diffusion-Weighted Imaging for Differentiating Nasopharyngeal Lymphoma from Nasopharyngeal Carcinoma: Combination with Morphological Features. British Journal of Radiology, 97, 1278-1285. [Google Scholar] [CrossRef] [PubMed]
[10] Li, Q., Yu, Q., Gong, B., Ning, Y., Chen, X., Gu, J., et al. (2023) The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating Stage I-II and III-IVA Nasopharyngeal Carcinoma. Diagnostics, 13, Article 300. [Google Scholar] [CrossRef] [PubMed]
[11] Feng, Q., Liang, J., Wang, L., Ge, X., Ding, Z. and Wu, H. (2022) A Diagnosis Model in Nasopharyngeal Carcinoma Based on PET/MRI Radiomics and Semiquantitative Parameters. BMC Medical Imaging, 22, Article No. 150. [Google Scholar] [CrossRef] [PubMed]
[12] Lin, M., Tang, X., Cao, L., Liao, Y., Zhang, Y. and Zhou, J. (2022) Using Ultrasound Radiomics Analysis to Diagnose Cervical Lymph Node Metastasis in Patients with Nasopharyngeal Carcinoma. European Radiology, 33, 774-783. [Google Scholar] [CrossRef] [PubMed]
[13] Tseng, H., Shen, C., Kao, P., Chuang, C., Yan, D., Liao, Y., et al. (2024) Prediction of Persistent Tumor Status in Nasopharyngeal Carcinoma Post-Radiotherapy-Related Treatment: A Machine Learning Approach. Cancers, 17, Article 96. [Google Scholar] [CrossRef] [PubMed]
[14] Liao, H., Zhao, Y., Pei, W., Huang, X., Huang, S., Wei, W., et al. (2025) An Interpretable Machine Learning Model Assists in Predicting Induction Chemotherapy Response and Survival for Locoregionally Advanced Nasopharyngeal Carcinoma Using MRI: A Multicenter Study. European Radiology, 35, 5121-5134. [Google Scholar] [CrossRef] [PubMed]
[15] Pan, G., Sun, X., Kong, F., Wang, J., He, X., Lu, X., et al. (2024) Delta Magnetic Resonance Imaging Radiomics Featuresbased Nomogram Predicts Long-Term Efficacy after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma. Oral Oncology, 157, Article ID: 106987. [Google Scholar] [CrossRef] [PubMed]
[16] Chen, Z., Han, X., Lin, L., Lin, G., Li, B., Kou, J., et al. (2025) Harnessing Deep Learning to Optimize Induction Chemotherapy Choices in Nasopharyngeal Carcinoma. Radiotherapy and Oncology, 211, Article ID: 111047. [Google Scholar] [CrossRef] [PubMed]
[17] Wu, Q., Qiang, W., Pan, L., Cha, T., Li, Q., Gao, Y., et al. (2025) Performance of MRI-Based Radiomics for Prediction of Residual Disease Status in Patients with Nasopharyngeal Carcinoma after Radical Radiotherapy. Scientific Reports, 15, Article No. 16758. [Google Scholar] [CrossRef] [PubMed]
[18] Wang, X., Song, J., Qiu, Q., Su, Y., Wang, L. and Cao, X. (2025) A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma. Academic Radiology, 32, 1631-1644. [Google Scholar] [CrossRef] [PubMed]
[19] Mai, H., Li, L., Xin, X., Jiang, Z., Tang, Y., Huang, J., et al. (2025) Prediction of Immunotherapy Response in Nasopharyngeal Carcinoma: A Comparative Study Using MRI-Based Radiomics Signature and Programmed Cell Death Ligand 1 Expression Score. European Radiology, 35, 4403-4414. [Google Scholar] [CrossRef] [PubMed]
[20] Sun, J., Wu, X., Zhang, X., Huang, W., Zhong, X., Li, X., et al. (2025) Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma. Research, 8, Article ID: 0749. [Google Scholar] [CrossRef] [PubMed]
[21] Liu, Z., Zang, S., OuYang, P., Jing, B., Zheng, C., Zhao, Y., et al. (2025) Multimodal Prognostic Model for de Novo Metastatic Nasopharyngeal Carcinoma after First-Line Immunochemotherapy. Radiotherapy and Oncology, 210, Article ID: 111010. [Google Scholar] [CrossRef] [PubMed]
[22] Jin, X., Li, W., Guo, Y., Wu, G., Huang, W. and Chen, F. (2025) Predicting Progression-Free Survival Using Dynamic Contrast-Enhanced Imaging-Based Radiomics in Advanced Nasopharyngeal Carcinoma Patients Treated with Nimotuzumab. European Radiology, 35, 5135-5145. [Google Scholar] [CrossRef] [PubMed]
[23] Ritlumlert, N., Wongwattananard, S., Prayongrat, A., Oonsiri, S., Kitpanit, S., Kannarunimit, D., et al. (2023) Improved Prediction of Radiation-Induced Hypothyroidism in Nasopharyngeal Carcinoma Using Pre-Treatment CT Radiomics. Scientific Reports, 13, Article No. 17437. [Google Scholar] [CrossRef] [PubMed]
[24] Wang, M., Xi, Y., Wang, L., Chen, H., Jiang, F. and Ding, Z. (2024) Predictive Value of Delta Radiomics in Xerostomia after Chemoradiotherapy in Patients with Stage III-IV Nasopharyngeal Carcinoma. Radiation Oncology, 19, Article No. 26. [Google Scholar] [CrossRef] [PubMed]
[25] Zhong, X., Li, L., Jiang, H., Yin, J., Lu, B., Han, W., et al. (2020) Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization. BMC Medical Imaging, 20, Article No. 104. [Google Scholar] [CrossRef] [PubMed]
[26] Yang, S., Peng, Q., Wu, A., Zhang, B., Liu, Z., Chen, E., et al. (2023) Radiomics and Dosiomics for Predicting Radiation-Induced Hypothyroidism and Guiding Intensity-Modulated Radiotherapy. iScience, 26, Article ID: 108394. [Google Scholar] [CrossRef] [PubMed]
[27] Gao, X., Peng, Y., Lu, S., An, Y., Chen, M., Zhang, J., et al. (2025) Deep Learning-Based Multi-Omics Model to Predict Nasopharyngeal Necrosis of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma. Frontiers in Oncology, 15, Article 1607218. [Google Scholar] [CrossRef] [PubMed]
[28] Chen, L., Wang, Z., Meng, Y., Zhao, C., Wang, X., Zhang, Y., et al. (2024) A Clinical-Radiomics Nomogram Based on Multisequence MRI for Predicting the Outcome of Patients with Advanced Nasopharyngeal Carcinoma Receiving Chemoradiotherapy. Frontiers in Oncology, 14, Article 1460426. [Google Scholar] [CrossRef] [PubMed]
[29] Kou, J., Peng, J., Lv, W., Wu, C., Chen, Z., Zhou, G., et al. (2025) A Serial MRI-Based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma. Radiology: Artificial Intelligence, 7, e230544. [Google Scholar] [CrossRef] [PubMed]
[30] Lin, D., Li, H., Liu, T., Lv, X., Xie, C., Ou, X., et al. (2024) Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Survival in Locally Recurrent Nasopharyngeal Carcinoma. JNCI: Journal of the National Cancer Institute, 116, 1294-1302. [Google Scholar] [CrossRef] [PubMed]
[31] Hu, C., Xu, C., Chen, J., Huang, Y., Meng, Q., Lin, Z., et al. (2025) Deep Learning MRI-Based Radiomic Models for Predicting Recurrence in Locally Advanced Nasopharyngeal Carcinoma after Neoadjuvant Chemoradiotherapy: A Multi-Center Study. Clinical & Experimental Metastasis, 42, Article No. 30. [Google Scholar] [CrossRef] [PubMed]
[32] Zhu, C., Huang, H., Liu, X., Chen, H., Jiang, H., Liao, C., et al. (2021) A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence after Radiotherapy in Nasopharyngeal Carcinoma. Frontiers in Oncology, 11, Article 637687. [Google Scholar] [CrossRef] [PubMed]

为你推荐