Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2023, Vol. 37 ›› Issue (2): 135-142.doi: 10.6040/j.issn.1673-3770.0.2022.089

• 综述 • Previous Articles     Next Articles

Research progress on the application of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma

LIU Jiayu, FAN Huiming, ZOU You, CHEN Shiming   

  1. Department of Otorhinolaryngology & Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
  • Published:2023-03-30

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Abstract: The incidence of nasopharyngeal carcinoma is high in southern China. As traditional diagnosis and treatment methods are affected by both low efficiency and subjective and objective factors, a more objective, stable and efficient method of diagnosis and treatment is urgently needed. Artificial intelligence has been widely used in tumor diagnosis and treatment, and has shown high accuracy in image recognition, segmentation, risk prediction, and efficacy prediction. In addition, the application of AI models to assist clinicians in diagnosis and treatment can significantly reduce the amount of time required, improve the accuracy of clinicians' performance, and reduce observer variability among physicians, establishing conditions for enhanced tumor diagnosis and treatment. In this paper, the application status and value of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma are reviewed and summarized, and its future development direction is discussed.

Key words: Nasopharyngeal carcinoma, Artificial intelligence, Machine learning, Deep learning, Convolutional neural network, Support vector machine

CLC Number: 

  • R766.3
[1] Xia CF, Yu XQ, Zheng RS, et al. Spatial and temporal patterns of nasopharyngeal carcinoma mortality in China, 1973-2005[J]. Cancer Lett, 2017, 401: 33-38. doi:10.1016/j.canlet.2017.04.016
[2] Chen YP, Chan ATC, Le QT, et al. Nasopharyngeal carcinoma[J]. Lancet, 2019, 394(10192): 64-80. doi:10.1016/S0140-6736(19)30956-0
[3] King AD, Vlantis AC, Bhatia KS, et al. Primary nasopharyngeal carcinoma: diagnostic accuracy of MR imaging versus that of endoscopy and endoscopic biopsy[J]. Radiology, 2011, 258(2): 531-537. doi:10.1148/radiol.10101241
[4] King AD, Woo JKS, Ai QY, et al. Complementary roles of MRI and endoscopic examination in the early detection of nasopharyngeal carcinoma[J]. Ann Oncol, 2019, 30(6): 977-982. doi:10.1093/annonc/mdz106
[5] Savic M, Ma YH, Ramponi G, et al. Lung nodule segmentation with a region-based fast marching method[J]. Sensors(Basel), 2021, 21(5): 1908. doi:10.3390/s21051908
[6] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. doi:10.1038/nature21056
[7] Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. J Clin Oncol, 2016, 34(18): 2157-2164. doi:10.1200/JCO.2015.65.9128
[8] Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157. doi:10.3322/caac.21552
[9] Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25(1): 24-29. doi:10.1038/s41591-018-0316-z
[10] Soffer S, Ben-Cohen A, Shimon O, et al. Convolutional neural networks for radiologic images: a radiologist's guide[J]. Radiology, 2019, 290(3): 590-606. doi:10.1148/radiol.2018180547
[11] Wong LM, King AD, Ai QYH, et al. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI[J]. Eur Radiol, 2021, 31(6): 3856-3863. doi:10.1007/s00330-020-07451-y
[12] Ke LR, Deng YS, Xia WX, et al. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images[J]. Oral Oncol, 2020, 110: 104862. doi:10.1016/j.oraloncology.2020.104862
[13] Wang HY, Han GQ, Li HJ, et al. A collaborative dictionary learning model for nasopharyngeal carcinoma segmentation on multimodalities MR sequences[J]. Comput Math Methods Med, 2020: 7562140. doi:10.1155/2020/7562140
[14] Daoud B, Morooka K, Kurazume R, et al. 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning[J]. Comput Med Imaging Graph, 2019, 77: 101644. doi:10.1016/j.compmedimag.2019.101644
[15] Wu BX, Khong PL, Chan T. Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine[J]. Int J Comput Assist Radiol Surg, 2012, 7(4): 635-646. doi:10.1007/s11548-011-0669-y
[16] Li CF, Jing BZ, Ke LR, et al. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies[J]. Cancer Commun(Lond), 2018, 38(1): 59. doi:10.1186/s40880-018-0325-9
[17] Žuvela P, Lin K, Shu C, et al. Fiber-optic Raman spectroscopy with nature-inspired genetic algorithms enhances real-time in vivo detection and diagnosis of nasopharyngeal carcinoma[J]. Anal Chem, 2019, 91(13): 8101-8108. doi:10.1021/acs.analchem.9b00173
[18] Shu C, Yan HS, Zheng W, et al. Deep learning-guided fiberoptic Raman spectroscopy enables real-time in vivo diagnosis and assessment of nasopharyngeal carcinoma and post-treatment efficacy during endoscopy[J]. Anal Chem, 2021, 93(31): 10898-10906. doi:10.1021/acs.analchem.1c01559
[19] Diao SH, Hou JX, Yu H, et al. Computer-aided pathologic diagnosis of nasopharyngeal carcinoma based on deep learning[J]. Am J Pathol, 2020, 190(8): 1691-1700. doi:10.1016/j.ajpath.2020.04.008
[20] Chen X, Li YX, Li X, et al. An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features[J]. Oral Oncol, 2021, 118: 105335. doi:10.1016/j.oraloncology.2021.105335
[21] Grégoire V, Ang K, Budach W, et al. Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines[J]. Radiother Oncol, 2014, 110(1): 172-181. doi:10.1016/j.radonc.2013.10.010
[22] Shen C, Liu ZY, Wang ZQ, et al. Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction[J]. Transl Oncol, 2018, 11(3): 815-824. doi:10.1016/j.tranon.2018.04.005
[23] Bayanati H, E Thornhill R, Souza CA, et al. Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? [J]. Eur Radiol, 2015, 25(2): 480-487. doi:10.1007/s00330-014-3420-6
[24] 陈海兵, 卫亚楠, 许晓泉, 等. 基于XGBoost人工智能结合CT构建甲状腺癌颈部淋巴结转移预测模型[J]. 山东大学耳鼻喉眼学报, 2020, 34(3): 40-45. doi:10.6040/j.issn.1673-3770.1.2020.031 CHEN Haibing, WEI Yanan, XU Xiaoquan, et al. Prediction of cervical lymph node metastasis in papillary thyroid cancer based on XGBoost artificial intelligence and enhanced computed tomography[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(3): 40-45. doi:10.6040/j.issn.1673-3770.1.2020.031
[25] 刘渊, 程玉玉, 贺睿敏, 等. 基于机器学习的鼻咽癌转移淋巴结鉴别模型[J]. 中国医学物理学杂志, 2019, 36(11): 1350-1355. doi:10.3969/j.issn.1005-202X.2019.11.020 LIU Yuan, CHENG Yuyu, HE Ruimin, et al. Machine learning-based classification model of lymph node metastasis in nasopharyngeal carcinoma[J]. Chinese Journal of Medical Physics, 2019, 36(11): 1350-1355. doi:10.3969/j.issn.1005-202X.2019.11.020
[26] Mao YP, Xie FY, Liu LZ, et al. re-evaluation of 6th edition of AJCC staging system for nasopharyngeal carcinoma and proposed improvement based on magnetic resonance imaging[J]. Int J Radiat Oncol Biol Phys, 2009, 73(5): 1326-1334. doi:10.1016/j.ijrobp.2008.07.062
[27] Liu LT, Chen QY, Tang LQ, et al. Advanced-stage nasopharyngeal carcinoma: restaging system after neoadjuvant chemotherapy on the basis of MR imaging determines survival[J]. Radiology, 2017, 282(1): 171-181. doi:10.1148/radiol.2016152540
[28] Jing BZ, Zhang T, Wang ZX, et al. A deep survival analysis method based on ranking[J]. Artif Intell Med, 2019, 98: 1-9. doi:10.1016/j.artmed.2019.06.001
[29] Jing BZ, Deng YS, Zhang T, et al. Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs[J]. Comput Methods Programs Biomed, 2020, 197: 105684. doi:10.1016/j.cmpb.2020.105684
[30] Cui CY, Wang SX, Zhou J, et al. Machine learning analysis of image data based on detailed MR image reports for nasopharyngeal carcinoma prognosis[J]. Biomed Res Int, 2020: 8068913. doi:10.1155/2020/8068913
[31] Zhuo EH, Zhang WJ, Li HJ, et al. Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma(NPC)into distinct survival subgroups[J]. Eur Radiol, 2019, 29(10): 5590-5599. doi:10.1007/s00330-019-06075-1
[32] Xie CY, Du R, Ho JW, et al. Effect of machine learning re-sampling techniques for imbalanced datasets in 18 F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients[J]. Eur J Nucl Med Mol Imaging, 2020, 47(12): 2826-2835. doi:10.1007/s00259-020-04756-4
[33] Tang LQ, Li CF, Li J, et al. Establishment and validation of prognostic nomograms for endemic nasopharyngeal carcinoma[J]. J Natl Cancer Inst, 2015, 108(1): 291. doi: 10.1093/jnci/djv291
[34] 林琰超. 基于深度学习的鼻咽癌分类诊断以及远转预测[D]. 青岛: 山东科技大学, 2018.
[35] Wu XJ, Dong D, Zhang L, et al. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: a multicenter study[J]. Med Phys, 2021, 48(5): 2374-2385. doi:10.1002/mp.14767
[36] Zhang L, Wu XJ, Liu J, et al. MRI-based deep-learning model for distant metastasis-free survival in locoregionally advanced nasopharyngeal carcinoma[J]. J Magn Reson Imaging, 2021, 53(1): 167-178. doi:10.1002/jmri.27308
[37] Zhang B, He X, Ouyang FS, et al. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma[J]. Cancer Lett, 2017, 403: 21-27. doi:10.1016/j.canlet.2017.06.004
[38] 孙笑晗, 李娜. 西班牙医学肿瘤学会鼻咽癌临床指南(2017)介绍[J]. 山东大学耳鼻喉眼学报, 2019, 33(2): 57-59. doi:10.6040/j.issn.1673-3770.1.2019.017 SUN Xiaohan, LI Na. Introduction to the SEOM clinical guidelines for nasopharyngeal cancer(2017)[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2019, 33(2): 57-59. doi:10.6040/j.issn.1673-3770.1.2019.017
[39] Chua ML, Sun Y, Supiot S. Advances in nasopharyngeal carcinoma - “west meets east”[J]. Br J Radiol, 2019, 92(1102): 20199004. doi:10.1259/bjr.20199004
[40] Kam MKM, Teo PML, Chau RMC, et al. Treatment of nasopharyngeal carcinoma with intensity-modulated radiotherapy: the Hong Kong experience[J]. Int J Radiat Oncol Biol Phys, 2004, 60(5): 1440-1450. doi:10.1016/j.ijrobp.2004.05.022
[41] Chen AM, Chin R, Beron P, et al. Inadequate target volume delineation and local-regional recurrence after intensity-modulated radiotherapy for human papillomavirus-positive oropharynx cancer[J]. Radiother Oncol, 2017, 123(3): 412-418. doi:10.1016/j.radonc.2017.04.015
[42] Lin L, Dou Q, Jin YM, et al. Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma[J]. Radiology, 2019, 291(3): 677-686. doi:10.1148/radiol.2019182012
[43] Yang G, Dai ZH, Zhang YW, et al. Multiscale local enhancement deep convolutional networks for the automated 3D segmentation of gross tumor volumes in nasopharyngeal carcinoma: a multi-institutional dataset study[J]. Front Oncol, 2022, 12: 827991. doi:10.3389/fonc.2022.827991
[44] Zhang JJ, Gu L, Han GH, et al. AttR2U-net: a fully automated model for MRI nasopharyngeal carcinoma segmentation based on spatial attention and residual recurrent convolution[J]. Front Oncol, 2021, 11: 816672. doi:10.3389/fonc.2021.816672
[45] 肖银燕, 全惠敏. 基于3D CNN的鼻咽癌CT图像分割[J]. 计算机工程与科学, 2019, 41(8): 1444-1452. doi:10.3969/j.issn.1007-130X.2019.08.015 XIAO Yinyan, QUAN Huimin. A nasopharyngeal carcinoma CT image segmentation method based on 3D CNNs[J]. Computer Engineering and Science, 2019, 41(8): 1444-1452. doi:10.3969/j.issn.1007-130X.2019.08.015
[46] Men K, Chen XY, Zhang Y, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images[J]. Front Oncol, 2017, 7: 315. doi:10.3389/fonc.2017.00315
[47] Hvid CA, Elstrøm UV, Jensen K, et al. Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy[J]. Acta Oncol, 2016, 55(11): 1324-1330. doi:10.1080/0284186X.2016.1185149
[48] Kim N, Chang JS, Kim YB, et al. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers[J]. Radiat Oncol, 2020, 15(1): 106. doi:10.1186/s13014-020-01562-y
[49] 李金凯, 王沛沛, 曹远东, 等. AccuContour软件在头颈部危及器官自动勾画中的应用研究[J]. 中国医疗设备, 2021, 36(6): 66-70. doi:10.3969/j.issn.1674-1633.2021.06.017 LI Jinkai, WANG Peipei, CAO Yuandong, et al. Research on application of AccuContour software in automatic delineation of organs at risk in head and neck[J]. China Medical Devices, 2021, 36(6): 66-70. doi:10.3969/j.issn.1674-1633.2021.06.017
[50] 谢辉, 李庆. OIS软件在鼻咽癌危及器官自动勾画的临床应用研究[J]. 中国数字医学, 2020, 15(11): 36-39. doi:10.3969/j.issn.1673-7571.2020.11.009. XIE Hui, LI Qing. Research on the clinical application of OIS software in the automatic outlining of organ at risk of nasopharyngeal carcinoma[J]. China Digital Medicine, 2020, 15(11): 36-39. doi:10.3969/j.issn.1673-7571.2020.11.009.
[51] Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks[J]. Med Phys, 2017, 44(2): 547-557. doi:10.1002/mp.12045.
[52] 蒋家良, 罗勇, 何奕松, 等. 特征区域再聚焦提升全卷积神经网络勾画较小靶区准确度[J]. 中国医学物理学杂志, 2020, 37(1): 75-78. doi:10.3969/j.issn.1005-202X.2020.01.015. JIANG Jialiang, LUO Yong, HE Yisong, et al. Feature area refocusing for improving the accuracy of small target area segmentations by fully convolutional networks[J]. Chinese Journal of Medical Physics, 2020, 37(1): 75-78. doi:10.3969/j.issn.1005-202X.2020.01.015.
[53] Zhong T, Huang X, Tang F, et al. Boosting-based cascaded convolutional neural networks for the segmentation of CT organs-at-risk in nasopharyngeal carcinoma[J]. Med Phys, 2019,16. doi:10.1002/mp.13825
[54] Liang SJ, Tang F, Huang X, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning[J]. Eur Radiol, 2019, 29(4): 1961-1967. doi:10.1007/s00330-018-5748-9
[55] 董迪, 巩立鑫, 王坤, 等. 影像组学的临床应用[J]. 中国科学基金, 2021, 35(1): 85-91. doi:10.16262/j.cnki.1000-8217.2021.01.019 DONG Di, GONG Lixin, WANG Kun, et al. Clinical applications of radiomics[J]. Bulletin of National Natural Science Foundation of China, 2021, 35(1): 85-91. doi:10.16262/j.cnki.1000-8217.2021.01.019
[56] Liu KY, Xia WX, Qiang MY, et al. Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy[J]. Cancer Med, 2020, 9(4): 1298-1306. doi:10.1002/cam4.2802
[57] Qiang MY, Li CF, Sun YY, et al. A prognostic predictive system based on deep learning for locoregionally advanced nasopharyngeal carcinoma[J]. J Natl Cancer Inst, 2021, 113(5): 606-615. doi:10.1093/jnci/djaa149
[58] Peng H, Dong D, Fang MJ, et al. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma[J]. Clin Cancer Res, 2019, 25(14): 4271-4279. doi:10.1158/1078-0432.CCR-18-3065
[59] Zhao LN, Gong J, Xi YB, et al. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma[J]. Eur Radiol, 2020, 30(1): 537-546. doi:10.1007/s00330-019-06211-x
[60] Chen XY, Men K, Zhu J, et al. DVHnet: a deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning[J]. Med Phys, 2021, 48(6): 2705-2713. doi:10.1002/mp.14758
[61] Lan MY, Yang WL, Lin KT, et al. Using computational strategies to predict potential drugs for nasopharyngeal carcinoma[J]. Head Neck, 2014, 36(10): 1398-1407. doi:10.1002/hed.23464
[62] Zhong LZ, Dong D, Fang XL, et al. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: a multicentre study[J]. E Bio Medicine, 2021, 70: 103522. doi:10.1016/j.ebiom.2021.103522
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