Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2022, Vol. 36 ›› Issue (2): 113-119.doi: 10.6040/j.issn.1673-3770.0.2021.175

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

HUA Hongli1, LI Song1,TAO Zezhang1,2   

  1. 1. Department of Otorhinolaryngology & Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei , China;
    2. Department of Otorhinolaryngology & Head and Neck Surgery Institute, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei, China
  • Published:2022-04-15

Abstract: To explore the use of artificial intelligence(AI)technology to establish a learning model based on massive medical image big data such as nasopharyngeal pathology, imaging and endoscopy to realize the AI-assisted diagnosis and treatment decision system of medical image of nasopharyngeal cancer, so as to assist doctors to diagnose nasopharyngeal cancer more accurately and make treatment more personalized.AI is still in the research stage in the diagnosis and treatment of nasopharyngeal cancer, and has not been really carried out and applied in the clinic. This paper reviews the current research on AI in the diagnosis and treatment of nasopharyngeal carcinoma, and further discusses its existing problems and future development direction.

Key words: Artificial intelligence, Nasopharyngeal carcinoma, Diagnosis and treatment

CLC Number: 

  • R739.6
[1] Goecks J, Jalili V, Heiser LM, et al. How Machine Learning Will Transform Biomedicine[J]. Cell, 2020, 181(1): 92-101. doi:10.1016/j.cell.2020.03.022.
[2] Erickson BJ, Korfiatis P, Akkus Z, et al. Machine Learning for Medical Imaging[J]. Radiographics, 2017, 37(2): 505-515. doi:10.1148/rg.2017160130.
[3] Chartrand G, Cheng PM, Vorontsov E, et al. Deep Learning: A Primer for Radiologists[J]. Radiographics, 2017, 37(7): 2113-2131. doi:10.1148/rg.2017170077.
[4] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi:10.1038/nature14539.
[5] 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.
[6] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651. doi:10.1109/TPAMI.2016.2572683.
[7] Ronneberger O, Fischer P, Brox T, et al. U-Net: convolutional networks for biomedical image segmentation[J]. Springer International Publishing, 2015, 9351: 234-241. DOI: 10.1007/978-3-319-24574-4_28
[8] Fu Y, Lei Y, Wang T, et al. A review of deep learning based methods for medical image multi-organ segmentation[J]. Phys Med, 2021, 85: 107-122. doi:10.1016/j.ejmp.2021.05.003
[9] Wu YP, Cai PQ, Tian L, et al. Hypertrophic adenoids in patients with nasopharyngeal carcinoma: appearance at magnetic resonance imaging before and after treatment[J]. Chin J Cancer, 2015, 34(3): 130-136. doi:10.1186/s40880-015-0005-y.
[10] Cengiz K, Kumral TL, Yildirim G. Diagnosis of pediatric nasopharynx carcinoma after recurrent adenoidectomy[J]. Case Rep Otolaryngol, 2013, 2013: 653963. doi:10.1155/2013/653963.
[11] Li C, Jing B, Ke L, 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.
[12] Chuang WY, Chang SH, Yu WH, et al. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning[J]. Cancers(Basel), 2020, 12(2): E507. doi:10.3390/cancers12020507.
[13] Du D, Feng H, Lv W, et al. Machine learning methods for optimal radiomics-based differentiation between recurrence and Inflammation: application to nasopharyngeal carcinoma post-therapy PET/CT images[J]. Mol Imaging Biol, 2020, 22(3): 730-738. doi:10.1007/s11307-019-01411-9.
[14] 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.
[15] Yang Q, Guo Y, Ou X, et al. Automatic t staging using weakly supervised deep learning for nasopharyngeal carcinoma on MR Images[J]. J Magn Reson Imaging, 2020, 52(4): 1074-1102. doi:10.1002/jmri.27202.
[16] Chan AT, Gregoire V, Lefebvre JL, et al. Nasopharyngeal cancer: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol,2012,23(17):vii83-vii85. doi:10.1093/annonc/mds266.
[17] Zhang L, Huang Y, Hong S, et al. Gemcitabine plus cisplatin versus fluorouracil plus cisplatin in recurrent or metastatic nasopharyngeal carcinoma: a multicentre, randomised, open-label, phase 3 trial[J]. The Lancet, 2016, 388(10054): 1883-1892. doi:10.1016/S0140-6736(16)31388-5.
[18] 曾娜. 基于MRI影像组学建立鼻咽癌早期疗效预测模型[D].衡阳:南华大学.2020. doi: 10.27234/d.cnki.gnhuu.2020.000686
[19] Liu J, Mao Y, Li Z, et al. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma[J]. J Magn Reson Imaging, 2016, 44(2): 445-455. doi:10.1002/jmri.25156.
[20] Zhang L, Ye Z, Ruan L, et al. Pretreatment MRI-Derived radiomics may evaluate the response of different induction chemotherapy regimens in locally advanced nasopharyngeal carcinoma[J]. Acad Radiol, 2020, 27(12): 1655-1664. doi:10.1016/j.acra.2020.09.002.
[21] Zhao L, Gong J, Xi Y, 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.
[22] 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.
[23] Liu K, Xia W, Qiang M, 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.
[24] Cui C, WANG S, 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.
[25] Zhang L, Wu X, 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.
[26] Zhang B, Lian Z, Zhong L, et al. Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma[J]. BMC Cancer, 2020, 20(1): 502. doi:10.1186/s12885-020-06957-4.
[27] Blanchard P, Lee A, Marguet S, et al. Chemotherapy and radiotherapy in nasopharyngeal carcinoma: an update of the MAC-NPC meta-analysis[J]. The Lancet Oncology, 2015, 16(6): 645-655. doi:10.1016/S1470-2045(15)70126-9.
[28] Wang WY, Twu CW, Chen HH, et al. Plasma EBV DNA clearance rate as a novel prognostic marker for metastatic/recurrent nasopharyngeal carcinoma[J]. Clin Cancer Res,2010,16(3):1016-1024. doi:10.1158/1078-0432.CCR-09-2796.
[29] Zhang L, Dong D, Li H, et al. Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: a retrospective cohort study[J]. EBioMedicine,2019,40:327-335. doi:10.1016/j.ebiom.2019.01.013.
[30] Zhou Z, Wang K, Folkert M, et al. Multifaceted radiomics for distant metastasis prediction in head & neck cancer[J]. Phys Med Biol,2020,65(15):155009. doi:10.1088/1361-6560/ab8956.
[31] Diamant A, Chatterjee A, Vallières M, et al. Deep learning in head & neck cancer outcome prediction[J]. Scientific Reports, 2019, 9(1): 2764. doi:10.1038/s41598-019-39206-1.
[32] Wu Q, Wang S, Zhang S, et al. Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer[J]. JAMA Netw Open, 2020, 3(7): e2011625. doi:10.1001/jamanetworkopen.2020.11625.
[33] Wu X, 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.
[34] Xia WX, Zhang HB, Shi JL, et al. A prognostic model predicts the risk of distant metastasis and death for patients with nasopharyngeal carcinoma based on pre-treatment serum C-reactive protein and N-classification[J]. Eur J Cancer, 2013, 49(9): 2152-2160. doi:10.1016/j.ejca.2013.03.003.
[35] Yang H, Bai X, Baoyin H. Rapid generation of time-optimal trajectories for asteroid landing via convex optimization[J]. Journal of Guidance, Control, and Dynamics, 2017, 40(3): 628-641. doi:10.2514/1.G002170.
[36] An X, Wang FH, Ding PR, et al. Plasma epstein-Barr virus DNA level strongly predicts survival in metastatic/recurrent nasopharyngeal carcinoma treated with palliative chemotherapy[J]. Cancer, 2011, 117(16): 3750-3757. doi:10.1002/cncr.25932.
[37] Chua MLK, Wee JTS, Hui EP, et al. Nasopharyngeal carcinoma[J]. Lancet, 2016, 387(10022): 1012-1024. doi:10.1016/S0140-6736(15)00055-0.
[38] 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.
[39] 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.
[40] Ke L, Deng Y, Xia W, et al. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images[J]. Oral Oncology,2020,110:104862. doi:10.1016/j.oraloncology.2020.104862.
[41] Liang S, 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.
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