山东大学耳鼻喉眼学报 ›› 2022, Vol. 36 ›› Issue (2): 113-119.doi: 10.6040/j.issn.1673-3770.0.2021.175
华红利1,李松1,陶泽璋1,2
HUA Hongli1, LI Song1,TAO Zezhang1,2
摘要: 探讨利用人工智能(AI)技术在鼻咽部病理学、影像学和内镜学等海量医学图像大数据的基础上建立学习模型,实现鼻咽癌医学图像的AI辅助诊疗决策系统,从而辅助医师对鼻咽癌的诊断更为精准,让治疗更加个性化。AI在鼻咽癌诊疗方面处于研究阶段,尚未真正在临床开展和应用。针对目前AI在鼻咽癌诊疗中的研究情况作一综述,进一步探讨其存在的问题和未来发展方向。
中图分类号:
[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. |
[1] | 周宇翔,苗北平,卢永田. 首诊鼻咽癌内镜手术的治疗进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 108-112. |
[2] | 冯成敏,敬一丹刘海,王冰. 咽喉部鳞状细胞癌细胞系[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 113-124. |
[3] | 王迪,程金章于丹. 基于机器学习的人工智能技术在耳鼻喉科临床诊疗中的应用进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 125-131. |
[4] | 黄永望,傅德慧. 嗓音医学的范畴和疾病分类[J]. 山东大学耳鼻喉眼学报, 2021, 35(3): 1-4. |
[5] | 王中卫,杨林,郭亚,孙斌,王亚利,马秀龙,任宏涛,包兴. 鼻咽癌患者miR-429、miR-200C表达与预后关系分析[J]. 山东大学耳鼻喉眼学报, 2021, 35(3): 81-86. |
[6] | 谭玉芳,易天华. 鼻咽癌放疗后突发性聋18例[J]. 山东大学耳鼻喉眼学报, 2021, 35(1): 35-39. |
[7] | 范黎,黎越,徐细明. 鼻咽癌同步放化疗前后炎性指标变化及预测价值[J]. 山东大学耳鼻喉眼学报, 2020, 34(6): 36-41. |
[8] | 杨军, 郑贵亮. 外周前庭疾病的诊断和治疗[J]. 山东大学耳鼻喉眼学报, 2020, 34(5): 1-6. |
[9] | 秦书琪,王露萍,姜彬,王艳玲. 眼缺血综合征并发新生血管性青光眼一例并文献复习[J]. 山东大学耳鼻喉眼学报, 2020, 34(4): 53-55. |
[10] | 韩继波,邹游,杨蕊,陶泽璋. Notch受体调控上皮-间质转化对鼻咽癌细胞顺铂耐药的影响[J]. 山东大学耳鼻喉眼学报, 2020, 34(4): 105-110. |
[11] | 陈海兵, 卫亚楠, 许晓泉, 陈曦. 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. |
[12] | 蒋振华,张礼俊,李莹,肖研芹,李超,石波,张桂英,胥斌,邓伟,罗刚,罗继芳,刘国旗. COVID-19防控期间疫情非高发地区综合医院耳鼻咽喉头颈外科病房诊疗实践[J]. 山东大学耳鼻喉眼学报, 2020, 34(2): 93-98. |
[13] | 朱志玲,李松管国芳. 人工智能在耳鼻咽喉头颈外科的运用及展望[J]. 山东大学耳鼻喉眼学报, 2020, 34(2): 115-120. |
[14] | 陈雪松,付伟伟,刘江涛. 中性粒细胞淋巴细胞比值和血小板淋巴细胞比值对调强放疗鼻咽癌患者的预后价值[J]. 山东大学耳鼻喉眼学报, 2020, 34(1): 50-53. |
[15] | 张剑利,陈伟雄,陈瑞开,邝德斌,庞艺施. 鼻咽癌放疗后远期吞咽功能的纤维喉镜评估[J]. 山东大学耳鼻喉眼学报, 2019, 33(6): 56-59. |
|