|Table of Contents|

Research progress of artificial intelligence in diagnosis and prognosis of nasopharyngeal carcinoma

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 09
Page:
1765-1768
Research Field:
Publishing date:

Info

Title:
Research progress of artificial intelligence in diagnosis and prognosis of nasopharyngeal carcinoma
Author(s):
DONG GuohuiGAO BoPAN Yun
Basic Medical College of Dali University,Yunnan Dali 671000,China.
Keywords:
nasopharyngeal carcinomaartificial intelligenceimaging diagnosispathologyendoscopeprognosis
PACS:
R739.6
DOI:
10.3969/j.issn.1672-4992.2023.09.035
Abstract:
The application of artificial intelligence(AI) can help solving many problems in the diagnosis of nasopharyngeal carcinoma(NPC),including clinical diagnosis,imaging diagnosis,pathological diagnosis,endoscopic screening and so on,also,the prognosis of patients with nasopharyngeal cancer can be predicted by AI.At present,AI is being applied in nasopharyngeal cancer increasingly,but it still faces many challenges in practical application.It is believed that with the increasing application of AI in nasopharyngeal cancer and the continuous improvement of the algorithm,AI will be used as a common tool in clinical application in the future.

References:

[1]PETERSSON F.Nasopharyngeal carcinoma:a review Semin[J].Diagn Pathol,2015,32(1):54-73.
[2]BADOUAL C.Update from the 5th edition of the World Health Organization classification of head and neck tumors:Oropharynx and nasopharynx[J].Head Neck Pathol,2022,16(1):19-30.
[3]LI N,LI P.Effects of different chemoradiotherapy regimens on early survival outcomes in patients with locally advanced nasopharyngeal carcinoma[J].Journal of Sichuan University(Medical Science),2020,51(5):702-707.
[4] HOLMES JH,SACCHI L,BELLAZ ZI,et al.Artificial Intelligence in medicine AIME 2015[J].Artif Intell Med,2017,81:1-2.
[5] TOPOL E.High-performance medicine:the convergence of human and artificial intelligence[J].Nat Med,2019,25(1):44-56.
[6] ERICKSON BJ,KORFIATIS P,AKKUS Z,et al.Machine learning for medical imaging[J].Radiographics,2017,37(2):505-515.
[7] TOMITA H,YAMSHIRO T,IIDA G,et al.Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma[J].Nagoya J Med Sci,2021,83(1):135-149.
[8] SONG L,LI Y,DONG G,et al.Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy[J].Quant Imaging Med Surg,2021,11(12):4709-4720.
[9] DU D,FENG H,LYU 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.
[10] WU B,KHONG PL,CHAN T,et al.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.
[11] 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.
[12] WU B,KHONG PL,CHAN T,et al.Preliminary study of 11C-choline PET/CT for T staging of locally advanced nasopharyngeal carcinoma:comparison with 18F-FDG PET/CT[J].J Nucl Med 2011,52(3):341-346.
[13] ZHAO W,ZHANG D,MAO X,et al.Application of artificial intelligence in radiotherapy of nasopharyngeal carcinoma with magnetic resonance imaging[J].J Healthc Eng,2022,2022:4132989.
[14] 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.
[15] MA Z,ZHOU S,WU X,et al.Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning[J].Phys Med Biol,2019,64(2):025005.
[16] WONG LM,AI QYH,MO FKF,et al.Convolutional neural network in nasopharyngeal carcinoma:how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI[J].Jpn J Radiol,2021,39(6):571-579.
[17] LEE N,HARRIS J,GARDEN AS,et al.Intensity-modulated radiation therapy with or without chemotherapy for nasopharyngeal carcinoma:radiation therapy oncology group phase Ⅱ trial 0225[J].J Clin Oncol,2009,27(22):3684-3690.
[18] QIANG M,LI C,SUN Y,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.
[19] 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.
[20] 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.
[21] 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,2020,2020:8068913.
[22] CHEN X,LI Y,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(2):105335.
[23] JING B,DENG Y,ZHANG T,et al.Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRI[J].sComput Methods Programs Biomed,2020,197:105684.
[24] ZHONG LZ,FANG XL,DONG D,et al.A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0[J].Radiother Oncol,2020,151:1-9.
[25] 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].EBio Medicine,2021,70:103522.
[26] 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.
[27] 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):507.
[28] ZHANG F,ZHONG LZ,ZHAO X,et al.A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma:a multi-cohort study[J].Ther Adv Med Oncol,2020,12:431380840.
[29] DIAO S,HOU J,YU H,et al.Computer-aided pathologic diagnosis of nasopharyngeal carcinoma based on deep learning[J].Am J Pathol,2020,190(8):1691-1700.
[30] LIU K,XIA W,QIANG M,et al.Deep learning pathological microscopic features in endemic nasopharyngeal cancer:Prognostic value and potential role for individual induction chemotherapy[J].Cancer Med,2020,9(4):1298-1306.
[31] WAN XB,ZHAO Y,FAN XJ,et al.Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach[J].PLoS One,2012,7(3):e31989.
[32] 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.
[33] SHU C,YAN H,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.
[34] XU J,WANG J,BIAN X,et al.Deep learning for nasopharyngeal carcinoma identification using both white light and narrow-band imaging endoscopy[J].Laryngoscope,2022,132(5):999-1007.

Memo

Memo:
National Natural Science Foundation of China(No.81960042,82160044,82160582);国家自然科学基金(编号:81960042,82160044,82160582);大理大学肿瘤分子病理学科技创新团队项目(编号:理大校发[2019]13号)
Last Update: 2023-03-30