山东大学耳鼻喉眼学报 ›› 2024, Vol. 38 ›› Issue (3): 124-129.doi: 10.6040/j.issn.1673-3770.0.2023.037

• 综述 • 上一篇    

人工智能在头颈部鳞状细胞癌淋巴结转移的病理研究进展

谢玉林,雷大鹏   

  1. 山东大学齐鲁医院 耳鼻咽喉科/国家卫生健康委员会耳鼻咽喉科学重点实验室, 山东 济南 250012
  • 发布日期:2024-06-04
  • 通讯作者: 雷大鹏. E-mail:leidapeng@sdu.edu.cn

Advances in the pathological study of artificial intelligence in the lymph node metastasis of head and neck squamous cell carcinoma

XIE Yulin, LEI Dapeng   

  1. Department of Otorhinolaryngology, Qilu Hospital of Shandong University/National Health Commission Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, Shandong, China
  • Published:2024-06-04

摘要: 人工智能(artificial intelligence, AI)在医学领域发展迅速,广泛应用于疾病的诊断及预后评价。头颈癌是全球常见恶性肿瘤之一,其中大部分为鳞状细胞癌,头颈部鳞状细胞癌(head and neck squamous cell carcinoma, HNSCC)颈部淋巴结转移是重要的预后因素,能否准确评估颈部淋巴结转移情况影响临床决策。目前许多研究已开发出预测HNSCC颈部淋巴结转移的模型,但不同模型构建时应用的临床、病理参数不同,如何更全面地分析HNSCC患者的临床、病理数据,并建立更精准预测模型是未来的发展方向。本文通过阐述AI在病理方面的研究进展以及在HNSCC中的研究现状,对于如何运用AI有效地评估HNSCC淋巴结转移、建立更精确有效的深度学习算法展开了深入的思考与展望,从而不断提升HNSCC的诊疗水平。

关键词: 人工智能, 头颈鳞癌, 淋巴结转移

Abstract: Artificial intelligence(AI)has developed rapidly in the field of medicine and is widely used in the diagnosis and prognosis evaluation of diseases. Head and neck cancer is one of the most common malignant tumors in the world, most of which are squamous cell carcinoma. Cervical lymph node metastasis of head and neck squamous cell carcinoma(HNSCC)is an important prognostic factor. The accuracy of the assessment of cervical lymph node metastasis is highly dependent on clinical diagnosis and treatment. At present, many studies have been conducted to develop models that can be used to predict cervical lymph node metastasis of HNSCC, and different clinical and pathological parameters were used in these predictive models. In order to progress development, we need to determine the optimum method to analyze the clinical and pathological data of HNSCC patients in a more comprehensive manner, as well as establish a prediction model with better precision. In this paper, we describe the progress of AI in the field of pathology and discuss the current status of its use in HNSCC research. Additionally, we have conducted an in-depth consideration and prospect on building an accurate and efficient AI prediction model for HNSCC lymph node metastasis to continuously improve the diagnosis and treatment of HNSCC.

Key words: Artificial Intelligence, Head and neck squamous cell carcinoma, Lymph node metastasis

中图分类号: 

  • R739.63
[1] 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
[2] Kong XY, Gong S, Su LJ, et al. Automatic detection of acromegaly from facial photographs using machine learning methods[J]. EBioMedicine, 2018, 27: 94-102. doi:10.1016/j.ebiom.2017.12.015
[3] Copeland BJ. The essential turing: the ideas that gave birth to the computer age[M]. Oxford: Clarendon Press, 2004
[4] Cheng N, Ren Y, Zhou J, et al. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images[J]. Gastroenterology, 2022, 162(7): 1948-1961.e7. doi:10.1053/j.gastro.2022.02.025
[5] Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology[J]. Nat Rev Clin Oncol, 2019, 16(11): 703-715. doi:10.1038/s41571-019-0252-y
[6] Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases[J]. J Pathol Inform, 2016, 7: 29. doi:10.4103/2153-3539.186902
[7] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. doi:10.3322/caac.21660
[8] Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study[J]. JAMA Oncol, 2019, 5(12): 1749-1768. doi:10.1001/jamaoncol.2019.2996
[9] Das DK, Bose S, Maiti AK, et al. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis[J]. Tissue Cell, 2018, 53: 111-119. doi:10.1016/j.tice.2018.06.004
[10] Lu C, Lewis JS Jr, Dupont WD, et al. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival[J]. Mod Pathol, 2017, 30(12): 1655-1665. doi:10.1038/modpathol.2017.98
[11] Mamelle G, Pampurik J, Luboinski B, et al. Lymph node prognostic factors in head and neck squamous cell carcinomas[J]. Am J Surg, 1994, 168(5): 494-498. doi:10.1016/S0002-9610(05)80109-6
[12] Roberts TJ, Colevas AD, Hara W, et al. Number of positive nodes is superior to the lymph node ratio and American Joint Committee on Cancer N staging for the prognosis of surgically treated head and neck squamous cell carcinomas[J]. Cancer, 2016, 122(9): 1388-1397. doi:10.1002/cncr.29932
[13] Lydiatt WM, Patel SG, O'Sullivan B, et al. Head and Neck cancers-major changes in the American Joint Committee on cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(2): 122-137. doi:10.3322/caac.21389
[14] Hamet P, Tremblay J. Artificial intelligence in medicine[J]. Metabolism, 2017, 69S: S36-S40. doi:10.1016/j.metabol.2017.01.011
[15] Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images[J]. Nat Med, 2019, 25(8): 1301-1309. doi:10.1038/s41591-019-0508-1
[16] McAlpine ED, Michelow P, Celik T. The utility of unsupervised machine learning in anatomic pathology[J]. Am J Clin Pathol, 2022, 157(1): 5-14. doi:10.1093/ajcp/aqab085
[17] Roohi A, Faust K, Djuric U, et al. Unsupervised machine learning in pathology: the next frontier[J]. Surg Pathol Clin, 2020, 13(2): 349-358. doi:10.1016/j.path.2020.01.002
[18] 姜凡, 陈庆, 赵强, 等. 高频超声与喉镜、CT在喉癌中的应用探讨[J]. 临床医学影像杂志, 1996, 7(4): 239-240 JIANG Fan, CHEN Qing, ZHAO Qiang, et al. Application of high frequency ultrasound, laryngoscope and CT in laryngeal cancer[J]. Journal of China Clinic Medical Imaging, 1996, 7(4): 239-240
[19] 刘彬, 宫凤玲, 孙川, 等. CT平扫与对比增强扫描对下咽癌的诊断价值及对下咽癌侵犯周围结构的影像表现分析[J]. 实用癌症杂志, 2018, 33(3): 398-400. doi:10.3969/j.issn.1001-5930.2018.03.016 LIU Bin, GONG Fengling, SUN Chuan, et al. Analysis of the application value of plain CT scan and contrast-enhanced scan in the diagnosis of hypopharyngeal carcinoma and clinical manifestation of its adjacent structure[J]. The Practical Journal of Cancer, 2018, 33(3): 398-400. doi:10.3969/j.issn.1001-5930.2018.03.016
[20] 陈东彦, 钱晔, 魏东敏, 等. 高频超声诊断下咽鳞癌淋巴结转移的临床研究[J]. 山东大学耳鼻喉眼学报, 2022, 36(5): 18-23. doi:10.6040/j.issn.1673-3770.0.2022.180 CHEN Dongyan, QIAN Ye, WEI Dongmin, et al. Clinical value of high-frequency ultrasound in the diagnosis of lymph node metastasis in hypopharyngeal squamous cell carcinoma[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2022, 36(5): 18-23. doi:10.6040/j.issn.1673-3770.0.2022.180
[21] Wang F, Zhang B, Wu XJ, et al. Radiomic nomogram improves preoperative T category accuracy in locally advanced laryngeal carcinoma[J]. Front Oncol, 2019, 9: 1064. doi:10.3389/fonc.2019.01064
[22] Ren JL, Tian J, Yuan Y, et al. Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and Ⅲ-Ⅳ head and neck squamous cell carcinoma[J]. Eur J Radiol, 2018, 106: 1-6. doi:10.1016/j.ejrad.2018.07.002
[23] Dasgupta A, Fatima K, DiCenzo D, et al. Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy[J]. Cancer Med, 2021, 10(8): 2579-2589. doi:10.1002/cam4.3634
[24] Folmsbee J, Liu X, Brandwein-Weber M. Active deep learning: improved training efficiency of convolutional neural networks for tissue classification in oral cavity cancer[J]. 2018 IEEE 15th International Symposium on Biomedical Imaging(ISBI 2018), 2018: 770-773. doi: 10.1109/ISBI.2018.8363686
[25] Shaban M, Khurram SA, Fraz MM, et al. A novel digital score for abundance of tumour infiltrating lymphocytes predicts disease free survival in oral squamous cell carcinoma[J]. Sci Rep, 2019, 9(1): 13341. doi:10.1038/s41598-019-49710-z
[26] 丁妍, 韩梦雪, 刘月平. AI辅助预估乳腺癌淋巴结转移的研究现状及前景[J]. 四川大学学报(医学版), 2021, 52(2): 162-165. doi:10.12182/20210360102 DING Yan, HAN Mengxue, LIU Yueping. AI-assisted prediction of lymph node metastasis of breast cancer: current and prospective research[J]. Journal of Sichuan University(Medical Sciences), 2021, 52(2): 162-165. doi:10.12182/20210360102
[27] Bejnordi BE, Veta M, van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318(22): 2199-2210. doi:10.1001/jama.2017.14585
[28] Bandi P, Geessink O, Manson Q, et al. From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge[J]. IEEE Trans Med Imaging, 2019, 38(2): 550-560. doi:10.1109/TMI.2018.2867350
[29] Johnson DE, Burtness B, Leemans CR, et al. Head and neck squamous cell carcinoma[J]. Nat Rev Dis Primers, 2020, 6(1): 92. doi:10.1038/s41572-020-00224-3
[30] Chuang WY, Chen CC, Yu WH, et al. Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images[J]. Mod Pathol, 2021, 34(10): 1901-1911. doi:10.1038/s41379-021-00838-2
[31] Wang XD, Chen Y, Gao YS, et al. Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning[J]. Nat Commun, 2021, 12(1): 1637. doi:10.1038/s41467-021-21674-7
[32] Braakhuis BJM, Brakenhoff RH, René Leemans C. Treatment choice for locally advanced head and neck cancers on the basis of risk factors: biological risk factors[J]. Ann Oncol, 2012, 23: x173-x177. doi:10.1093/annonc/mds299
[33] Tang HS, Li G, Liu C, et al. Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning[J]. Laryngoscope Investig Otolaryngol, 2022, 7(1): 161-169. doi:10.1002/lio2.742
[34] Kapil A, Meier A, Zuraw A, et al. Deep semi supervised generative learning for automated tumor proportion scoring on NSCLC tissue needle biopsies[J]. Sci Rep, 2018, 8(1): 17343. doi:10.1038/s41598-018-35501-5
[35] Qu H, Wu P, Huang Q, et al. Weakly supervised deep nuclei segmentation using partial points annotation in histopathology images[J]. IEEE Trans Med Imaging, 2020, 39(11): 3655-3666. doi: 10.1109/TMI.2020.3002244
[36] Chang HY, Jung CK, Woo JI, et al. Artificial intelligence in pathology[J]. J Pathol Transl Med, 2019, 53(1): 1-12. doi:10.4132/jptm.2018.12.16
[37] Mahmood H, Shaban M, Rajpoot N, et al. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview[J]. Br J Cancer, 2021, 124(12): 1934-1940. doi:10.1038/s41416-021-01386-x
[38] Go H. Digital pathology and artificial intelligence applications in pathology[J]. Brain Tumor Res Treat, 2022, 10(2): 76-82. doi:10.14791/btrt.2021.0032
[39] Sultan AS, Elgharib MA, Tavares T, et al. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology[J]. J Oral Pathol Med, 2020, 49(9): 849-856. doi:10.1111/jop.13042
[40] Levy J, Haudenschild C, Barwick C, et al. Topological feature extraction and visualization of whole slide images using graph neural networks[J]. Pac Symp Biocomput, 2021, 26: 285-296. doi:10.1101/2020.07.03.187237
[41] Gupta R, Srivastava D, Sahu M, et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery[J]. Mol Divers, 2021, 25(3): 1315-1360. doi:10.1007/s11030-021-10217-3
[42] Schmid P, Adams S, Rugo HS, et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer[J]. N Engl J Med, 2018, 379(22): 2108-2121. doi:10.1056/NEJMoa1809615
[43] Rosenberg JE, Hoffman-Censits J, Powles T, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial[J]. Lancet, 2016, 387(10031): 1909-1920. doi:10.1016/S0140-6736(16)00561-4
[44] Büttner R, Gosney JR, Skov BG, et al. Programmed death-ligand 1 immunohistochemistry testing: a review of analytical assays and clinical implementation in non-small-cell lung cancer[J]. J Clin Oncol, 2017, 35(34): 3867-3876. doi:10.1200/JCO.2017.74.7642
[45] Serag A, Ion-Margineanu A, Qureshi H, et al. Translational AI and deep learning in diagnostic pathology[J]. Front Med(Lausanne), 2019, 6: 185. doi:10.3389/fmed.2019.00185
[46] 沈晓涵, 杜祥. 人工智能在病理诊断领域中的应用[J]. 肿瘤防治研究, 2020, 47(7): 487-491. doi:10.3971/j.issn.1000-8578.2020.19.1131 SHEN Xiaohan, DU Xiang. Application of artificial intelligence in pathological diagnosis[J]. Cancer Research on Prevention and Treatment, 2020, 47(7): 487-491. doi:10.3971/j.issn.1000-8578.2020.19.1131
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