病理组学在头颈部肿瘤中的研究进展

摘要/Abstract

摘要：

病理组学将数字化病理学和人工智能相融合，通过提取、筛选和分析病理图片中蕴含的数据特征，对肿瘤的诊断、治疗和预后进行评估。近年来，越来越多的病理组学研究在头颈部肿瘤的计算机辅助诊断、肿瘤微环境和生物标志物识别以及预后评估等方面显示出巨大的价值，未来有望为辅助临床决策、实现头颈部肿瘤的精准治疗发挥重要的作用。

关键词:头颈部肿瘤,诊断,预后,病理组学

Abstract:

Pathomics， the integration of digital pathology and artificial intelligence， is used to assess tumor diagnosis， treatment and prognosis by extracting， screening and analyzing the data features contained in pathological pictures. In recent years， more and more pathomics studies of head and neck neoplasms have shown great value in the areas of computer-assisted diagnosis， tumor microenvironment and biomarker identification as well as prognosis evaluation. It is expected to play an important role in clinical assistance and precise treatment of head and neck neoplasms in the future.

Key words:Head and neck neoplasms,Diagnosis,Prognosis,Pathomics

鞠逸凡, 徐晨阳, 雷大鹏. 病理组学在头颈部肿瘤中的研究进展[J]. 国际肿瘤学杂志, 2023, 50(5): 294-298.

Ju Yifan, Xu Chenyang, Lei Dapeng. Research progress of pathomics in head and neck neoplasms[J]. Journal of International Oncology, 2023, 50(5): 294-298.

参考文献37

[1]	Pisani P, Airoldi M, Allais A, et al. Metastatic disease in head & neck oncology[J]. Acta Otorhinolaryngol Ital, 2020, 40(SUPPL. 1): S1-S86. DOI: 10.14639/0392-100X-suppl.1-40-2020. doi:10.14639/0392-100X-suppl.1-40-2020
[2]	Meccariello G, Maniaci A, Bianchi G, et al. Neck dissection and trans oral robotic surgery for oropharyngeal squamous cell carcinoma[J]. Auris Nasus Larynx, 2022, 49(1): 117-125. DOI: 10.1016/j.anl.2021.05.007. doi:10.1016/j.anl.2021.05.007
[3]	Chi AC, Katabi N, Chen HS, et al. Interobserver variation among pathologists in evaluating perineural invasion for oral squamous cell carcinoma[J]. Head Neck Pathol, 2016, 10(4): 451-464. DOI: 10.1007/s12105-016-0722-9. doi:10.1007/s12105-016-0722-9pmid:27140176
[4]	Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens[J]. JAMA, 2015, 313(11): 1122-1132. DOI: 10.1001/jama.2015.1405. doi:10.1001/jama.2015.1405pmid:25781441
[5]	Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence[J]. Lancet Oncol, 2019, 20(5): e253-e261. DOI: 10.1016/S1470-2045(19)30154-8. doi:10.1016/S1470-2045(19)30154-8
[6]	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. doi:10.1038/s41571-019-0252-y
[7]	Prewitt JM, Mendelsohn ML. The analysis of cell images[J]. Ann N Y Acad Sci, 1966, 128(3): 1035-1053. DOI: 10.1111/j.1749-6632.1965.tb11715.x. doi:10.1111/j.1749-6632.1965.tb11715.x
[8]	Wright AM, Smith D, Dhurandhar B, et al. Digital slide imaging in cervicovaginal cytology: a pilot study[J]. Arch Pathol Lab Med, 2013, 137(5): 618-624. DOI: 10.5858/arpa.2012-0430-OA. doi:10.5858/arpa.2012-0430-OApmid:22970841
[9]	Wang S, Yang DM, Rong R, et al. Pathology image analysis using segmentation deep learning algorithms[J]. Am J Pathol, 2019, 189(9): 1686-1698. DOI: 10.1016/j.ajpath.2019.05.007. doi:S0002-9440(18)31121-0pmid:31199919
[10]	Jacques SL. Optical properties of biological tissues: a review[J]. Phys Med Biol, 2013, 58(11): R37-R61. DOI: 10.1088/0031-9155/58/11/R37. doi:10.1088/0031-9155/58/11/R37
[11]	Lu G, Fei B. Medical hyperspectral imaging: a review[J]. J Biomed Opt, 2014, 19(1): 10901. DOI: 10.1117/1.JBO.19.1.010901. doi:10.1117/1.JBO.19.1.010901pmid:24441941
[12]	Manolakis D, Shaw G. Detection algorithms for hyperspectral ima-ging applications[J]. IEEE Signal Process Mag, 2002, 19(1): 29-43. DOI: 10.1109/79.974724. doi:10.1109/79.974724
[13]	Lu S, Stein JE, Rimm DL, et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis[J]. JAMA Oncol, 2019, 5(8): 1195-1204. DOI: 10.1001/jamaoncol.2019.1549. doi:10.1001/jamaoncol.2019.1549
[14]	Bodenmiller B. Multiplexed epitope-based tissue imaging for disco-very and healthcare applications[J]. Cell Syst, 2016, 2(4): 225-238. DOI: 10.1016/j.cels.2016.03.008. doi:10.1016/j.cels.2016.03.008pmid:27135535
[15]	Mansoor I, Zalles C, Zahid F, et al. Fine-needle aspiration of folli-cular adenoma versus parathyroid adenoma: the utility of multis-pectral imaging in differentiating lesions with subtle cyto-morphologic differences[J]. Cancer, 2008, 114(1): 22-26. DOI: 10.1002/cncr.23252. doi:10.1002/cncr.23252
[16]	Halicek M, Little JV, Wang X, et al. Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks[J]. J Biomed Opt, 2019, 24(3): 1-9. DOI: 10.1117/1.JBO.24.3.036007. doi:10.1117/1.JBO.24.3.036007pmid:30891966
[17]	Baik J, Ye Q, Zhang L, et al. Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms[J]. Cell Oncol (Dordr), 2014, 37(3): 193-202. DOI: 10.1007/s13402-014-0172-x. doi:10.1007/s13402-014-0172-x
[18]	Wang Y, Guan Q, Lao I, et al. Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study[J]. Ann Transl Med, 2019, 7(18): 468. DOI: 10.21037/atm.2019.08.54. doi:10.21037/atm.2019.08.54pmid:31700904
[19]	Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using Convolutional Neural Networks[J]. PLoS One, 2017, 12(6): e0177544. DOI: 10.1371/journal.pone.0177544. doi:10.1371/journal.pone.0177544
[20]	Han Z, Wei B, Zheng Y, et al. Breast cancer multi-classification from histopathological images with structured deep learning model[J]. Sci Rep, 2017, 7(1): 4172. DOI: 10.1038/s41598-017-04075-z. doi:10.1038/s41598-017-04075-zpmid:28646155
[21]	Ringash J. Survivorship and quality of life in head and neck cancer[J]. J Clin Oncol, 2015, 33(29): 3322-3327. DOI: 10.1200/JCO.2015.61.4115. doi:10.1200/JCO.2015.61.4115pmid:26351336
[22]	Lu G, Little JV, Wang X, et al. Detection of head and neck cancer in surgical specimens using quantitative hyperspectral imaging[J]. Clin Cancer Res, 2017, 23(18): 5426-5436. DOI: 10.1158/1078-0432.CCR-17-0906. doi:10.1158/1078-0432.CCR-17-0906pmid:28611203
[23]	Halicek M, Dormer JD, Little JV, et al. Hyperspectral imaging of head and neck squamous cell carcinoma for cancer margin detection in surgical specimens from 102 patients using deep learning[J]. Cancers (Basel), 2019, 11(9): 1367. DOI: 10.3390/cancers11091367. doi:10.3390/cancers11091367
[24]	Labani-Motlagh A, Ashja-Mahdavi M, Loskog A. The tumor micro-environment: a milieu hindering and obstructing antitumor immune responses[J]. Front Immunol, 2020, 11: 940. DOI: 10.3389/fimmu.2020.00940. doi:10.3389/fimmu.2020.00940pmid:32499786
[25]	Tong CC, Kao J, Sikora AG. Recognizing and reversing the im-munosuppressive tumor microenvironment of head and neck cancer[J]. Immunol Res, 2012, 54(1/3): 266-274. DOI: 10.1007/s12026-012-8306-6. doi:10.1007/s12026-012-8306-6
[26]	Wondergem NE, Nauta IH, Muijlwijk T, et al. The immune micro-environment in head and neck squamous cell carcinoma: on subsets and subsites[J]. Curr Oncol Rep, 2020, 22(8): 81. DOI: 10.1007/s11912-020-00938-3. doi:10.1007/s11912-020-00938-3pmid:32602047
[27]	Peled M, Onn A, Herbst RS. Tumor-infiltrating lymphocytes-location for prognostic evaluation[J]. Clin Cancer Res, 2019, 25(5): 1449-1451. DOI: 10.1158/1078-0432.CCR-18-3803. doi:10.1158/1078-0432.CCR-18-3803pmid:30567833
[28]	Hartman DJ, Ahmad F, Ferris RL, et al. Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma[J]. Oral Oncol, 2018, 86: 278-287. DOI: 10.1016/j.oraloncology.2018.10.005. doi:S1368-8375(18)30358-0pmid:30409313
[29]	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. doi:10.1038/s41598-019-49710-zpmid:31527658
[30]	Shaban M, Raza SEA, Hassan M, et al. A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma[J]. J Pathol, 2022, 256(2): 174-185. DOI: 10.1002/path.5819. doi:10.1002/path.5819
[31]	Fraz MM, Khurram SA, Graham S, et al. FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer[J]. Neural Comput Appl, 2020, 32(14): 9915-9928. DOI: 10.1007/s00521-019-04516-y. doi:10.1007/s00521-019-04516-y
[32]	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. doi:S0040-8166(18)30113-7pmid:30060821
[33]	Cyll K, Ersvær E, Vlatkovic L, et al. Tumour heterogeneity poses a significant challenge to cancer biomarker research[J]. Br J Cancer, 2017, 117(3): 367-375. DOI: 10.1038/bjc.2017.171. doi:10.1038/bjc.2017.171
[34]	de Ruiter EJ, de Roest RH, Brakenhoff RH, et al. Digital pathology-aided assessment of tumor-infiltrating T lymphocytes in advanced stage, HPV-negative head and neck tumors[J]. Cancer Immunol Immunother, 2020, 69(4): 581-591. DOI: 10.1007/s00262-020-02481-3. doi:10.1007/s00262-020-02481-3pmid:31980916
[35]	Humphries MP, Craig SG, Kacprzyk R, et al. The adaptive immune and immune checkpoint landscape of neoadjuvant treated esophageal adenocarcinoma using digital pathology quantitation[J]. BMC Cancer, 2020, 20(1): 500. DOI: 10.1186/s12885-020-06987-y. doi:10.1186/s12885-020-06987-ypmid:32487090
[36]	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. doi:10.4103/2153-3539.186902pmid:27563488
[37]	Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J]. Nat Mach Intell, 2019, 1(5): 206-215. DOI: 10.1038/s42256-019-0048-x. doi:10.1038/s42256-019-0048-xpmid:35603010