REFERENCES
1. Nasopharyngeal cancer statistics. Available from: https://www.wcrf.org/cancer-trends/nasopharyngeal-cancer-statistics [Last accessed on 17 Mar 2023].
2. Centre for Health Protection. department of health - nasopharyngeal cancer. 2022. Available from: https://www.chp.gov.hk/en/healthtopics/content/25/54.html [Last accessed on 20 Mar 2023].
3. Pan JJ, Ng WT, Zong JF, et al. Proposal for the 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy. Cancer 2016;122:546-58.
4. Lee AW, Sze WM, Au JS, et al. Treatment results for nasopharyngeal carcinoma in the modern era: the Hong Kong experience. Int J Radiat Oncol Biol Phys 2005;61:1107-16.
5. Ho FC, Tham IW, Earnest A, Lee KM, Lu JJ. Patterns of regional lymph node metastasis of nasopharyngeal carcinoma: a meta-analysis of clinical evidence. BMC Cancer 2012;12:98.
6. Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A comprehensive review on radiomics and deep learning for nasopharyngeal carcinoma imaging. Diagnostics 2021;11:1523.
7. Ng WT, But B, Choi HCW, et al. Application of artificial intelligence for nasopharyngeal carcinoma management - a systematic review. Cancer Manag Res 2022;14:339-66.
8. 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. Radiother Oncol 2020;151:1-9.
9. Xia C, Chen Y, Vardhanabhuti V. Primary tumour and lymph node radiomics assessment in PET-CT in non-metastatic nasopharyngeal carcinoma patients. In European Congress of Radiology (ECR). Germany: European Society of Radiology/SpringerOpen; 2020. Available from: http://www.springer.com/medicine/radiology/journal/13244 [Last accessed on 20 Mar 2023].
10. Mccarthy J, Hayes P. Some philosophical problems from the standpoint of artificial intelligence. In: Webber BL, Nilsson NJ, editors. Readings in artificial intelligence. Burlington, USA: Morgan Kaufmann; 1981, pp. 431-50.
11. Luger GF. Artificial intelligence: structures and strategies for complex problem solving, 6th ed. Pearson Addison-Wesley; 2009. Available from: http://www.uoitc.edu.iq/images/documents/informatics-institute/exam_materials/artificial%20intelligence%20structures%20and%20strategies%20for%20%20complex%20problem%20solving.pdf [Last accessed on 17 Mar 2023].
12. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics 2017;37:505-15.
13. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: a review. J Med Syst 2018;42:226.
14. Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-37.
15. Wagner MW, Namdar K, Biswas A, Monah S, Khalvati F, Ertl-Wagner BB. Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know. Neuroradiology 2021;63:1957-67.
16. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-6.
17. Bogowicz M, Tanadini-Lang S, Guckenberger M, Riesterer O. Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer. Sci Rep 2019;9:15198.
18. Lee CK, Jeong SH, Jang C, et al. Tumor metastasis to lymph nodes requires YAP-dependent metabolic adaptation. Science 2019;363:644-9.
19. Li S, Wang K, Hou Z, et al. Use of radiomics combined with machine learning method in the recurrence patterns after intensity-modulated radiotherapy for nasopharyngeal carcinoma: a preliminary study. Front Oncol 2018;8:648.
20. Yu TT, Lam SK, To LH, et al. Pretreatment prediction of adaptive radiation therapy eligibility using MRI-based radiomics for advanced nasopharyngeal carcinoma patients. Front Oncol 2019;9:1050.
21. Kang L, Niu Y, Huang R, et al. Predictive value of a combined model based on pre-treatment and mid-treatment mri-radiomics for disease progression or death in locally advanced nasopharyngeal carcinoma. Front Oncol 2021;11:774455.
22. Lam SK, Zhang Y, Zhang J, et al. Multi-organ omics-based prediction for adaptive radiation therapy eligibility in nasopharyngeal carcinoma patients undergoing concurrent chemoradiotherapy. Front Oncol 2021;11:792024.
23. Lam SK, Zhang J, Zhang YP, et al. A multi-center study of CT-based neck nodal radiomics for predicting an adaptive radiotherapy trigger of ill-fitted thermoplastic masks in patients with nasopharyngeal carcinoma. Life 2022;12:241.
24. Registry, HKC. Report of Stage-specific surival of nasopharyngeal cancer in Hong Kong 2019. Available from: https://www3.ha.org.hk/cancereg/pdf/survival/Stage-specific%20Survival%20of%20NPC%20in%20HK.pdf [Last accessed on 17 Mar 2023].
25. Xu H, Liu J, Huang Y, Zhou P, Ren J. MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma. Br J Radiol 2021;94:20201212.
26. Wang Y, Li C, Yin G, et al. Extraction parameter optimized radiomics for neoadjuvant chemotherapy response prognosis in advanced nasopharyngeal carcinoma. Clin Transl Radiat Oncol 2022;33:37-44.
27. Yang K, Tian J, Zhang B, et al. A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma. Oral Oncol 2019;98:85-91.
28. Ming X, Oei RW, Zhai R, et al. MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma. Sci Rep 2019;9:10412.
29. Bologna M, Corino V, Calareso G, et al. Baseline MRI-radiomics can predict overall survival in non-endemic ebv-related nasopharyngeal carcinoma patients. Cancers 2020;12:2958.
30. 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. Ther Adv Med Oncol 2020;12:1758835920971416.
31. Chen X, Li Y, Li X, et al. An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features. Oral Oncol 2021;118:105335.
32. Qiang M, Li C, Sun Y, et al. A prognostic predictive system based on deep learning for locoregionally advanced nasopharyngeal carcinoma. J Natl Cancer Inst 2021;113:606-15.
33. Zhang L, Wu X, Liu J, et al. MRI-based deep-learning model for distant metastasis-free survival in locoregionally advanced nasopharyngeal carcinoma. J Magn Reson Imaging 2021;53:167-78.
34. Aussem A, de Morais SR, Corbex M. Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks. Artif Intell Med 2012;54:53-62.
35. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA. 2022. Available from: https://dl.acm.org/doi/10.5555/3295222.3295230#sec-cit [Last accessed on 17 Mar 2023].
36. Du R, Lee VH, Yuan H, et al. Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: a multicenter study. Radiol Artif Intell 2019;1:e180075.
37. Chao KS, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys 2007;68:1512-21.
38. Stapleford LJ, Lawson JD, Perkins C, et al. Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010;77:959-66.
39. Zhou JY, Fang W, Chan KL, Chong VF, Khoo JB. Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches. J Digit Imaging 2007;20:336-46.
40. Yang J, Beadle BM, Garden AS, et al. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Pract Radiat Oncol 2014;4:e31-7.
41. Cardenas CE, Beadle BM, Garden AS, et al. Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based approach. Int J Radiat Oncol Biol Phys 2021;109:801-12.
42. Men K, Chen X, Zhang Y, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front Oncol 2017;7:315.
43. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118-27.
44. Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol 2020;65:24TR02.
45. Boellaard R, Delgado-Bolton R, Oyen WJ, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015;42:328-54.
46. Kaalep A, Sera T, Rijnsdorp S, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging 2018;45:1344-61.
47. Schick U, Lucia F, Dissaux G, et al. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019;92:20190105.
48. Fujita S, Hagiwara A, Yasaka K, et al. Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features. Eur Radiol 2022;32:4791-800.
49. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.
50. Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 2018;288:407-15.
51. Mahon RN, Ghita M, Hugo GD, Weiss E. ComBat harmonization for radiomic features in independent phantom and lung cancer patient computed tomography datasets. Phys Med Biol 2020;65:015010.
52. Horng H, Singh A, Yousefi B, et al. Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects. Sci Rep 2022;12:4493.
