口咽鱗狀細胞癌病患有著不佳的預後。本論文研究治療前葡萄糖正子影像對於口咽鱗狀細胞癌的應用。收案病患為無遠端轉移之口咽鱗狀細胞癌。每位病患皆完成同步化學與放射治療。依照正子影像造影日期將病患分組，一組病患正子影像用於訓練預後預測模型，另一組用於驗證模型，兩組之比例為1:1.5。使用接收者操作特徵曲線(receiver operating characteristic curves, ROC curves)與單變量Cox回歸分析，得到與無惡化存活時間顯著相關之正子影像參數。使用這些參數，利用遞迴分割分析(recursive partitioning analysis, RPA)方法，建立病患預後模式。這一個模型接著進入多變量Cox回歸分析，與其他臨床參數比較。本論文發現正子影像參數: 標準攝取值熵值(SUV-entry)和型狀不規則度(asphericity)與無惡化存活時間顯著相關，而且同時具有高標準攝取值熵值和型狀不規則度的病患其預後最差。此一結果可以在驗證組病患可以重現。在全部病患中，此模型之一致性統計量(concordance index)可達0.75，顯著高過其它臨床參數。
更進一步的，我們比較正子影像研究結果與免疫化學組織染色和臨床常用之危險分期系統。洪氏(Ang's)分期是口咽癌常用之臨床系統，包含因子為p16結果，吸菸與否，和口咽癌癌症分期。我們收集第三與第四期口咽癌病患，每位病患皆完成同步化學與放射治療。使用Kaplan-Meier存活曲線與Cox比例風險模式分析無疾病存活率與整體存活率。使用自助法以減少變異。最後採用遞迴分割分析建立預後預測模型。總共收集了113位口咽癌病患，其中28位病患是p16陽性的。多變數Cox比例風險模式分析結果顯示: 洪氏分期高危險群；高EGFR免疫化學組織染色；與高正子掃描標準攝取值熵值(SUV-entry)和型狀不規則度(asphericity)為無疾病存活率與整體存活率預測之獨立因子。這些危險因子使用遞迴分割分析整合成一個模型，可以成功預測p16陽性與陰性之口咽癌預後。在洪氏分期高危險群中(共77位患者)也可以再細分預後。此一新建模型可以更佳分類口咽癌病患預後。
綜合以上，正子影像參數模型可以用於高危險性口咽癌病患預後預測使用。

Oropharyngeal squamous cell carcinoma (OPSCC) has unfavorable survival outcomes. We investigate the prognostic role of pre-treatment positron emission tomography (PET) in OPSCC. OPSCC patients without distant metastasis were enrolled. All of them completed primary chemoradiotherapy. Patients were classified into training and validation cohorts with a ratio of 1:1.5 according to the PET date. Tumors were segmented. PET heterogeneity and irregularity indices were obtained. PET parameters with significant impact on progression-free survival (PFS) in receiver operating characteristic curves and univariate Cox models were identified and included in recursive partitioning analysis (RPA) for constructing a prognostic model. The RPA-based prognostic model was further tested in the validation cohort using multivariate Cox models. Fifty-eight and 89 patients were in the training and validation groups, respectively. Heterogeneity parameter, SUV-entropy (derived from histogram analysis), and irregularity index, asphericity, were significantly associated with PFS. The RPA model revealed that patients with both high SUV-entropy and high asphericity experienced the worst PFS. Results were confirmed in the validation group. The overall concordance index for PFS of the model was 0.75, which was higher than the clinical stages, performance status, SUVmax, and metabolic tumor volume of PET.
These results were compared with immunohistochemistry (IHC) studies of OPSCC and the clinically used risk stratification system. The Ang's risk profile (based on p16, smoking and cancer stage) is a well-known prognostic factor in OPSCC. Patients with stage III-IV OPSCC who completed primary therapy were eligible. Disease-specific survival (DSS) and overall survival (OS) served as outcome measures. Kaplan-Meier estimates and Cox proportional hazards regression models were used for survival analysis. A bootstrap resampling technique was applied to investigate the stability of outcomes. Finally, RPA-based model was constructed. A total of 113 patients were included, of which 28 were p16-positive. Multivariate analysis identified the Ang's profile, high EGFR expression (IHC result), high SUV-entropy and high asphericity as independent predictors of both DSS and OS. Using RPA, the three risk factors were used to devise a prognostic scoring system that successfully predicted DSS in both p16-positive and -negative cases. In patients showing an Ang's high-risk profile (77 cases), the use of our scoring system clearly identified three distinct prognostic subgroups. It was concluded that a novel index may improve the prognostic stratification of patients with advanced-stage OPSCC.
PET parameters provided useful prediction of survivals for patients with OPSCC.

Abstract i
中文摘要 iv
Contents vi
Chapter 1. Introduction 1
1.1 Oropharyngeal squamous cell carcinoma 2
1.2 18F-fluorodeoxyglucose positron emission tomography image 4
1.3 Texture and shape features of FDG PET 5
1.3.1 Histogram analysis 6
1.3.2 Normalized gray-level cooccurrence matrix (NGLCM) 7
1.3.3 Gray-level size zone matrix (GLSZM) 8
1.3.4 Shape analysis 9
1.3.5 Variations of PET image parameters 9
1.4 Scope of the Thesis 10
Chapter 2. Texture and shape features of pretreatment 18F-FDG PET/CT in oropharyngeal carcinoma: Training and validation for prognostic values 12
2.1. Rational and significances 13
2.2. Materials and Methods 13
2.2.1 Study patients 13
2.2.2 FDG PET/CT acquisition 15
2.2.3 FDG PET/CT image analyses 15
2.2.4 Statistical analysis 16
2.3. Results 18
2.3.1 Patient characteristics and outcomes 18
2.3.2 PET parameters and prediction of survival 21
2.3.3 PET prognostic index 21
Chapter 3: Texture and shape features of pretreatment 18F-FDG PET/CT combined with expression of EGFR improves the prognostic stratification of oropharyngeal carcinoma 25
3.1. Rational and significances 26
3.2. Materials and methods 26
3.2.1 Study patients 26
3.2.2 Immunohistochemical analyses 28
3.2.3 18F-FDG PET/CT and imaging analysis 29
3.2.4 Statistical analysis 30
3.3. Results 31
3.3.1 Patient characteristics 31
3.3.2 Patient outcomes 33
3.3.3 Prediction of survival 34
Chapter 4: Discussion 37
Referred Publication 45
Reference 46
Figures and Legends 57
Tables 69

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010;127:2893-2917.

2. Applebaum KM, Furniss CS, Zeka A, et al. Lack of association of alcohol and tobacco with HPV16-associated head and neck cancer. J Natl Cancer Inst. 2007;99:1801-1810.

3. Bouvard V, Baan R SK, Grosse Y, et al. A review of human carcinogens - Part B: biological agents. Lancet Oncol. 2009;10:321-322.

4. Näsman A, Attner P, Hammarstedt L, et al. Incidence of human papillomavirus (HPV) positive tonsillar carcinoma in Stockholm, Sweden: an epidemic of viral-induced carcinoma? Int J Cancer. 2009;125:362-366.

5. Hong AM, Grulich AE, Jones D, et al. Squamous cell carcinoma of the oropharynx in Australian males induced by human papillomavirus vaccine targets. Vaccine. 2010;28:3269-3272.

6. Näsman A, Nordfors C, Holzhauser S, et al. Incidence of human papillomavirus positive tonsillar and base of tongue carcinoma: a stabilisation of an epidemic of viral induced carcinoma? Eur J Cancer. 2015;51:55-61.

7. Chaturvedi AK, Engels EA, Pfeiffer RM, et al. Human papillomavirus and rising oropharyngeal cancer incidence in the United States. J Clin Oncol. 2011;29:4294-4301.

8. Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363:24-35.

9. Granata R, Miceli R, Orlandi E, et al. Tumor stage, human papillomavirus and smoking status affect the survival of patients with oropharyngeal cancer: an Italian validation study. Ann Oncol. 2012;23:1832-1837.

10. Bossi P, Orlandi E, Miceli R, et al. Treatment-related outcome of oropharyngeal cancer patients differentiated by HPV dictated risk profile: a tertiary cancer centre series analysis. Ann Oncol 2014;25:694-699.

11. Hitt R, Grau JJ, López-Pousa A, et al. A randomized phase III trial comparing induction chemotherapy followed by chemoradiotherapy versus chemoradiotherapy alone as treatment of unresectable head and neck cancer. Ann Oncol. 2014;25:216-225.

12. Urban D, Corry J RD. What is the best treatment for patients with human papillomavirus-positive and -negative oropharyngeal cancer? Cancer. 2014;120:1462-1470.

13. Dahlstrom KR, Garden AS, William WN Jr, Lim MY, Sturgis EM. Proposed Staging System for Patients With HPV-Related Oropharyngeal Cancer Based on Nasopharyngeal Cancer N Categories. J Clin Oncol. 2016;34:1848-1854.

14. Liu C, Mann D, Sinha UK, Kokot NC. The molecular mechanisms of increased radiosensitivity of HPV-positive oropharyngeal squamous cell carcinoma (OPSCC): an extensive review. J Otolaryngol Head Neck Surg. 2018;47:59. doi: 10.1186/s40463-40018-40302-y.

15. Rischin D, Young RJ, Fisher R, et al. Prognostic significance of p16INK4A and human papillomavirus in patients with oropharyngeal cancer treated on TROG 02.02 phase III trial. J Clin Oncol. 2010;28:4142-4148.

16. 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. CA Cancer J Clin. 2017;67:122-137.

17. Nauta IH, Rietbergen MM, van Bokhoven AAJD, et al. Evaluation of the eighth TNM classification on p16-positive oropharyngeal squamous cell carcinomas in the Netherlands and the importance of additional HPV DNA testing. Ann Oncol. 2018;29:1273-1279.

18. Gillison ML, Zhang Q, Jordan R, et al. Tobacco smoking and increased risk of death and progression for patients with p16-positive and p16-negative oropharyngeal cancer. J Clin Oncol. 2012;30:2102-2111.

19. O'Sullivan B, Huang SH, Siu LL, et al. Deintensification candidate subgroups in human papillomavirus-related oropharyngeal cancer according to minimal risk of distant metastasis. J Clin Oncol. 2013;31:543-550.

20. Bhatia A, Burtness B. Human Papillomavirus-Associated Oropharyngeal Cancer: Defining Risk Groups and Clinical Trials. J Clin Oncol. 2015;33:3243-3250.

21. Wagner S, Wittekindt C, Sharma SJ, et al. Human papillomavirus association is the most important predictor for surgically treated patients with oropharyngeal cancer. Br J Cancer. 2017;116:1604-1611.

22. Huang SH, Xu W, Waldron J, et al. Refining American Joint Committee on Cancer/Union for International Cancer Control TNM Stage and Prognostic Groups for Human Papillomavirus-Related Oropharyngeal Carcinomas. J Clin Oncol. 2015;33:836-845.

23. Psyrri A, Yu Z, Weinberger PM, et al. Quantitative determination of nuclear and cytoplasmic epidermal growth factor receptor expression in oropharyngeal squamous cell cancer by using automated quantitative analysis. Clin Cancer Res. 2005;11:5856-5862.

24. Kumar B, Cordell KG, Lee JS, et al. EGFR, p16, HPV Titer, Bcl-xL and p53, sex, and smoking as indicators of response to therapy and survival in oropharyngeal cancer. J Clin Oncol. 2008;26:3128-3137.

25. Al-Swiahb JN, Huang CC, Fang FM, et al. Prognostic impact of p16, p53, epidermal growth factor receptor, and human papillomavirus in oropharyngeal cancer in a betel nut-chewing area. Arch Otolaryngol Head Neck Surg. 2010;136:502-508.

26. Lassen P, Overgaard J, Eriksen JG. Expression of EGFR and HPV-associated p16 in oropharyngeal carcinoma: correlation and influence on prognosis after radiotherapy in the randomized DAHANCA 5 and 7 trials. Radiother Oncol. 2013;108:489-494.

27. Vainshtein JM, Spector ME, McHugh JB, et al. Refining risk stratification for locoregional failure after chemoradiotherapy in human papillomavirus-associated oropharyngeal cancer. Oral Oncol. 2014;50:513-519.

28. Huang SF, Cheng SD, Chuang WY, et al. Cyclin D1 overexpression and poor clinical outcomes in Taiwanese oral cavity squamous cell carcinoma. World J Surg Oncol. 2012;10.

29. Bhavna Kumar, Arti Yadav, Nicole V. Brown, et al. Nuclear PRMT5, cyclin D1 and IL-6 are associated with poor outcome in oropharyngeal squamous cell carcinoma patients and is inversely associated with p16-status. Oncotarget. 2017;8:14847-14859.

30. Ang KK, Zhang Q, Rosenthal DI, et al. Randomized phase III trial of concurrent accelerated radiation plus cisplatin with or without cetuximab for stage III to IV head and neck carcinoma: RTOG 0522. J Clin Oncol. 2014;32:2940-2950.

31. Masterson L, Moualed D, Liu ZW, et al. De-escalation treatment protocols for human papillomavirus-associated oropharyngeal squamous cell carcinoma: a systematic review and meta-analysis of current clinical trials. Eur J Cancer. 2014;50:2636-2648.

32. Aloj L, Caracó C, Jagoda E, Eckelman WC, Neumann RD. Glut-1 and hexokinase expression: relationship with 2-fluoro-2-deoxy-D-glucose uptake in A431 and T47D cells in culture. Cancer Res. 1999;59:4709-4714.

33. Ong LC, Jin Y, Song IC, Yu S, Zhang K, Chow PK. 2-[18F]-2-deoxy-D-glucose (FDG) uptake in human tumor cells is related to the expression of GLUT-1 and hexokinase II. Acta Radiol. 2008;49:1145-1153.

34. Som P, Atkins HL, Bandoypadhyay D, et al. A fluorinated glucose analog, 2-fluoro-2-deoxy-D-glucose (F-18): nontoxic tracer for rapid tumor detection. J Nucl Med. 1980;21:670-675.

35. Querellou S, Abgral R, Le Roux PY, et al. Prognostic value of fluorine-18 fluorodeoxyglucose positron-emission tomography imaging in patients with head and neck squamous cell carcinoma. Head Neck. 2012;34:462-468.

36. Abgral R, Le Roux PY, Rousset J, et al. Prognostic value of dual-time-point 18F-FDG PET-CT imaging in patients with head and neck squamous cell carcinoma. Nucl Med Commun. 2013;34:551-556.

37. Dibble EH, Alvarez AC, Truong MT, Mercier G, Cook EF, Subramaniam RM. 18F-FDG metabolic tumor volume and total glycolytic activity of oral cavity and oropharyngeal squamous cell cancer: adding value to clinical staging. J Nucl Med. 2012;53:709-715.

38. Lim R, Eaton A, Lee NY, et al. 18F-FDG PET/CT Metabolic Tumor Volume and Total Lesion Glycolysis Predict Outcome in Oropharyngeal Squamous Cell Carcinoma. J Nucl Med. 2012;53:1506-1513.

39. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12:323-334.

40. Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol 2011;35:853-860.

41. Davnall F, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3:573-589.

42. Chen SW, Shen WC, Lin YC, et al. Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes. Eur J Nucl Med Mol Imaging. 2017;44:567-580.

43. Tixier F, Le Rest CC, Hatt M, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369-378.

44. Cheng NM, Fang DY, Chang JT, et al. Texture features of pretreatment 18F FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54:1703-1709.

45. Cook GJ, Yip C, Siddique M, et al. Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? J Nucl Med. 2013;54:19-26.

46. Cheng NM, Fang YH, Lee LY, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419-428.

47. Kidd EA, Grigsby PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res. 2008;14:5236-5241.

48. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144:646-674.

49. El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162-1171.

50. Chan SC, Cheng NM, Hsieh CH, et al. Multiparametric imaging using 18F-FDG PET/CT heterogeneity parameters and functional MRI techniques: prognostic significance in patients with primary advanced oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiotherapy. Oncotarget. 2017;8:62606-62621.

51. Wang HM, Cheng NM, Lee LY, et al. Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. Int J Cancer. 2016;138:731-738.

52. Cheng NM, Fang YD, Tsan DL, et al. Heterogeneity and irregularity of pretreatment 18F-fluorodeoxyglucose positron emission tomography improved prognostic stratification of p16-negative high-risk squamous cell carcinoma of the oropharynx. Oral Oncol. 2018;78:156-162.

53. Cheng NM, Fang YH, Tsan DL, Hsu CH, Yen TC. Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients. PLoS One. 2016;11:e0150509.

54. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 2010;49:1012-1016.

55. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in (18)F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133-140.

56. Yan J, Chu-Shern JL, Loi HY, et al. Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET. J Nucl Med. 2015;56:1667-1673.

57. Desseroit MC, Tixier F, Weber WA, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort. J Nucl Med. 2017;58:406-411.

58. Haralick Robert M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;3:610-621.

59. Tesar L, Shimizu A, Smutek D, Kobatake H, Nawano S. Medical image analysis of 3D CT images based on extension of Haralick texture features. Comput Med Imaging Graph. 2008;32:510-520.

60. Thibault G, Fertil B, Navarro C, et al. Texture indexes and gray level size zone matrix: application to cell nuclei classification. In: Krasnoproshin V, Ablameyko S, Sadykhov R, editors Proceedings of 10th International Conference on Pattern Recognition and Information Processing. 2009;Minsk, Belarus. Belarus: Belarusian State University:140-145.

61. Apostolova I, Rogasch J, Buchert R, et al. Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC. BMC Cancer. 2014;14:896.

62. Apostolova I, Steffen IG, Wedel F, et al. Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer. Eur Radiol. 2014;24:2077-2087.

63. Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662-1671.

64. La TH, Filion EJ, Turnbull BB, et al. Metabolic tumor volume predicts for recurrence and death in head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2009;74:1335-1341.

65. Tang C, Murphy JD, Khong B, et al. Validation that metabolic tumor volume predicts outcome in head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2012;83:1514-1520.

66. Fang YH, Lin CY, Shih MJ, et al. Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;doi: 10.1155/2014/248505.

67. Irene A. Burger, Hebert Alberto Vargas, Olivio F. Donati, et al. The value of 18F-FDG PET/CT in recurrent gynecologic malignancies prior to pelvic exenteration. Gynecol Oncol. 2013;129:586-592.

68. Satoh Y, Nambu A, Ichikawa T, Onishi H. Whole-body total lesion glycolysis measured on fluorodeoxyglucose positron emission tomography/computed tomography as a prognostic variable in metastatic breast cancer. BMC Cancer. 2014;14:doi: 10.1186/1471-2407-1114-1525.

69. Folkert MR, Setton J, Apte AP, et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol 2017;62:5327-5343.

70. Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151-165.

71. Lin SH, Komaki R, Liao Z, et al. Proton beam therapy and concurrent chemotherapy for esophageal cancer. Int J Radiat Oncol Biol Phys. 2012;83:e345-351.

72. Klussmann JP, Gültekin E, Weissenborn SJ, et al. Expression of p16 protein identifies a distinct entity of tonsillar carcinomas associated with human papillomavirus. Am J Pathol. 2003;162:747-753.

73. Salazar CR, Anayannis N, Smith RV, et al. Combined P16 and human papillomavirus testing predicts head and neck cancer survival. Int J Cancer. 2014;135:2404-2412.

74. Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92:205-216.

75. Reimers N, Kasper HU, Weissenborn SJ, et al. Combined analysis of HPV-DNA, p16 and EGFR expression to predict prognosis in oropharyngeal cancer. Int J Cancer. 2007;120:1731-1738.

76. Sedaghat AR, Zhang Z, Begum S, et al. Prognostic significance of human papillomavirus in oropharyngeal squamous cell carcinomas. Laryngoscope. 2009;119:1542-1549.

77. Hyun SH, Kim HS, Choi SH, et al. Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2016;43:1461-1468.

78. Lee Y, Dominy JE, Choi YJ, et al. Cyclin D1-Cdk4 controls glucose metabolism independently of cell cycle progression. Nature. 2014;510:547-551.

79. Orlhac F, Thézé B, Soussan M, Boisgard R, Buvat I. Multi-scale texture analysis: from 18F-FDG PET images to pathological slides. J Nucl Med. 2016;57:1823-1828.

80. Gong P, Wang Y, Liu G, Zhang J, Wang Z. New insight into Ki67 expression at the invasive front in breast cancer. PLoS One. 2013;8:e54912.

81. Waclaw B, Bozic I, Pittman ME, Hruban RH, Vogelstein B, Nowak MA. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature. 2015;525:261-264.

82. Chen RY, Lin YC, Shen WC, et al. Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck. Sci Rep. 2018;8:105. doi: 110.1038/s41598-41017-18489-41592.

83. Bluff JE, Menakuru SR, Cross SS, et al. Angiogenesis is associated with the onset of hyperplasia in human ductal breast disease. Br J Cancer. 2009;101:666-672.

84. Hensley CT, Faubert B, Yuan Q, et al. Metabolic Heterogeneity in Human Lung Tumors. Cell. 2016;164:681-694.

85. Ntziachristos V, Pleitez MA, Aime S, Brindle KM. Emerging Technologies to Image Tissue Metabolism. Cell Metab. 2019;29:518-538.

86. Mirghani H, Amen F, Moreau F, Guigay J, Hartl DM, Lacau St Guily J. Oropharyngeal cancers: Relationship between epidermal growth factor receptor alterations and human papillomavirus status. Eur J Cancer. 2014;50:1100-1111.

87. Sørensen BS, Busk M, Olthof N, et al. Radiosensitivity and effect of hypoxia in HPV positive head and neck cancer cells. Radiother Oncol. 2013;108:500-505.

88. The Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517:576-582.

89. Fazal MI, Patel ME, Tye J, Y G. The past, present and future role of artificial intelligence in imaging. Eur J Radiol. 2018;105:246-250.

90. C L. The future of radiology augmented with Artificial Intelligence: A strategy for success. Eur J Radiol. 2018;102:152-156.

91. Chen PC, Liu Y, L P. How to develop machine learning models for healthcare. Nat Mater 2019;18:410-414.

92. Shen WC CS, Wu KC, Hsieh TC, et al. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography. Eur Radiol. 2019;May 27. doi: 10.1007/s00330-019-06265-x.

93. Castelli J, Depeursinge A, de Bari B, et al. Metabolic Tumor Volume and Total Lesion Glycolysis in Oropharyngeal Cancer Treated With Definitive Radiotherapy: Which Threshold Is the Best Predictor of Local Control? Clin Nucl Med. 2017;42:e281-e285.

94. Bogowicz M, Riesterer O, Ikenberg K, et al. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys. 2017;Jun 15. pii: S0360-3016(17)31011-8.

95. Bonner JA, Mesia R, Giralt J, et al. p16, HPV, and Cetuximab: What Is the Evidence? Oncologist. 2017;22:811-822.