Identification of HPV status in head and neck squamous cell carcinoma patients based on radiomics: diagnostic meta-analysis

doi:10.19438/j.cjoms.2023.01.012

Abstract

Abstract: PURPOSE: To evaluate the value of human papillomavirus (HPV) status in patients with head and neck squamous cell carcinoma (HNSCC) using diagnostic meta-analysis based on radiomics. METHODS: PubMed, Embase, Web of Science, CNKI and VIP databases were searched for literatures related to radiomics identification of HPV status. The retrieval time was from the establishment of the database to May 28, 2022. Statistical software such as Revman 5.4 was used to evaluate the diagnostic performance of the prediction model. RESULTS: A total of 9 papers, including 3312 patients, were included in this study. The combined results showed that the sensitivity, specificity and positive likelihood ratio of radiomic-based methods in the diagnosis of HPV status were 0.82[95%CI(0.74,0.87)], 0.66[95%CI(0.54,0.75)] and 2.4[95%CI(1.8,3.1)], respectively. The negative likelihood ratio was 0.28[95%CI(0.21,0.36)], the diagnostic odds ratio was 9[95%CI(6,12)], and the area under the receiver operating characteristic curve (AUC) was 0.82[95%CI(0.78,0.85)]. HPV status was identified by imaging histochemistry with a 25% pretest probability, which was elevated to 44%(post-test probability). CONCLUSIONS: The radiomics method has high practical value in identification of HPV status in patients with HNSCC.

Key words: Head and neck squamous cell carcinoma, Human Papillomavirus, Radiomics, Diagnostic meta-analysis

CLC Number:

R739.8

LI Xue-er, WANG Zheng-ye, TUERDI Maimaitituxun. Identification of HPV status in head and neck squamous cell carcinoma patients based on radiomics: diagnostic meta-analysis[J]. China Journal of Oral and Maxillofacial Surgery, 2023, 21(1): 69-74.

References

[1] Cai ZJ,Liu Q.Understanding the Global Cancer Statistics 2018: implications for cancer control[J]. Sci China Life Sci, 2021,64(6): 1017-1020.
[2] Marur S, Forastiere AA.Head and neck squamous cell carcinoma: update on epidemiology, diagnosis, and treatment[J]. Mayo Clin Proc, 2016, 91(3): 386-396.
[3] Syrjönen S.Human papillomavirus (HPV) in head and neck cancer[J]. J Clin Virol, 2005, 32(Suppl 1): S59-S66.
[4] Mirghani H, Blanchard P.Treatment de-escalation for HPV-driven oropharyngeal cancer: where do we stand?[J]. Clin Transl Radiat Oncol, 2018, 8: 4-11.
[5] Bruixola G, Remacha E, Jiménez-Pastor A, et al.Radiomics and radiogenomics in head and neck squamous cell carcinoma: potential contribution to patient management and challenges[J]. Cancer Treat Rev, 2021, 99: 102263.
[6] Whiting PF, Rutjes AWS, Westwood ME, et al.QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies[J]. Ann Intn Med, 2011, 155(8): 529-536.
[7] Huang C, Cintra M, Brennan K, et al.Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes[J]. EBioMedicine, 2019, 45: 70-80.
[8] Bagher-Ebadian H, Lu M, Siddiqui F, et al.Application of radiomics for the prediction of HPV status for patients with head and neck cancers[J]. Med Phys, 2020, 47(2): 563-575.
[9] 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[J].Int J Radiat Oncol Biol Phys, 2017, 99(4): 921-928.
[10] Suh CH, Lee KH, Choi YJ, et al.Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status[J]. Sci Rep, 2020, 10(1): 17525.
[11] Lv W, Xu H, Han X, et al.Context-aware saliency guided radiomics: application to prediction of outcome and HPV-status from multi-center PET/CT images of head and neck cancer[J]. Cancers, 2022, 14(7): 1674-1692.
[12] Ranjbar S, Ning S, Zwart CM, et al.Computed tomography-based texture analysis to determine human papillomavirus status of oropharyngeal squamous cell carcinoma[J]. J Comput Assist Tomogr, 2018, 42(2): 299-305.
[13] Leijenaar RT, Bogowicz M, Jochems A, et al.Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study[J]. Br J Radiol, 2018, 91(1086): 20170498.
[14] Bogowicz M, Jochems A, Deist TM, et al.Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer[J]. Sci Rep, 2020, 10(1): 4542.
[15] Yu K, Zhang Y, Yu Y, et al.Radiomic analysis in prediction of Human Papilloma Virus status[J]. Clin Translat Radiat Oncol, 2017, 7: 49-54.
[16] Gul M, Bonjoc KJC, Gorlin D, et al.Diagnostic utility of radiomics in thyroid and head and neck cancers[J]. Front Oncol, 2021, 11: 639326.
[17] 杨惠茹, 鲁海珍. 头颈部鳞状细胞癌免疫治疗预测指标及分子标志物的研究进展[J]. 中国肿瘤, 2022, 31(5): 387-393.
Yang HR, Lu HZ.Advances on predictive indicators for prognosis of head and neck squamous cell carcinoma[J].China Cancer, 2022, 31(5): 387-393.
[18] Peng Z, Wang Y, Wang Y, et al.Application of radiomics and machine learning in head and neck cancers[J]. Int J Biolog Sci, 2021, 17(2): 475.
[19] Fujita A, Buch K, Li B, et al.Difference between HPV-positive and HPV-negative non-oropharyngeal head and neck cancer: texture analysis features on CT[J]. J Comput Assist Tomogr, 2016, 40(1): 43-47.
[20] Payabvash S, Chan A, Jabehdar Maralani P, et al.Quantitative diffusion magnetic resonance imaging for prediction of human papillomavirus status in head and neck squamous-cell carcinoma: a systematic review and meta-analysis[J]. Neuroradiol J, 2019, 32(4): 232-240.
[21] 米那瓦尔, 阿达莱提, 买买提吐逊, 等. 口腔鳞癌中HPV16、18型感染和p53蛋白表达的检测研究[J]. 中华口腔医学杂志, 2001,36(6):451-453.
Minawaer, Adalaiti, Maimaitituxun, et al. Detection of HPV type 16、18 infection and p53 protein overexpression in oral squamous cell carcinoma[J]. Chinese Journal of Stomatology, 2001, 36(6): 451-453.
[22] 林涛,刘爱连.基于医学影像的影像组学及深度学习在肝细胞癌中的研究进展[J].中国医学影像学杂志,2022,30(4):401-405.
Lin T, Liu AL.Advances in medical image-based radiomics and deep learning in patients with hepatocellular carcinoma[J]. Chinese Journal of Medical Imaging, 2022, 30(4): 401-405.
[23] 周健文,冯峰.食管癌CT影像组学研究进展[J].CT理论与应用研究, 2022, 31(5): 687-696.
Zhou JW, Feng F.Advances in CT radiomics of esophageal cancer[J]. CT Theory and Application Research, 2022, 31(5): 687-696.
[24] 屠妙倩, 蓝雨晴, 郭潇辰, 等. 良恶性肺结节CT定量及影像组学研究进展[J].现代医药卫生, 2022, 38(9): 1527-1531.
Tu MQ, Lan YQ, Guo XC, et al.Advances in CT quantification and radiomics of benign and malignant pulmonary nodules[J]. Modern Medicine and Health, 2022, 38(9): 1527-1531.
[25] 罗焱文, 朱庆莉. 乳腺癌影像组学研究进展[J]. 协和医学杂志, 2021, 12(6): 983-988.
Luo YW, Zhu QL.Application of radiomics in breast cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 983-988.
[26] 李婧, 张红, 束研, 等. 国家中医药数据中心医学影像索引库建设实践[J]. 中国数字医学, 2019, 14(1): 26-28.
Li J, Zhang H, Shu Y, et al.Construction practice of the medical picture index library for National Traditional Chinese Medicine Data Center[J]. China Digital Medicine, 2019, 14(1): 26-28.
[27] 陈余, 荆慧. 基于深度学习的超声影像组学在乳腺癌中的研究进展[J]. 肿瘤学杂志, 2022, 28(9): 730-735.
Chen Y, Jing H.Advances in application of ultrasound-based deep learning radiomics in breast cancer[J]. Journal of Oncology, 2022, 28(9): 730-735.
[28] 刘波, 刘菲, 周冠知, 等. 人工智能在胃癌影像学中的应用进展[J].磁共振成像, 2022, 13(6): 155-159.
Liu B, Liu F, Zhou GZ, et al.The application progress of artificial intelligence in gastric cancer imaging[J]. Magnetic Resonance Imaging, 2022, 13(6): 155-159.