Blog
Home Blog An Article on oropharyngeal cancer due to HPV

An Article on oropharyngeal cancer due to HPV

Akash Pandey

Assistant Professor

CS & IT Dept.

Kalinga University

akash.pandey@kalingauniversity.ac.in

 

Introduction:

Oropharyngeal cancer, a type of head and neck cancer, has emerged as a significant health concern in recent years. Human papillomavirus (HPV) has been identified as a major risk factor for the development of oropharyngeal cancer. This article aims to explore the relationship between HPV and oropharyngeal cancer, including its prevalence, risk factors, symptoms, diagnostic methods, treatment options, and preventive measures.

Keywords: HPV, Oropharyngeal cancer.

Understanding HPV and Oropharyngeal Cancer:

HPV is a common sexually transmitted infection that affects both men and women. While most HPV infections are harmless and clear up on their own, persistent infections with high-risk HPV strains, primarily HPV-16 and HPV-18, can lead to the development of various cancers, including oropharyngeal cancer. The presence of HPV in oropharyngeal cancer tumors has been established through extensive research.

Prevalence and Trends:

The prevalence of oropharyngeal cancer caused by HPV has been steadily rising in recent years. It is estimated that HPV accounts for approximately 70% of oropharyngeal cancer cases worldwide. This shift in the etiology of oropharyngeal cancer is particularly noticeable among younger individuals, with a higher incidence in men compared to women.

Risk Factors:

Several risk factors contribute to the development of oropharyngeal cancer due to HPV infection. Engaging in oral sex, having multiple sexual partners, smoking tobacco, and having a weakened immune system increase the risk of acquiring HPV and developing oropharyngeal cancer. Additionally, studies have shown that certain genetic and lifestyle factors may also play a role in susceptibility to HPV-related oropharyngeal cancer.

Symptoms and Diagnosis:

The symptoms of oropharyngeal cancer, whether HPV-related or not, are similar and may include persistent sore throat, difficulty swallowing, ear pain, changes in the voice, and the presence of a lump or mass in the neck or throat. Diagnosis often involves a thorough examination of the oropharynx, biopsies, imaging tests, and testing for the presence of HPV DNA in the tumor tissue.

Treatment Options:

Treatment for oropharyngeal cancer due to HPV infection may involve a multidisciplinary approach, including surgery, radiation therapy, and chemotherapy. The specific treatment plan depends on the stage of the cancer, the location and size of the tumor, and the overall health of the patient. HPV-related oropharyngeal cancers tend to respond better to treatment and have a more favorable prognosis compared to non-HPV-related cases.

Prevention and Vaccination:

Preventing HPV infection is a crucial step in reducing the risk of oropharyngeal cancer. Vaccination against HPV is highly effective and is recommended for both males and females. The HPV vaccine provides protection against the most common cancer-causing HPV strains. Safe sexual practices, including the use of condoms and regular dental check-ups, are additional preventive measures that can reduce the transmission of HPV.

Conclusion:

The connection between HPV infection and oropharyngeal cancer has shed new light on the etiology and prevention of this type of cancer. Increased awareness, early detection, and preventive measures, such as HPV vaccination and safe sexual practices, are paramount in reducing the burden of HPV-related oropharyngeal cancer. By prioritizing education and encouraging regular screenings, we can work towards early diagnosis and improved outcomes for individuals affected by this preventable form of cancer.

References:

 

 

1.   Park JG, Lee C (2009) Skull stripping based on region growingformagneticresonancebrainimages.Neuroimage47:1394–1407

2.   A. Pandey and S. K. Shrivastava, “A Survey Paper on Calcaneus Bone Tumor Detection Using different Improved Canny Edge Detector,” 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA), Pondicherry, India, 2018, pp. 1-5, doi: 10.1109/ICSCAN.2018.8541194.

 

3.   Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T et al(2019)Braintumordetectionandclassification:Aframeworkofmarker-basedwatershedalgorithmandmultilevelpriorityfeaturesselection.MicroscResTech82:909–922.

 

4.   RazaM,SharifM,YasminM,MasoodS,MohsinS(2012)Brainimagerepresentationandrendering:asurvey.ResJApplSciEngTechnol4:3274–3282

 

5.   Watson C, Kirkcaldie M, Paxinos G (2010) The brain: an intro-ductiontofunctionalneuroanatomy.AcademicPress,NewYork

 

6.   (2015).https://en.wikipedia.org/wiki/Brain_size.Accessed19Oct2019

 

7.   DubinMW(2013)Howthebrainworks.Wiley,NewYork

 

8.   Koziol LF, Budding DE, Chidekel D (2012) From movement tothought: executive function, embodied cognition, and the cerebel-lum.Cerebellum11:505–525

 

9.   Knierim J (1997) Neuroscience Online Chapter 5: Cerebellum.TheUniversityofTexasHealthScienceCenter,Houston.

 

10.Nuñez MA, Miranda JCF, de Oliveira E, Rubino PA, VoscoboinikS,RecaldeRetal(2019)Brainstemanatomyandsurgi-cal approaches. Comprehensive overview of modern surgicalapproaches to intrinsic brain tumors. Elsevier, Amsterdam, pp53–105

 

11.   DeAngelisLM(2001)Braintumors.NEnglJMed344:114–123

 

12.   Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA (2020) Braintumordetection:alongshort-termmemory(LSTM)-basedlearn-ingmodel.NeuralComputAppl32:15965–15973

 

13.   Sajjad S, Hanan Abdullah A, Sharif M, Mohsin S (2014) Psy-chotherapythroughvideogametotargetillnessrelatedproblem-atic behaviors of children with brain tumor. Curr Med Imaging10:62–72

 

14.   YasminM,SharifM,MasoodS,RazaM,MohsinS(2012)Brainimagereconstruction:ashortsurvey.WorldApplSciJ19:52–62

 

15.   AminJ,SharifM,RazaM,YasminM(2018)Detectionofbraintumorbasedonfeaturesfusionandmachinelearning.JAmbientIntellHuman Comput:1–17

 

16.   AminJ,SharifM,YasminM,FernandesSL(2020)Adistinctiveapproach in brain tumor detection and classification using MRI.PatternRecognLett139:118–127

 

17.   Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020)Brain tumor detection using fusion of hand crafted and deep learn-ingfeatures.CognSystRes59:221–230

 

18.   Sharif M, Amin J, Nisar MW, Anjum MA, Muhammad N, ShadSA (2020) A unified patch based method for brain tumor detectionusingfeaturesfusion.CognSystRes59:273–286

19.   Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018)Braintumorsegmentationandclassificationbyimprovedbino-mialthresholding and multi-features selection. J Ambient IntellHumanComput:1–20.

 

20.   Sharif MI, Li JP, Khan MA, Saleem MA (2020) Active deepneural network features selection for segmentation and recog-nition of brain tumors using MRI images. Pattern RecognLett129:181–189

 

21.   SharifMI,LiJP,NazJ,RashidI(2020)Acomprehensivereviewonmulti-organstumordetectionbasedonmachinelearning.Pat-ternRecognLett131:30–37

 

22.   Ohgaki H, Kleihues P (2013) The definition of primary and sec-ondaryglioblastoma.ClinCancerRes19:764–772

 

23.   Cachia D, Kamiya-Matsuoka C, Mandel JJ, Olar A, CykowskiMD, Armstrong TS et al (2015) Primary and secondary gliosarco-mas: clinical, molecular and survival characteristics. J Neurooncol125:401–410

 

24.   Amin J, Sharif M, Gul N, Yasmin M, Shad SA (2020) Brain tumorclassification based on DWT fusion of MRI sequences using con-volutionalneuralnetwork.PatternRecognLett129:115–122

 

25.   SharifM,AminJ,RazaM,YasminM,SatapathySC(2020)An integrated design of particle swarm optimization (PSO) withfusion of features for detection of brain tumor. Pattern RecognLett129:150–157

 

26.   Amin J, Sharif M, Anjum MA, Raza M, Bukhari SAC (2020) Con-volutional neural network with batch normalization for glioma andstrokelesiondetectionusingMRI.CognSystRes59:304–311

 

27.   Amin J, Sharif M, Raza M, Saba T, Anjum MA (2019) Braintumor detection using statistical and machine learning method.ComputMethodsProgrBiomed177:69–79

 

28.   Amin J, Sharif M, Gul N, Raza M, Anjum MA, Nisar MW et al(2020) Brain tumor detection by using stacked autoencoders indeeplearning.JMedSyst44:32

 

29.   Johnson DR, Guerin JB, Giannini C, Morris JM, Eckel LJ,Kaufmann TJ (2017) 2016 updates to the WHO brain tumorclassificationsystem:whattheradiologistneedstoknow.Radio-graphics37:2164–2180

 

30.   WrightE,AmankwahEK,WinesettSP,TuiteGF,JalloG,CareyC et al (2019) Incidentally found brain tumors in the pediatricpopulation: a case series and proposed treatment algorithm. J Neu-rooncol141:355–361

 

31.   PellegrinoMP,MoreiraF,ConfortoAB(2021)Ischemicstroke.Neurocritical care for neurosurgeons. Springer, New York, pp517–534

 

32.   GarrickR,RotundoE,ChughSS,BrevikTA(2021)Acutekidney injuryintheelderlysurgicalpatient.Emergencygeneralsurgeryingeriatrics.Springer,NewYork,pp205–227

 

33.   Lehmann ALCF, Alfieri DF, de Araújo MCM, Trevisani ER,NagaoMR,PesenteFS,GelinskiJR,deFreitasLB,FlauzinoT, Lehmann MF, Lozovoy MAB (2021) Carotid intima mediathickness measurements coupled with stroke severity strongly pre-dictshort-termoutcomeinpatientswithacuteischemicstroke:amachinelearningstudy.MetabBrainDis36:1747–1761

 

34.   Scott AM (2005) PET imaging in oncology. In: Bailey DL,Townsend DW, Valk PE, Maisey MN (eds) Positron emissiontomography.Springer,London,pp311–325

 

35.   WongTZ,vanderWesthuizenGJ,ColemanRE(2002)Positronemission tomography imaging of brain tumors. NeuroimagingClin12:615–626

 

36.   WongKP,FengD,MeikleSR,FulhamMJ(2002)Segmentationof dynamicPETimagesusingclusteranalysis.IEEETransNuclearSci49:200–207

 

37.   BrennerDJ,HallEJ(2007)Computedtomography—anincreas-ingsourceofradiationexposure.NEnglJMed357:2277–2284

 

38.   Smith-Bindman R, Lipson J, Marcus R, Kim K-P, Mahesh M,Gould R et al (2009) Radiation dose associated with commoncomputed tomography examinations and the associated lifetimeattributableriskofcancer.ArchInternMed169:2078–2086

 

39.   Sinha, A., &Barde, S. (2022). Face recognition across age progression by using PCA. International Journal of Food and Nutritional Sciences, 11(S3), 4608-4617. https://doi.org/10.48047/IJFANS/S3/144

 

40.   Sinha, A., &Barde, S. (2022). Age Invariant Face Recogntion Using Pca And Msvm. Journal of Pharmaceutical Negative Results, 2174-2185.

 

41.   Sinha, A., &Barde, S. (2022, October). Illumination invariant face recognition using MSVM. In AIP Conference Proceedings (Vol. 2455, No. 1, p. 040005). AIP Publishing LLC.

 

42.   Pandey, Akash& Sinha, Anupa. (2023). Journal of Clinical Otorhinolaryngology, Head, and Neck Surgery BRAIN TUMOR DETECTION USING CANNY EDGE DETECTOR WITH MACHINE LEARNING. 2023.

 

 

 

 

 

Kalinga Plus is an initiative by Kalinga University, Raipur. The main objective of this to disseminate knowledge and guide students & working professionals.
This platform will guide pre – post university level students.
Pre University Level – IX –XII grade students when they decide streams and choose their career
Post University level – when A student joins corporate & needs to handle the workplace challenges effectively.
We are hopeful that you will find lot of knowledgeable & interesting information here.
Happy surfing!!

  • Free Counseling!