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Laryngeal Cancer

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Most cited protocols related to «Laryngeal Cancer»

1
GBD 2019 Risk-Outcome Pair Updates
Cited 27 times
Since GBD 2010, we have used the World Cancer Research Fund criteria for convincing or probable evidence of risk–outcome pairs.16 For GBD 2019, we completely updated our systematic reviews for 81 risk–outcome pairs. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowcharts on these reviews are available in appendix 1 (section 4). Convincing evidence requires more than one study type, at least two cohorts, no substantial unexplained heterogeneity across studies, good-quality studies to exclude the risk of confounding and selection bias, and biologically plausible dose–response gradients. For GBD, for a newly proposed or evaluated risk–outcome pair, we additionally required that there was a significant association (p<0·05) after taking into account sources of potential bias. To avoid risk–outcome pairs repetitively entering and leaving the analysis with each cycle of GBD, the criteria for exclusion requires that with the available studies the association has a p value greater than 0·1. On the basis of these reviews and meta-regressions, 12 risk–outcome pairs included in GBD 2017 were excluded from GBD 2019: vitamin A deficiency and lower respiratory infections; zinc deficiency and lower respiratory infections; diet low in fruits and four outcomes: lip and oral cavity cancer, nasopharynx cancer, other pharynx cancer, and larynx cancer; diet low in whole grains and two outcomes: intracerebral haemorrhage and subarachnoid haemorrhage; intimate partner violence and maternal abortion and miscarriage; and high FPG and three outcomes: chronic kidney disease due to hypertension, chronic kidney disease due to glomerulonephritis, and chronic kidney disease due to other and unspecified causes. In addition, on the basis of multiple requests to begin capturing important dimensions of climate change into GBD, we evaluated the direct relationship between high and low non-optimal temperatures on all GBD disease and injury outcomes. Rather than rely on a heterogeneous literature with a small number of studies examining relationships with specific diseases and injuries, we analysed individual-level cause of death data for all locations with available information on daily temperature, location, and International Classification of Diseases-coded cause of death. These data totalled 58·9 million deaths covering eight countries. On the basis of this analysis, 27 GBD cause Level 3 outcomes met the inclusion criteria for each non-optimal risk factor (appendix 1 section 2.2.1) and were included in this analysis. Other climate-related relationships, such as between precipitation or humidity and health outcomes, have not yet been evaluated.
Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. (2020). Lancet (London, England), 396(10258), 1223-1249.
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Publication 2020
Cancer of Mouth Cancer of Nasopharynx Cancer of Pharynx Cerebral Hemorrhage Chronic Kidney Diseases Climate Climate Change Cold Temperature Diet Fruit Genetic Heterogeneity Glomerulonephritis Humidity Hypertensive Nephropathy Induced Abortions Injuries Laryngeal Cancer Malignant Neoplasms Mothers Respiratory Tract Infections Spontaneous Abortion Subarachnoid Hemorrhage Vitamin A Deficiency Whole Grains Zinc
2
Development and Evaluation of the QuinteT Recruitment Intervention
Cited 11 times
The development of the recruitment intervention followed an iterative and accumulative process, and while this largely mirrored guidelines now published [15 (link)–17 (link)], it did not strictly follow all the recommendations, as they were not then available. The first version of the recruitment intervention was developed in the feasibility study of an RCT that was considered difficult for recruitment: the NIHR ProtecT (Prostate cancer testing and Treatment) trial with randomisation between radical surgery, radical radiotherapy, and active monitoring for clinically localised prostate cancer. This trial started in 1999, and the recruitment was completed in 2009. The theoretical framework, the context for the intervention, and the evolution and key components of the framework have been published elsewhere [18 (link)–20 (link)]. Additional file 1 contains RCT registration details.
The initial version of the recruitment intervention was then applied through the MRC Quartet (Qualitative Research in Trials) programme in four RCTs expected to have recruitment challenges in different contexts: mental health, paediatrics, treatment for laryngeal cancer, and follow-up strategies following treatment for cancer [8 (link)]. Three RCTs completed recruitment successfully (see, for example, [21 (link)]) and the other closed with clear explication [22 ]. After further refinement, the near-final intervention was applied to another cancer trial, which also closed with clear reasons [23 (link)]. These six RCTs were phase III, pragmatic, unblinded trials in feasibility stages or full-scale recruitment, and all were considered challenging for recruitment because of very contrasting arms, no-treatment comparators, or controversial clinical contexts. The accumulated experience and data from these RCTs were synthesised and used to finalise the intervention, which was then applied in seven further RCTs by the QuinteT (Qualitative Research Integrated in Trials) team (http://www.bristol.ac.uk/social-community-medicine/research/groups/social-sciences-health/quintet/qri-rcts/). The intervention was named after the team: the QuinteT Recruitment Intervention (QRI).
Donovan J.L., Rooshenas L., Jepson M., Elliott D., Wade J., Avery K., Mills N., Wilson C., Paramasivan S, & Blazeby J.M. (2016). Optimising recruitment and informed consent in randomised controlled trials: the development and implementation of the Quintet Recruitment Intervention (QRI). Trials, 17, 283.
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Publication 2016
Arm, Upper Biological Evolution Laryngeal Cancer Malignant Neoplasms Mental Health Operative Surgical Procedures Prostate Cancer Radiotherapy
3
Estimating Cancer Burden: GBD Methodology
Cited 10 times
The GBD cancer mortality and YLL estimation process included 2 primary steps (eFigure 2 in the Supplement), beginning with the estimation of cancer MIRs, which provide an association between mortality and incidence estimation, maximizing data availability. The MIRs were modeled using a space-time Gaussian process regression approach26 (link) (MIR methods are described in the eAppendix in the Supplement) using matched incidence and mortality data from cancer registries (eTable 6 in the Supplement) and the GBD-estimated health care access and quality index33 (link) as a covariate. These estimated MIRs were then used to convert cancer registry incidence data into inputs for mortality modeling.
Estimating cancer mortality was the second step. The GBD 2019 study used a Cause of Death Ensemble model (CODEm) approach that combined data from vital registration systems, cancer registries, and verbal autopsy reports to estimate mortality across several submodels.34 (link) Covariates provided for potential inclusion in the submodels of the ensemble, such as smoking prevalence or alcohol use, can be found in the eAppendix and eTables 7 and 8 in the Supplement. Ensemble model construction and performance was evaluated through out-of-sample predictive validity tests (eTable 9 in the Supplement). For each cancer, sex-specific CODEm models generated mortality estimates across locations, years, and age groups. These cancer mortality estimates were then scaled to align with the total mortality for all causes of death, which was separately estimated in GBD 2019 (eTable 10 in the Supplement).21 (link) To estimate YLLs, a standard age-specific GBD life expectancy was applied to mortality estimates by age group (eAppendix in the Supplement).20 (link)The GBD cancer incidence and YLD estimation process included 2 additional steps (eFigure 3 in the Supplement), starting with estimating incidence. Incidence was estimated by taking mortality estimates from the second step described previously and dividing by MIR estimates from the first step described previously for each cancer type, sex, location, year, and 5-year age group. Additional information can be found in the eAppendix in the Supplement.
Next, YLDs were estimated by combining prevalence estimates with disability weights associated with various phases of cancer survival. To estimate 10-year cancer prevalence, survival curves estimated from MIRs were combined with GBD-estimated background mortality and applied to incidence estimates. Additional information regarding survival and prevalence estimation can be found in the eAppendix and eFigure 3 in the Supplement. These 10-year prevalence estimates were then partitioned into 4 sequelae according to the expected person-time spent in these 4 phases of cancer survival: (1) diagnosis/treatment, (2) remission, (3) metastatic/disseminated, and (4) terminal (eTable 11 in the Supplement). Each sequela prevalence was multiplied by a sequela-specific disability weight that represented the magnitude of health loss (eTable 12 in the Supplement).20 (link) For 5 cancer types (bladder, breast, colorectal, larynx, and prostate cancer), the total prevalence additionally included lifetime prevalence of procedure-related disability (eg, laryngectomy due to larynx cancer). These procedure-related prevalence estimates were modeled in the Bayesian meta-regression tool DisMod-MR, version 2.1,20 (link) using medical records data on the proportion of patients with cancer who underwent these procedures and the estimated number of 10-year survivors (eAppendix in the Supplement). These procedure-related prevalence estimates were then multiplied by procedure-specific disability weights (eTable 12 in the Supplement). Total cancer-specific YLDs were estimated by summing across these sequelae. Finally, DALYs were estimated as the sum of YLDs and YLLs.20 (link)
Kocarnik J.M., Compton K., Dean F.E., Fu W., Gaw B.L., Harvey J.D., Henrikson H.J., Lu D., Pennini A., Xu R., Ababneh E., Abbasi-Kangevari M., Abbastabar H., Abd-Elsalam S.M., Abdoli A., Abedi A., Abidi H., Abolhassani H., Adedeji I.A., Adnani Q.E., Advani S.M., Afzal M.S., Aghaali M., Ahinkorah B.O., Ahmad S., Ahmad T., Ahmadi A., Ahmadi S., Ahmed Rashid T., Ahmed Salih Y., Akalu G.T., Aklilu A., Akram T., Akunna C.J., Al Hamad H., Alahdab F., Al-Aly Z., Ali S., Alimohamadi Y., Alipour V., Aljunid S.M., Alkhayyat M., Almasi-Hashiani A., Almasri N.A., Al-Maweri S.A., Almustanyir S., Alonso N., Alvis-Guzman N., Amu H., Anbesu E.W., Ancuceanu R., Ansari F., Ansari-Moghaddam A., Antwi M.H., Anvari D., Anyasodor A.E., Aqeel M., Arabloo J., Arab-Zozani M., Aremu O., Ariffin H., Aripov T., Arshad M., Artaman A., Arulappan J., Asemi Z., Asghari Jafarabadi M., Ashraf T., Atorkey P., Aujayeb A., Ausloos M., Awedew A.F., Ayala Quintanilla B.P., Ayenew T., Azab M.A., Azadnajafabad S., Azari Jafari A., Azarian G., Azzam A.Y., Badiye A.D., Bahadory S., Baig A.A., Baker J.L., Balakrishnan S., Banach M., Bärnighausen T.W., Barone-Adesi F., Barra F., Barrow A., Behzadifar M., Belgaumi U.I., Bezabhe W.M., Bezabih Y.M., Bhagat D.S., Bhagavathula A.S., Bhardwaj N., Bhardwaj P., Bhaskar S., Bhattacharyya K., Bhojaraja V.S., Bibi S., Bijani A., Biondi A., Bisignano C., Bjørge T., Bleyer A., Blyuss O., Bolarinwa O.A., Bolla S.R., Braithwaite D., Brar A., Brenner H., Bustamante-Teixeira M.T., Butt N.S., Butt Z.A., Caetano dos Santos F.L., Cao Y., Carreras G., Catalá-López F., Cembranel F., Cerin E., Cernigliaro A., Chakinala R.C., Chattu S.K., Chattu V.K., Chaturvedi P., Chimed-Ochir O., Cho D.Y., Christopher D.J., Chu D.T., Chung M.T., Conde J., Cortés S., Cortesi P.A., Costa V.M., Cunha A.R., Dadras O., Dagnew A.B., Dahlawi S.M., Dai X., Dandona L., Dandona R., Darwesh A.M., das Neves J., De la Hoz F.P., Demis A.B., Denova-Gutiérrez E., Dhamnetiya D., Dhimal M.L., Dhimal M., Dianatinasab M., Diaz D., Djalalinia S., Do H.P., Doaei S., Dorostkar F., dos Santos Figueiredo F.W., Driscoll T.R., Ebrahimi H., Eftekharzadeh S., El Tantawi M., El-Abid H., Elbarazi I., Elhabashy H.R., Elhadi M., El-Jaafary S.I., Eshrati B., Eskandarieh S., Esmaeilzadeh F., Etemadi A., Ezzikouri S., Faisaluddin M., Faraon E.J., Fares J., Farzadfar F., Feroze A.H., Ferrero S., Ferro Desideri L., Filip I., Fischer F., Fisher J.L., Foroutan M., Fukumoto T., Gaal P.A., Gad M.M., Gadanya M.A., Gallus S., Gaspar Fonseca M., Getachew Obsa A., Ghafourifard M., Ghashghaee A., Ghith N., Gholamalizadeh M., Gilani S.A., Ginindza T.G., Gizaw A.T., Glasbey J.C., Golechha M., Goleij P., Gomez R.S., Gopalani S.V., Gorini G., Goudarzi H., Grosso G., Gubari M.I., Guerra M.R., Guha A., Gunasekera D.S., Gupta B., Gupta V.B., Gupta V.K., Gutiérrez R.A., Hafezi-Nejad N., Haider M.R., Haj-Mirzaian A., Halwani R., Hamadeh R.R., Hameed S., Hamidi S., Hanif A., Haque S., Harlianto N.I., Haro J.M., Hasaballah A.I., Hassanipour S., Hay R.J., Hay S.I., Hayat K., Heidari G., Heidari M., Herrera-Serna B.Y., Herteliu C., Hezam K., Holla R., Hossain M.M., Hossain M.B., Hosseini M.S., Hosseini M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Househ M., Hsairi M., Huang J., Hugo F.N., Hussain R., Hussein N.R., Hwang B.F., Iavicoli I., Ibitoye S.E., Ida F., Ikuta K.S., Ilesanmi O.S., Ilic I.M., Ilic M.D., Irham L.M., Islam J.Y., Islam R.M., Islam S.M., Ismail N.E., Isola G., Iwagami M., Jacob L., Jain V., Jakovljevic M.B., Javaheri T., Jayaram S., Jazayeri S.B., Jha R.P., Jonas J.B., Joo T., Joseph N., Joukar F., Jürisson M., Kabir A., Kahrizi D., Kalankesh L.R., Kalhor R., Kaliyadan F., Kalkonde Y., Kamath A., Kameran Al-Salihi N., Kandel H., Kapoor N., Karch A., Kasa A.S., Katikireddi S.V., Kauppila J.H., Kavetskyy T., Kebede S.A., Keshavarz P., Keykhaei M., Khader Y.S., Khalilov R., Khan G., Khan M., Khan M.N., Khan M.A., Khang Y.H., Khater A.M., Khayamzadeh M., Kim G.R., Kim Y.J., Kisa A., Kisa S., Kissimova-Skarbek K., Kopec J.A., Koteeswaran R., Koul P.A., Koulmane Laxminarayana S.L., Koyanagi A., Kucuk Bicer B., Kugbey N., Kumar G.A., Kumar N., Kumar N., Kurmi O.P., Kutluk T., La Vecchia C., Lami F.H., Landires I., Lauriola P., Lee S.W., Lee S.W., Lee W.C., Lee Y.H., Leigh J., Leong E., Li J., Li M.C., Liu X., Loureiro J.A., Lunevicius R., Magdy Abd El Razek M., Majeed A., Makki A., Male S., Malik A.A., Mansournia M.A., Martini S., Masoumi S.Z., Mathur P., McKee M., Mehrotra R., Mendoza W., Menezes R.G., Mengesha E.W., Mesregah M.K., Mestrovic T., Miao Jonasson J., Miazgowski B., Miazgowski T., Michalek I.M., Miller T.R., Mirzaei H., Mirzaei H.R., Misra S., Mithra P., Moghadaszadeh M., Mohammad K.A., Mohammad Y., Mohammadi M., Mohammadi S.M., Mohammadian-Hafshejani A., Mohammed S., Moka N., Mokdad A.H., Molokhia M., Monasta L., Moni M.A., Moosavi M.A., Moradi Y., Moraga P., Morgado-da-Costa J., Morrison S.D., Mosapour A., Mubarik S., Mwanri L., Nagarajan A.J., Nagaraju S.P., Nagata C., Naimzada M.D., Nangia V., Naqvi A.A., Narasimha Swamy S., Ndejjo R., Nduaguba S.O., Negoi I., Negru S.M., Neupane Kandel S., Nguyen C.T., Nguyen H.L., Niazi R.K., Nnaji C.A., Noor N.M., Nuñez-Samudio V., Nzoputam C.I., Oancea B., Ochir C., Odukoya O.O., Ogbo F.A., Olagunju A.T., Olakunde B.O., Omar E., Omar Bali A., Omonisi A.E., Ong S., Onwujekwe O.E., Orru H., Ortega-Altamirano D.V., Otstavnov N., Otstavnov S.S., Owolabi M.O., P A M., Padubidri J.R., Pakshir K., Pana A., Panagiotakos D., Panda-Jonas S., Pardhan S., Park E.C., Park E.K., Pashazadeh Kan F., Patel H.K., Patel J.R., Pati S., Pattanshetty S.M., Paudel U., Pereira D.M., Pereira R.B., Perianayagam A., Pillay J.D., Pirouzpanah S., Pishgar F., Podder I., Postma M.J., Pourjafar H., Prashant A., Preotescu L., Rabiee M., Rabiee N., Radfar A., Radhakrishnan R.A., Radhakrishnan V., Rafiee A., Rahim F., Rahimzadeh S., Rahman M., Rahman M.A., Rahmani A.M., Rajai N., Rajesh A., Rakovac I., Ram P., Ramezanzadeh K., Ranabhat K., Ranasinghe P., Rao C.R., Rao S.J., Rawassizadeh R., Razeghinia M.S., Renzaho A.M., Rezaei N., Rezaei N., Rezapour A., Roberts T.J., Rodriguez J.A., Rohloff P., Romoli M., Ronfani L., Roshandel G., Rwegerera G.M., S M., Sabour S., Saddik B., Saeed U., Sahebkar A., Sahoo H., Salehi S., Salem M.R., Salimzadeh H., Samaei M., Samy A.M., Sanabria J., Sankararaman S., Santric-Milicevic M.M., Sardiwalla Y., Sarveazad A., Sathian B., Sawhney M., Saylan M., Schneider I.J., Sekerija M., Seylani A., Shafaat O., Shaghaghi Z., Shaikh M.A., Shamsoddin E., Shannawaz M., Sharma R., Sheikh A., Sheikhbahaei S., Shetty A., Shetty J.K., Shetty P.H., Shibuya K., Shirkoohi R., Shivakumar K.M., Shivarov V., Siabani S., Siddappa Malleshappa S.K., Silva D.A., Singh J.A., Sintayehu Y., Skryabin V.Y., Skryabina A.A., Soeberg M.J., Sofi-Mahmudi A., Sotoudeh H., Steiropoulos P., Straif K., Subedi R., Sufiyan M.B., Sultan I., Sultana S., Sur D., Szerencsés V., Szócska M., Tabarés-Seisdedos R., Tabuchi T., Tadbiri H., Taherkhani A., Takahashi K., Talaat I.M., Tan K.K., Tat V.Y., Tedla B.A., Tefera Y.G., Tehrani-Banihashemi A., Temsah M.H., Tesfay F.H., Tessema G.A., Thapar R., Thavamani A., Thoguluva Chandrasekar V., Thomas N., Tohidinik H.R., Touvier M., Tovani-Palone M.R., Traini E., Tran B.X., Tran K.B., Tran M.T., Tripathy J.P., Tusa B.S., Ullah I., Ullah S., Umapathi K.K., Unnikrishnan B., Upadhyay E., Vacante M., Vaezi M., Valadan Tahbaz S., Velazquez D.Z., Veroux M., Violante F.S., Vlassov V., Vo B., Volovici V., Vu G.T., Waheed Y., Wamai R.G., Ward P., Wen Y.F., Westerman R., Winkler A.S., Yadav L., Yahyazadeh Jabbari S.H., Yang L., Yaya S., Yazie T.S., Yeshaw Y., Yonemoto N., Younis M.Z., Yousefi Z., Yu C., Yuce D., Yunusa I., Zadnik V., Zare F., Zastrozhin M.S., Zastrozhina A., Zhang J., Zhong C., Zhou L., Zhu C., Ziapour A., Zimmermann I.R., Fitzmaurice C., Murray C.J, & Force L.M. (2021). Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncology, 8(3), 420-444.
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Publication 2021
Age Groups Autopsy Breast Diagnosis Dietary Supplements Disabled Persons Laryngeal Cancer Laryngectomy Larynx Malignant Neoplasms Patients Prostate Cancer sequels Staging, Cancer Survivors Urinary Bladder
4
Evaluating CPIB Across Acquired Disorders
Cited 7 times
Participants representing four medical diagnoses commonly associated with communication disorders were recruited to evaluate the function of the CPIB across different disorder groups. The four groups were multiple sclerosis (MS), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and head and neck cancer (HNCA; oral, oral-pharyngeal or laryngeal cancer, and including those treated with laryngectomy). These groups were chosen for several reasons. First, they are adult-onset conditions, and therefore participants have experienced living as ‘typical’ communicators before the onset of the condition. That provides a perspective of change from which participants can evaluate the impact of the health condition on communicative participation. Because the impact of health conditions on communicative participation has not been directly compared between groups with acquired versus congenital conditions, the decision was made in this study to focus on acquired conditions. Second, the communication disorders associated with these conditions are largely motor speech and voice disorders. In this study, the goal was to target groups who were more likely to retain relatively strong language and cognitive skills because the sole method of data collection was self-report without the presence of a researcher to assist participants with the questionnaires. Finally, these four groups represent different speech and voice disorder characteristics with different trajectories (e.g., stable, slowly degenerative, more rapidly degenerative). This diversity was desired to examine the function of the CPIB items in varying populations.
Baylor C., Yorkston K., Eadie T., Kim J., Chung H, & Amtmann D. (2013). The Communicative Participation Item Bank (CPIB): Item bank calibration and development of a disorder-generic short form. Journal of speech, language, and hearing research : JSLHR, 56(4), 1190-1208.
Publication 2013
Adult Amyotrophic Lateral Sclerosis associated conditions Cancer of Head and Neck Cognition Communicative Disorders Congenital Disorders Diagnosis Laryngeal Cancer Laryngectomy Multiple Sclerosis Oropharynxs Population Group Speech Voice Disorders
5
Modeling Cancer Burden Estimation
Cited 5 times
In GBD 2019, the initial step in the process of estimating the burden of cancer was modelling cause-specific mortality. Mortality data from multiple sources, including vital registries and verbal autopsies, were extracted. Because of scarce mortality data for some locations and time points, mortality measures were also estimated from the cancer registry incidence data with separately modelled mortality-to-incidence ratios (MIRs). The codes corresponding to cancers in the GBD cause hierarchy were taken from the International Classification of Diseases (ICD)-9 and ICD-10 codebooks and mapped to the GBD cause list for each cancer (appendix 1 p 28). The mortality estimates were then used as inputs for a Cause of Death Ensemble model (CODEm), which predicts single-cause mortality based on the available data and covariates with a causal relationship.11 (link), 12 (link) Additionally, to ensure that all single-cause mortality estimates matched the separately modelled all-cause mortality estimates, CoDCorrect was used to scale single-cause mortality estimates to all-cause mortality estimates.1 (link) The incidence of each cancer was calculated by dividing the cause-specific mortality estimates by the MIRs.
The survival of each cancer was modelled on the basis of MIR estimates for each location, year, sex, and age. The yearly prevalence of the population that did not survive beyond 10 years was divided into four sequelae corresponding to phases of the disease—diagnosis and primary therapy, the controlled phase, the metastatic phase, and the terminal phase—while the yearly prevalence of the population that survived beyond 10 years was only divided into the first and second phases. Disability weights associated with each of these four phases were multiplied by the sequelae prevalence to obtain the years lived with disability (YLDs). For larynx cancer, additional disability due to laryngectomy was also calculated using hospital data to estimate the proportion of the population with larynx cancer that underwent a laryngectomy. The hospital data sources and related ICD codes are described in appendix 1 (p 31). The years of life lost (YLLs) associated with each cancer were calculated by multiplying the number of deaths by age using a standard life expectancy at that age.1 (link) Disability-adjusted life-years (DALYs) were calculated by summing the YLDs and YLLs.1 (link)
Global, regional, and national burden of respiratory tract cancers and associated risk factors from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. (2021). The Lancet. Respiratory Medicine, 9(9), 1030-1049.
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Publication 2021
Autopsy Diagnosis Disabled Persons Laryngeal Cancer Laryngectomy Malignant Neoplasms sequels Therapeutics

Most recents protocols related to «Laryngeal Cancer»

1
Laryngeal Carcinoma Surgical Outcomes
The inclusion criteria are: the patients diagnosed with primary laryngeal carcinoma (ICD-10 number: C32.0-C32.9) by microscopic confirmation; the histopathological types revealed only squamous cell neoplasms (Histologic Type ICD-O-3: 8050-8089); the year of diagnosis was between 2004 and 2015; the patients who underwent surgical treatment with or without radiotherapy or chemotherapy. Based on the above inclusion criteria, the exclusion criteria are: the patients with not first primary tumor in the larynx; the patients had developed metastasis by the time of diagnosis; the patients who had missing survival data or survival time less than 3 months.
Sun H., Wu S., Li S, & Jiang X. (2023). Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database. Medicine, 102(10), e33144.
Publication 2023
Diagnosis Laryngeal Cancer Laryngeal Neoplasm Microscopy Neoplasm Metastasis Operative Surgical Procedures Patients Pharmacotherapy Radiotherapy Squamous Cell Neoplasms
2
Laryngeal Carcinoma: Clinical and Imaging Patterns
A retrospective institutional based cross sectional study design was employed to assess the clinical and imaging pattern of laryngeal carcinoma. The study was conducted at TASH from January 2016 to July 2019. TASH is the largest referral hospital as well as the largest radiotherapy center. The source populations were all patients with laryngeal carcinoma who were referred to TASH during the study period. The study population was all patients who have cross-sectional imaging and pathology results being evaluate in the study period.
Patients whose medical records were irretrievable or incomplete were excluded from the study. The medical records of 92 patients were reviewed, 2 were excluded leaving 90 patients. The patient's demography, history, physical examination, CT and histopathology reports and laryngoscope exam were reviewed and filled on a structured questionnaire.
Data analysis: Data entering, coding and clearing for the quantitative data was performed using Microsoft excel and the analysis was performed with SPSS version 23. The socio-demographic and clinical characteristics of participants were computed by using simple descriptive statistics (mean, percentage, frequencies). Means and ranges were calculated from continuous variables. The agreement between imaging and laryngoscope examination was analyzed using cross-tabulation.
Ethical consideration: Ethical clearance was obtained from the ethics committee of the department of radiology before the commencement of the study.
Aliye I.A, & Nour A.S. (2023). Laryngeal Cancer Imaging Patterns and Agreement with Laryngoscopy Findings at Tikur Anbessa Specialized Hospital: A Retrospective Study. Ethiopian Journal of Health Sciences, 33(1), 91-96.
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Publication 2023
Ethics Committees Laryngeal Cancer Laryngoscopes Patients Physical Examination Radiotherapy X-Rays, Diagnostic
3
Epidemiology of Head and Neck Cancer in Taiwan
Head and neck cancer cases between 1980 and 2019 were obtained from the national Taiwan Cancer Registration (TCR) database (https://twcr.tw/). The Taiwan Cancer Registration (TCR) database has collected newly diagnosed cancer cases from hospitals with 50 or more beds in Taiwan since 1979 [9 (link), 10 (link)]. In addition, the completeness of the TCR, the percentage of cases with death certificates, and the percentage of morphological verification in 2016 were respectively 98.4, 0.9, and 93%, while the completeness was measured by all registered cancer cases divided by all potential cancer cases from profiles of death certificate, NHI catastrophic illnesses, and four major cancer screening programs [9 (link)]. All cases in this analysis were classified based on the International Classification of Diseases for Oncology, third edition (ICD-O-03) [11 ]. Head and neck cancer cases were categorized into oral cancer (C00, C02, C03, C04, C050, C058, C059, and C06, excluding C024), oropharyngeal cancer (C01, C024, C051, C052, C09, C10, C142, and C148), hypopharyngeal cancer (C12, C13, and C140), and laryngeal cancer (C32).
Based on the 2000 World Health Organization standard population, the age-adjusted incidence rate in men from 1980 to 2019 was only analyzed due to the low incidence in women in Taiwan. For the analysis of long-term trends, the age-specific incidence rate from 1980 to 2019 was calculated for specific age groups, time periods, and birth cohorts. The age-specific incidence rate was classified into eighteen 5-year age groups (0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, and 85+) and eight 5-year time periods (1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, and 2015–2019). In addition, the birth cohort was divided into eleven birth groups (1930–1934, 1935–1939, 1940–1944, 1945–1949, 1950–1954, 1955–1959, 1960–1964, 1965–1969, 1970–1974, 1975–1979, and 1980–1984) and twelve 5-year age groups (30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, and 85+). Moreover, to describe the linear change in the age-adjusted incidence rate from 1980 to 2019, a join point regression model was utilized to detect the change point and calculate the average annual percent change (AAPC) and annual percent change (APC) [10 (link)]. In addition, the 95% confidence intervals of the average annual percent change (AAPC) and annual percent change (APC) were analyzed. 95% confidence interval indicated 95% would fall between the upper limit and the lower limit, while 95% confidence interval including 0 showed statistically nonsignificant. The research protocol was approved by the Institutional Review Board of Fu-Jen Catholic University (No. C104014).
Tsai Y.S., Chen Y.C., Chen T.I., Lee Y.K., Chiang C.J., You S.L., Hsu W.L, & Liao L.J. (2023). Incidence trends of oral cavity, oropharyngeal, hypopharyngeal and laryngeal cancers among males in Taiwan, 1980–2019: a population-based cancer registry study. BMC Cancer, 23, 213.
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Publication 2023
Age Groups Birth Cohort Cancer of Head and Neck Cancer of Mouth Catastrophic Illness Childbirth Ethics Committees, Research Hypopharyngeal Cancer Laryngeal Cancer Malignant Neoplasms Neoplasms Oropharyngeal Cancer Roman Catholics Woman
4
Laryngeal Cancer Genetic Risk Factors
All clinical and pathological parameters were collected by chart review. The criteria for inclusion in the LC group were as follows: 1. histopathological diagnosis of laryngeal squamous cell carcinoma; 2. regular smoker; and 3. data on clinical parameters [e.g. tumour (T), node (N), metastasis (M) classification, and location of tumour, according to the AJCC Cancer Staging Manual, 8th Edition] 10 . The criteria for inclusion in the control group were as follows: 1) no history of malignancy; 2) regular smoker; 3) undergoing septoplasty surgery at the otolaryngology clinic; 4) a normal laryngeal examination prior to surgery. In both groups, patients with a history of use of ACE inhibitors and/or AT receptor blockers and patients with genetic diseases involving predisposition to malignancy were excluded. Descriptive information was collected on the age, gender, tumour localisation, TNM classification and stage. The frequency of II, ID and DD genotypes and I/D alleles of the ACE gene in patients diagnosed with LC were compared with the control group. In addition, the clinical findings of patients diagnosed with LC were analysed in terms of genotype and allele frequency. For genetic analysis, 4 cc venous blood was taken from all patients in the study group into tubes containing EDTA during routine preoperative examinations. Samples were stored at -20°C in the medical genetics laboratory until DNA isolation.
Kumbul Y.Ç., Öztürk K.H., Yasan H., Akın V., Sivrice M.E, & Caner F. (2023). Angiotensin-converting enzyme insertion/deletion gene polymorphism in patients with laryngeal cancer. Acta Otorhinolaryngologica Italica, 43(1), 26-31.
Publication 2023
Alleles Angiotensin-Converting Enzyme Inhibitors Edetic Acid Gender Genes Genotype Hereditary Diseases isolation Laryngeal Cancer Larynx Malignant Neoplasms Neoplasm Metastasis Neoplasms Neoplasms by Site Operative Surgical Procedures Otorhinolaryngologic Surgical Procedures Patients Physical Examination Signs and Symptoms Squamous Epithelial Cells Susceptibility, Disease Veins
5
Cytotoxicity Evaluation of Cliona Extracts
Cliona sp. extract, and CH, CD, and CE fractions, were evaluated for their cytotoxic activity in two cell lines against HEB-2 and HCT-116 (Human larynx carcinoma and Colon carcinoma) using MTT assay as explained by [19 (link),74 (link),75 (link)]. Vinblastine sulphate and DMSO (Dimethyl sulfoxide) were used as positive and negative controls, respectively. The tested cell lines were obtained from VACSERA Tissue Culture Unit Giza, Egypt. DMEM (Dulbecco’s Modified Eagle’s Medium) was used for the tested cells’ propagation. Stock solutions of the extract and fractions were prepared in 10% DMSO in ddH2O. The cytotoxicity was determined using the MTT assay as described by [19 (link),75 (link)].
In brief, cells were seeded in 96-well plates (100 µL/well at a density of 1 × 104 cells/mL) and incubated in 5% CO2 at 37 °C for 24 h. Cells were treated in triplicate with various concentrations of the tested extract and fractions after 24 h. The viable cell yield was determined by a colorimetric method as follows: after additional 24 h, the supernatant was removed and a crystal violet solution (1%) was added to each well for at least 30 min. Then, the stain was removed and the plates were washed using tap water until all the excess stains were removed. Next, 30% Glacial acetic acid was added to the whole wells and mixed. The absorbance of the plates was measured after gently shaking the Microplate reader (TECAN, Inc., Morrisville, NC, USA), using a test wavelength of 490 nm. Compared with the untreated cells, the optical density was measured with the microplate reader (SunRise, TECAN, Inc, Morrisville, NC, USA) to determine the viable cell numbers. The following equation was used to determine the percentage of viability.

where ODt is the mean optical density of wells treated with the tested sample and ODc is the mean optical density of the untreated cells. The relation between the surviving cells and drug concentration was plotted to obtain the survival curve of each tumor cell line after treatment with the specified extract or fraction. The concentration required to cause toxic effects in 50% of the intact cells (IC50) was estimated from the graphic plots of the dose response curve for each concentration using the Graphpad Prism 5 software (Graphpad software, San Diego, CA, USA).
Hassan W.H., El Sayed Z.I., Al-Wahaibi L.H., Abdel-Aal M.M., Abdel-Mageed W.M., Abdelsalam E, & Abdelaziz S. (2023). Metabolites Profiling and In Vitro Biological Characterization of Different Fractions of Cliona sp. Marine Sponge from the Red Sea Egypt. Molecules, 28(4), 1643.
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Publication 2023
Acetic Acid Aftercare Biological Assay Cancer of Colon Cell Line, Tumor Cell Lines Cells Colorimetry Cytotoxin Eagle Genetic Testing Homo sapiens Laryngeal Cancer Pharmaceutical Preparations prisma Staining Stains Sulfoxide, Dimethyl Tissues Vinblastine Sulfate Violet, Gentian Vision

Top products related to «Laryngeal Cancer»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
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DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.
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Hep 2 by American Type Culture Collection
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The Hep-2 is a cell line derived from human epithelial cells. It is used for the detection and identification of antinuclear antibodies (ANA) in patient samples. The Hep-2 cell line provides a standardized substrate for ANA testing, which is an important diagnostic tool for various autoimmune diseases.
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Rpmi 1640 by Thermo Fisher Scientific
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RPMI 1640 is a common cell culture medium used for the in vitro cultivation of a variety of cells, including human and animal cells. It provides a balanced salt solution and a source of essential nutrients and growth factors to support cell growth and proliferation.
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Fetal bovine serum (fbs) by Merck Group
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FBS, or Fetal Bovine Serum, is a commonly used cell culture supplement. It is derived from the blood of bovine fetuses and provides essential growth factors, hormones, and other nutrients to support the growth and proliferation of a wide range of cell types in vitro.
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Hep 2 by Thermo Fisher Scientific
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The Hep-2 is a laboratory equipment used for the detection and identification of antinuclear antibodies (ANAs) in human serum samples. It is a cell-based indirect immunofluorescence assay (IFA) system that utilizes Hep-2 cells as the substrate. The Hep-2 system provides a standardized method for the screening and semi-quantitative analysis of ANAs, which are associated with various autoimmune disorders.
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Fetal bovine serum (fbs) by Sartorius
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The FBS (Fetal Bovine Serum) is a cell culture media supplement used to promote the growth and proliferation of cells in vitro. It is a complex mixture of proteins, growth factors, and other components derived from the blood of fetal bovine. The FBS provides essential nutrients and growth factors that support the survival and expansion of various cell types in cell culture applications.
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Penicillin by Thermo Fisher Scientific
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.

More about "Laryngeal Cancer"

Laryngeal Cancer, also known as throat cancer or voice box cancer, is a type of cancer that develops in the larynx, the part of the throat that contains the vocal cords.
This form of cancer can affect the ability to speak, swallow, and breathe, making it a significant health concern.
Researchers can explore the AI-driven platform of PubCompare.ai to study Laryngeal Cancer in depth.
This platform allows users to discover protocols from scientific literature, preprints, and patents, and use smart comparisons to identify the most effective protocols and products.
By utilizing this tool, researchers can enhance the reproducibility and accuracy of their studies on Laryngeal Cancer.
The larynx, or voice box, is a complex structure located in the throat that plays a crucial role in speech, swallowing, and breathing.
Laryngeal Cancer can develop in different parts of the larynx, including the glottis (the area containing the vocal cords), the supraglottis (the area above the vocal cords), and the subglottis (the area below the vocal cords).
Subtypes of Laryngeal Cancer include squamous cell carcinoma, which is the most common type, as well as adenocarcinoma, neuroendocrine tumors, and sarcomas.
Factors that may increase the risk of developing Laryngeal Cancer include smoking, excessive alcohol consumption, human papillomavirus (HPV) infection, and exposure to certain chemicals or airborne irritants.
To study Laryngeal Cancer, researchers may utilize cell lines such as Hep-2, which is a human laryngeal carcinoma cell line, and culture media like RPMI 1640 and DMEM.
These cell lines and culture media can provide valuable insights into the biology and behavior of Laryngeal Cancer cells.
Additionally, researchers may use antibiotics like Penicillin and Streptomycin to maintain cell cultures and prevent bacterial contamination.
By exploring the AI-driven platform of PubCompare.ai, researchers can access a wealth of information and resources to advance their understanding of Laryngeal Cancer.
This can lead to the development of more effective diagnostic tools, treatment strategies, and ultimately, improved outcomes for patients affected by this form of cancer.