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Enregistrement W3117385517 · doi:10.1093/jnci/djaa199

Screening for Nasopharyngeal Cancer in High-Risk Populations: A Small Price to Pay for Early Disease Identification?

2020· article· en· W3117385517 sur OpenAlexaff
John R. de Almeida, Scott V. Bratman, Aaron R. Hansen

Notice bibliographique

RevueJNCI Journal of the National Cancer Institute · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueHead and Neck Cancer Studies
Établissements canadiensPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésIdentification (biology)MedicineDiseaseCancerOncologyActuarial scienceInternal medicineBusinessBiology

Résumé

récupéré en direct d'OpenAlex

Screening tests for cancer have dramatically improved survival rates by identifying certain cancers early, before symptom onset. The Pap test for cervical cancer, colonoscopy for colorectal cancer, computed tomography for lung cancer, and mammography for breast cancer have reduced the mortality of these cancers (1‐4). To be considered an effective screening program, the disease must represent a clinically significant problem, the benefits from earlier diagnosis should outweigh the risks of diagnostic procedures, treatment should be available, and screening should not result in overdiagnosis of tumors that otherwise would not affect longevity. Moreover, a new screening program should be economically feasible (5). Indeed, for the aforementioned cancers, screening programs have been shown to be cost-effective (6‐9). In the article accompanying this editorial, Miller et al. (10) examine the cost-effectiveness of screening programs for nasopharyngeal carcinoma (NPC) in high-risk populations. The authors conclude that specific screening programs for NPC may be cost-effective in some high-risk populations. In general, screening programs are most useful in cancers with high incidence rates. The United States Preventative Services Task Force has grade A recommendations for cervical and colorectal cancer screening and Grade B recommendations for lung and breast cancer screening (11). Before the wide-scale adoption of the Pap test, cervical cancer was one of the leading causes of death in women, and the lifetime risk of developing colorectal, lung, and breast cancer is 1 in 23, 1 in 15, and 1 in 8 (for women), respectively (12‐15). By comparison, even in the region with the highest lifetime risk (in Guangdong, China), only 1 in every 56 individuals is affected (10). This lower lifetime risk will result in unnecessary tests due to false-positive screening tests. In 1 study demonstrating the effectiveness of screening for NPC, 1.4% of all screened patients would require unnecessary endoscopy and imaging to rule out a cancer. In a country such as China with a population of 1.4 billion, this would result in an enormous healthcare burden (16). The authors found that serology and plasma or nasopharyngeal Epstein-Barr virus (EBV) DNA polymerase chain reaction were each highly sensitive for NPC and that high specificity was linked with cost-effectiveness. Indeed, the most cost-effective approaches consisted of 2- or 3-step testing. Following a positive screening test, the addition of magnetic resonance imaging to the diagnostic regimen was more costly and produced a modest improvement in sensitivity. The results are reassuring in that a variety of screening approaches have the requisite performance attributes to drive the stage shift necessary for mortality reduction. Just as critical to the success of a cancer-screening program is the willingness of individuals to undergo the testing regimen. Notably, each of the screening regimens is minimally invasive. Phlebotomy, nasopharyngeal swab, and endoscopy are safe and require little resource use. However, some individuals may not be willing to engage in a multi-step process for NPC screening because it may be cumbersome and cause unnecessary anxiety. These unmeasured factors may need to be evaluated in real-world settings. Identification of earlier stage disease has direct implications in reduction of treatment costs. Treatment for NPC is stage dependent with curative treatment options for patients with nonmetastatic disease. Therapy for localized early-stage tumors consists of radiotherapy alone, whereas advanced tumors are treated with concurrent radiation and cisplatin with or without neoadjuvant or adjuvant platinum-based chemotherapy. For incurable metastatic or recurrent NPC, platinum gemcitabine chemotherapy in addition to palliative care is the main treatment option. Additional costs of ancillary and supportive care may be associated with management of side effects of treatment. Chronic toxicity, long-term utilization of medical services, psychological well-being, and work productivity are all difficult to measure and thus are not accurately modelled in any cost-effectiveness analysis. The assumptions used in the study may not easily generalize to low- to middle-income countries. Firstly, costs of testing are based on American costs and utility scores were derived from North American trials (RTOG-0522 and Checkmate 141), neither of which included patients with NPC. Broad-scale adoption in countries with limited spending capacity may be limited by the fact that incremental spending has an opportunity cost. In cost-effectiveness analyses, a declaration of cost-effectiveness assumes that society is willing to spend incrementally for an effective treatment as long as it is below a threshold known as the willingness-to-pay (WTP) threshold. This assumption may not be applicable to low- to middle-income countries where resources are often constrained. The authors use a rule of thumb established by the World Health Organization that WTP thresholds are double the local purchasing power parity–adjusted per capita gross domestic product. However, in low- to middle-income countries, the true WTP that may be affordable is likely between 1% and 51% of the per capita gross domestic product, not 200%, suggesting that these countries cannot afford such incremental expenditure (17). In the base case analysis, the authors explore the cost-effectiveness of a one-time screening strategy. This type of strategy would likely only be effective if the disease was associated with a substantial lead time. In reality, most screening programs are repetitive, and one-time screening programs would miss the vast majority of incident cancers. The authors demonstrate this in Supplementary Tables 21 and 22 (available online) whereby a yearly screening approach would result in a reduction in mortality of roughly 17 cases per 100 000 compared with approximately 1 case per 100 000 with a one-time approach. Although the authors examined repeat screening in sensitivity analyses, given that in the base case analysis the most cost-effective one-time screening strategy (EBV serology followed by nasopharyngeal swab EBV DNA polymerase chain reaction followed by endoscopy) is cost-effective in 14.5% of populations, repeat testing would likely be cost-effective in far fewer populations. Miller et al. (10) nicely modelled the cost-effectiveness of screening programs with the best available data and robust analyses. Ultimately, implementation in a real-world setting would require attention to compliance as well as careful evaluation from local public health authorities of the impact of any of these screening programs, given the local incidence rates and local costs of screening in the context of other competing healthcare priorities while optimizing the frequency of screening to ensure sustainability and affordability. None. Role of the funder: Not applicable. Disclosures: Dr de Almeida has nothing to disclose. Dr Hansen is a consultant for Merck, GSK, Bristol-Myers Squibb, Eisai and has received Research support from Novartis, Bristol-Myers Squibb, Genetech, AstraZeneca/Medimmune, Merck, Karyopharm, GSK, Boehringer-Ingelheim, Pfizer, Roche. Author contributions: Drs JRdeA, SVB, and ARH were involved in conceptualization, writing the original draft and review and editing of the manuscript. Not applicable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,066
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,059
Score d'incertitude au seuil0,198

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,066
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,003
Communication savante0,0050,010
Science ouverte0,0030,002
Intégrité de la recherche0,0140,011
Charge utile insuffisante (le modèle a refusé de juger)0,0590,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,142
Tête enseignante GPT0,393
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentnon

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