Incidence trends for upper aerodigestive tract cancers in rural United States counties

Pagedar, Nitin A.; Kahl, Amanda R; Tasche, Kendall K.; Seaman, Aaron; Christensen, Alan J.; Howren, M. Bryant; Charlton, Mary E.

doi:10.1002/hed.25736

Cited by 30 publications

(30 citation statements)

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“…The incidence rates for HNC and the rate of death among all HNC patients have both declined somewhat in recent years 16,25 . Yet, in a SEER database study, the incidence of oropharynx cancer was shown to be increasing faster in rural areas, while the rates of larynx and oral cavity cancers were decreasing more rapidly in urban areas 26 …”

Section: Hnc Outcomes and The “Rural Disadvantage”mentioning

confidence: 99%

Health‐related quality of life in head and neck cancer survivors: Evaluating the rural disadvantage

Adamowicz

Christensen

Howren

et al. 2021

The Journal of Rural Health

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Purpose Head and neck cancer (HNC) survivors often experience distress and health‐related quality of life (HRQOL) impairment. Research suggests that rural cancer patients may have poorer outcomes than urban patients. This study examined whether HNC patient emotional and HRQOL outcomes differ in those living in a rural versus urban location at 6 and 12 months postdiagnosis. Methods A total of 261 HNC patients were included from a longitudinal study of HNC outcomes. The majority were diagnosed with advanced stage cancer (51.3%); the most common cancer site was oral cavity (41.0%). Rurality was measured using the US Department of Agriculture Rural Urban Commuting Area codes. Depression was measured using the Beck Depression Inventory (BDI), general HRQOL using the Short Form‐36 (SF‐36), and HNC‐specific HRQOL using the Head and Neck Cancer Inventory (HNCI). Analyses were 2 (group) × 3 (assessment) repeated measures ANCOVAs, controlling for demographic and clinical characteristics. Findings Approximately 45% of the sample lived in a rural location. Follow‐up comparisons of significant overall models indicated that rural patients reported significantly more nonsomatic depression symptoms at 6‐month follow‐up. Rural patients were also more likely to report significantly poorer general mental HRQOL at 12‐month follow‐up, significantly poorer HNC‐specific HRQOL related to eating at 6‐ and 12‐month follow‐up, and marginally worse aesthetics at 12‐month follow‐up. Conclusions These findings are consistent with suggestions that rural HNC patients may be at heightened risk for depression symptoms and decrements in HRQOL. Patients should be screened and regularly monitored for issues with depression and HNC‐specific HRQOL throughout the survivorship period.

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Section: Hnc Outcomes and The “Rural Disadvantage”mentioning

confidence: 99%

Health‐related quality of life in head and neck cancer survivors: Evaluating the rural disadvantage

Adamowicz

Christensen

Howren

et al. 2021

The Journal of Rural Health

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“…For a long time, there is no well-established vision-based method for oral cancer detection. The diagnosis of OCSCC has to rely on invasive oral biopsy which is not only time-consuming, but also not guaranteed in primary care or community settings, especially in developing countries [ 9 , 10 ]. Thus, quite often OCSCC patients cannot receive timely diagnosis and referrals [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

et al. 2020

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Background The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers’ clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. Methods We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. Findings 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973–0·991), sensitivity of 94·9% (0·915–0·978), and specificity of 88·7% (0·845–0·926) on the internal validation dataset ( n = 401), and an AUC of 0·935 (0·910–0·957), sensitivity of 89·6% (0·847–0·942) and specificity of 80·6% (0·757–0·853) on the external validation dataset ( n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988–0·999), sensitivity of 97·4% (0·932–1·000) and specificity of 93·5% (0·882–0·979) in detecting early-stage OCSCC. On the clinical validation dataset ( n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902–0·943] vs 92.4% [0·912–0·936]), sensitivity (91·0% [0·879–0·941] vs 91·7% [0·898–0·934]), and specificity (93·5% [0·909–0·960] vs 93·1% [0·914–0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855–0·885], sensitivity of 83·1% [0·807–0·854], and specificity of 90·7% [0·889–0·924]) and the average non-medical student (accuracy of 77·2% [0...

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“…Cumulatively, they are the seventh most common cancer worldwide and the ninth most common cancer in the United States 2 . The vast majority are squamous cell carcinomas (SCC), with major risk factors including alcohol consumption, tobacco usage, human papillomavirus (HPV) infection, male gender, and older age 2,3 . Estimates of 5‐year survival for patients with HNC vary based on prognostic factors that include stage at initial diagnosis and age at diagnosis 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Disparities in survival for patients with HNC have been well documented in literature, and include race, 6–8 socioeconomic status of patient, 8–12 as well as the overall socioeconomic status of the neighborhood in which the patient resides 8,13 . These factors contribute to disparities on multiple levels of the care continuum, associated with altered rates of offering smoking cessation (primary prevention), 3,14 frequency of HNC examinations (secondary prevention), 14 treatment offered and received by patients, and time to treatment initiation 15,16 . Therefore, it is not surprising that these marginalized populations are often presenting with more advanced stage of cancer and may have poorer prognoses 8,16,17 …”

Section: Introductionmentioning

confidence: 99%

Geographic disparities in head and neck cancer survival in Upstate New York 2011–2019

et al. 2021

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Purpose To examine the association between distance to care‐center and urban–rural residence on 5‐year overall survival (OS) from head and neck cancer (HNC). Materials and methods Five‐year OS was retrospectively measured from date of initial diagnosis for patients with HNC treated at a single tertiary care center. Distances were calculated based on ZIP code of patient's residence and care center. Multilevel Weibull regression was used to adjust for confounders and identify disparities in 5‐year all‐cause mortality. Results A total of 670 patients included in study. Multivariable analysis revealed older age or late‐stage cancer at diagnosis, and HPV negative status were associated with poorer OS. Patients residing in isolated small rural town (HR = 2.20, p = 0.015) or small rural town (HR = 2.07, p = 0.015) had lower OS. Distance to care center was not associated with OS (HR = 0.996, p = 0.11). Conclusions Greater rurality was associated with poorer OS among HNC patients in Upstate New York.

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Incidence trends for upper aerodigestive tract cancers in rural United States counties

Cited by 30 publications

References 16 publications

Health‐related quality of life in head and neck cancer survivors: Evaluating the rural disadvantage

Health‐related quality of life in head and neck cancer survivors: Evaluating the rural disadvantage

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

Geographic disparities in head and neck cancer survival in Upstate New York 2011–2019

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