Cigarette Smoking and the Risks of Basal Cell Carcinoma and Squamous Cell Carcinoma

Dusingize, Jean Claude; Olsen, Catherine M.; Pandeya, Nirmala; Subramaniam, Padmini; Thompson, Bridie S.; Neale, Rachel E.; Green, Adèle C.; Whiteman, David C.

doi:10.1016/j.jid.2017.03.027

Cited by 62 publications

(55 citation statements)

References 28 publications

(31 reference statements)

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“…In addition, there was also some evidence showing that smoking might be a risk factor for SCC rather than BCC. 32,33 Our stratified analysis by geographic region indicated that use of diuretics was significantly associated with increased risk of SCC in are required to confirm our findings.…”

Section: Discussionsupporting

confidence: 50%

“…Although smoking is a well‐known risk factor for many human cancers, findings regarding potential associations between smoking and KC risk remain inconsistent . The magnitude of association between diuretics and BCC was similar between studies without controlling for smoking status and studies controlling for smoking status.…”

Section: Discussionmentioning

confidence: 99%

“…This attenuation suggested that smoking status might be a confounder for SCC association. In addition, there was also some evidence showing that smoking might be a risk factor for SCC rather than BCC …”

Section: Discussionmentioning

confidence: 99%

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Use of antihypertensive drugs and risk of keratinocyte carcinoma: A meta‐analysis of observational studies

Tang

Zhai

et al. 2018

Pharmacoepidemiology and Drug

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Section: Discussionsupporting

confidence: 50%

Section: Discussionmentioning

confidence: 99%

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Use of antihypertensive drugs and risk of keratinocyte carcinoma: A meta‐analysis of observational studies

Tang

Zhai

et al. 2018

Pharmacoepidemiology and Drug

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“…The key question is whether the observed inverse association reflects a "protective effect" of smoking on development of melanoma, or whether there are alternative non-causal explanations. It is likely that never smokers have more medical surveillance and earlier detection of disease compared with smokers as a consequence of a generally healthier lifestyle [359]. In our dataset, a greater proportion of never smokers than ever smokers self-reported that they had undergone a skin screening examination by a doctor in the 3 years prior to enrolment (75% vs 66% respectively).…”

Section: Discussionmentioning

confidence: 96%

“…Although no cancers of the skin are currently included in this list, there is strengthening evidence that smoking is a cause of cutaneous squamous cell carcinoma (cSCC) [359]. The association between smoking and melanoma remains unclear despite considerable investigation.…”

Section: Introductionmentioning

confidence: 99%

Epidemiologic studies of cancers of the skin

Dusingize¹

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Background Skin cancers are the most common form of cancer occurring in white populations. They account for approximately 40% of all cancer cases globally, with the highest incidence in Australia. The three most common histological types of skin cancer are basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC) and melanoma. The risk of developing skin cancer depends on both environmental and constitutional factors. Exposure to ultraviolet radiation (UVR) is the major environmental risk factor for BCC, cSCC and melanoma, and skin phenotypic characteristics are the major constitutional risk factors. Cigarette smoking, obesity and height have been shown to have an etiologic effect for many cancers in humans, but their associations with skin cancer are unclear. Studies examining the relationships between these factors and skin cancer have been limited by factors such as lack of information on established skin cancer risk factors, inadequate approach to statistical analyses, and lack of exploration of other potential sources of bias, such as detection bias. Aim The overall aim of this thesis was to assess whether cigarette smoking, obesity (estimated by body mass index (BMI)) and height are independent risk factors for BCC, cSCC and melanoma using various epidemiological and statistical techniques. In particular, the research aimed to address some of the limitations arising from previous studies. Methods This research was conducted using data from the QSkin Sun and Health Study (n=43,794), supplemented by linkage to various health databases including the Australian national health insurance scheme (Medicare), pathology laboratories and the Queensland Cancer Registry. Smoking history, BMI and height were self-reported at baseline, with nearly perfect repeatability. Hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between smoking, BMI and height were estimated using multivariable Cox proportional hazards regression, accounting for death as a competing event. viii Acknowledgements Undertaking this PhD journey has been a truly life-changing experience for me and the work presented in this thesis would not have been possible without the support that I received from many people. I would like to express my special appreciation and thanks to my principal supervisor Professor David Whiteman. Thank you for giving me your time and support over the years. Your advice on both research as well as on my career have been invaluable. You have taught me how to think critically, communicate more clearly and concisely and become a better researcher. Thank you for allowing me to pursue various projects without objection.

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Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

Wang

Liang

et al. 2019

JAMA Dermatol

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IMPORTANCE A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction. OBJECTIVE To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information. DESIGN, SETTING, AND PARTICIPANTS This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018. MAIN OUTCOMES AND MEASURES Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models. RESULTS A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867;-2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872;-2.32% loss], hypertension [AUROC, 0.879;-1.53% loss], and chronic kidney insufficiency [AUROC, 0.879;-1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; −2.67% reduction; acarbose AUROC, 0.870; −2.50 reduction; and systemic antifungal agents AUROC, 0.875; −1.99 reduction). CONCLUSIONS AND RELEVANCE The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.

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Cigarette Smoking and the Risks of Basal Cell Carcinoma and Squamous Cell Carcinoma

Cited by 62 publications

References 28 publications

Use of antihypertensive drugs and risk of keratinocyte carcinoma: A meta‐analysis of observational studies

Use of antihypertensive drugs and risk of keratinocyte carcinoma: A meta‐analysis of observational studies

Epidemiologic studies of cancers of the skin

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

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