Longitudinal study to assess impact of smoking at diagnosis and quitting on 1-year survival for people with non-small cell lung cancer

Gemine, Rachel; Ghosal, Robin; Collier, Gareth; Parry, D. H.; Campbell, Ian; Davies, G.; Davies, K. E.

doi:10.1016/j.lungcan.2018.12.028

Cited by 42 publications

(25 citation statements)

References 24 publications

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“…Data suggest that there is a benefit to smoking cessation even after a lung cancer diagnosis . Continuing to smoke can adversely affect survival, thus highlighting the importance of patient and clinician discussions regarding smoking status and cessation resources after a lung cancer diagnosis …”

Section: Selected Cancersmentioning

confidence: 99%

Cancer treatment and survivorship statistics, 2019

Miller

Nogueira

Mariotto

et al. 2019

CA A Cancer J Clinicians

3,702

2,656

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effects are also briefly described. More than 16.9 million Americans (8.1 million males and 8.8 million females) with a history of cancer were alive on January 1, 2019; this number is projected to reach more than 22.1 million by January 1, 2030 based on the growth and aging of the population alone. The 3 most prevalent cancers in 2019 are prostate (3,650,030), colon and rectum (776,120), and melanoma of the skin (684,470) among males, and breast (3,861,520), uterine corpus (807,860), and colon and rectum (768,650) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost two-thirds (64%) are aged 65 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by follow-up care providers. Although there are growing numbers of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care.

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Section: Selected Cancersmentioning

confidence: 99%

Cancer treatment and survivorship statistics, 2019

Miller

Nogueira

Mariotto

et al. 2019

CA A Cancer J Clinicians

3,702

2,656

View full text Add to dashboard Cite

show abstract

“…1 It has been estimated that two-thirds of patients who smoke continue to do so after cancer diagnosis. 2 , 3 This is alarming, since there is accumulating evidence that persistent smoking after cancer diagnosis impairs cancer-specific survival in multiple cancer types, 4 including small-cell and non-small-cell lung cancer (SCLC and NSCLC, respectively), 5 , 6 prostate, 7 , 8 head and neck, 9 , 10 colorectal, 11 , 12 and bladder cancer. 13 In breast cancer, conflicting results are reported, but large meta-analyses have demonstrated that persistent smoking is associated with poorer breast cancer-specific survival, especially in heavy smokers.…”

Section: Introductionmentioning

confidence: 99%

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit

et al. 2021

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Background Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the 2020 Surgeon General's report, smoking cessation may reduce CM but supporting evidence is not wide. Use of deep learning-based modeling that enables universal natural language processing of medical narratives to acquire population-based real-life smoking data may help overcome the challenge. We assessed the effect of smoking status and within-1-year smoking cessation on CM by an in-house adapted freely available language processing algorithm. Materials and methods This cross-sectional real-world study included 29 823 patients diagnosed with cancer in 2009-2018 in Southwest Finland. The medical narrative, International Classification of Diseases-10th edition codes, histology, cancer treatment records, and death certificates were combined. Over 162 000 sentences describing tobacco smoking behavior were analyzed with ULMFiT and BERT algorithms. Results The language model classified the smoking status of 23 031 patients. Recent quitters had reduced CM [hazard ratio (HR) 0.80 (0.74-0.87)] and OM [HR 0.78 (0.72-0.84)] compared to persistent smokers. Compared to never smokers, persistent smokers had increased CM in head and neck, gastro-esophageal, pancreatic, lung, prostate, and breast cancer and Hodgkin's lymphoma, irrespective of age, comorbidities, performance status, or presence of metastatic disease. Increased CM was also observed in smokers with colorectal cancer, men with melanoma or bladder cancer, and lymphoid and myeloid leukemia, but no longer independently of the abovementioned covariates. Specificity and sensitivity were 96%/96%, 98%/68%, and 88%/99% for never, former, and current smokers, respectively, being essentially the same with both models. Conclusions Deep learning can be used to classify large amounts of smoking data from the medical narrative with good accuracy. The results highlight the detrimental effects of persistent smoking in oncologic patients and emphasize that smoking cessation should always be an essential element of patient counseling.

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“…The literature confirms that lung cancer among non-smokers exhibits distinctive clinical characteristics, is more common in women and is diagnosed at later stages and that adenocarcinoma is the predominant histological type [ 31 , 32 , 33 ]. Meanwhile, tobacco smoking is associated with squamous and small cell types of lung cancer, as well as with an earlier age at the time of diagnosis [ 34 ], which leads to a generally poorer prognosis for patients in this group.…”

Section: Discussionmentioning

confidence: 80%

Features of the Metabolic Profile of Saliva in Lung Cancer and COPD: The Effect of Smoking Status

et al. 2021

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The aim of the study was to compare the metabolic characteristics of the salivary composition in lung cancer, chronic obstructive pulmonary disease (COPD) and their combination, depending on the smoking history. The study group included 392 patients with lung cancer of various histological types. The division into subgroups was carried out in accordance with the severity of COPD and smoking experience. Salivary biochemical composition was determined according to 34 indicators. For data processing, the principal component method was used. Different groups of biochemical saliva markers are informative when separately accounting for the smoking factor and the presence of COPD in lung cancer. For smoking, antioxidant enzymes and electrolyte components of saliva are informative; for COPD metabolic enzymes, lipid peroxidation products, sialic acids and electrolyte components are informative. While taking into account the smoking factor and the presence of COPD, biochemical markers corresponding to the presence/absence and severity of COPD are the priority. Changes occurring in the background of smoking are of a secondary nature, manifesting as much as possible with a smoking history of more than 50 pack-years. Thus, the metabolic changes that occur in lung cancer in combination with COPD, depending on the smoking factor, can be estimated using saliva.

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Longitudinal study to assess impact of smoking at diagnosis and quitting on 1-year survival for people with non-small cell lung cancer

Cited by 42 publications

References 24 publications

Cancer treatment and survivorship statistics, 2019

Cancer treatment and survivorship statistics, 2019

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit

Features of the Metabolic Profile of Saliva in Lung Cancer and COPD: The Effect of Smoking Status

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