Differences in clinical presentation of non-small cell lung cancer in never-smokers versus smokers

Sociodemographic and clinical characteristics associated with never-smoking status in patients with lung cancer: findings from a large integrated health system

Background

Evidence is limited characterizing sociodemographically diverse patient populations with lung cancer in relation to smoking status.

Methods

In a cross-sectional analysis of adults diagnosed with lung cancer at ages ≥30 years from 2007-2018 within an integrated healthcare system, overall and sex-specific prevalence of never smoking were estimated according to sociodemographic and clinical characteristics. Adjusted prevalence ratio (aPR) and 95% confidence interval (CI) were also estimated using modified Poisson regression to identify patient characteristics associated with never smoking, overall and by sex. Similar analyses were conducted to explore whether prevalence and association patterns differed between non-Hispanic White and Asian/Pacific Islander patients.

Results

Among 17,939 patients with lung cancer, 2,780 (15.5%) never smoked and 8,698 (48.5%) had adenocarcinoma. Overall prevalence of never smoking was higher among females than males (21.2% vs. 9.2%, aPR 2.13, 95% CI: 1.98-2.29); Asian/Pacific Islander (aPR 2.85, 95% CI: 2.65-3.07) and Hispanic (aPR 1.72, 95% CI: 1.51-1.95) than non-Hispanic White patients; patients who primarily spoke Spanish (aPR 1.60, 95% CI: 1.32-1.94), any Asian language (aPR 1.20, 95% CI: 1.10-1.30), or other languages (aPR 1.84, 95% CI: 1.27-2.65) than English; patients living in the least vs. most deprived neighborhoods (aPR 1.36, 95% CI: 1.24-1.50); and patients with adenocarcinoma (aPR 2.57, 95% CI: 2.18-3.03), other non-small cell lung cancer (NSCLC) (aPR 2.00, 95% CI: 1.63-2.45), or carcinoid (aPR 3.60, 95% CI: 2.96-4.37) than squamous cell carcinoma tumors. Patterns of never smoking associated with sociodemographic, but not clinical factors, differed by sex. The higher prevalence of never smoking associated with Asian/Pacific Islander race/ethnicity was more evident among females (aPR 3.30, 95% CI: 2.95-3.47) than males (aPR 2.25, 95% CI: 1.92-2.63), whereas the higher prevalence of never smoking associated with living in the least deprived neighborhoods was more evident among males (aPR 1.93, 95% CI: 1.56-2.38) than females (aPR 1.18, 95% CI: 1.06-1.31). Associations between primary language and never-smoking status were found only among females. Overall and sex-specific prevalence and association patterns differed between Asian/Pacific Islander and non-Hispanic white patients.

Conclusions

Our findings suggest that patterns of never-smoking status associated with sociodemographic and clinical characteristics are different across sex and race/ethnicity among patients with lung cancer. Such data are critical to increasing awareness and expediting diagnosis of this disease.

A qualitative study of healthcare-related experiences of non-smoking women with lung cancer

PURPOSE

Lung cancer in non-smoking women is a distinct entity, but few studies have examined these patients' healthcare-related experiences.

METHODS

Women with lung cancer and with no smoking history underwent a face-to-face semi-structured, audio-recorded interview that was analyzed with a qualitative inductive approach.

RESULTS

Twenty-three patients were interviewed, and three themes emerged. The first theme centered on a delay in cancer diagnosis. One patient described, "The whole initial diagnostic process just fills me with rage… I didn't actually get my Tarceva® until the last week in April." Second, the diagnosis of lung cancer seemed especially challenging in view of patients' non-smoking history and otherwise good health; these factors seem to have contributed to the diagnostic delay. One patient explained, "Well, I was just so adamant that I didn't like smoking… maybe if I had been a smoker, they [the healthcare providers] would've been more resourceful." Finally, the stigma of a smoking-induced malignancy was clearly articulated, "Yeah. Because it's a stigma, and I had read that, too -- people go, 'Well, it's your own damn fault because you were a smoker.'" CONCLUSIONS: Non-smoking women with lung cancer appear to endure a long trajectory from symptoms to cancer diagnosis to the initiation of cancer therapy. An awareness and acknowledgement of this long trajectory might help healthcare providers render more compassionate cancer care to these patients.

Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

Background

Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information.

Methods

We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models.

Results

Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models.

Conclusion

In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.