Progress in site-specific cancer mortality in Canada over the last 70 years.

In Canada, the absolute number of cancer deaths has been steadily increasing, however, age-standardized cancer mortality rates peaked decades ago for most cancers. The objective of this study was to estimate the reduction in deaths for each cancer type under the scenario where peak mortality rates had remained stable in Canada. Data for this study were obtained the Global Cancer Observatory and Statistics Canada. We estimated age-standardized mortality rates (ASMR, per 100,000) from 1950 to 2022, standardized to the 2011 Canadian standard population. We identified peak mortality rates and applied the age-specific mortality rates from the peak year to the age-specific Canadian population estimates for subsequent years (up to 2022) to estimate the number of expected deaths. Avoided cancer deaths were the difference between the observed and expected number of cancer deaths. There have been major reductions in deaths among cancers related to tobacco consumption and other modifiable lifestyle habits (417,561 stomach; 218,244 colorectal; 186,553 lung; 66,281 cervix; 32,732 head and neck; 27,713 bladder; 22,464 leukemia; 20,428 pancreas; 8863 kidney; 3876 esophagus; 290 liver). There have been 201,979 deaths avoided for female-specific cancers (breast, cervix, ovary, uterus). Overall, there has been a 34% reduction in mortality for lung cancer among males and a 9% reduction among females. There has been a significant reduction in cancer mortality in Canada since site-specific cancer mortality rates peaked decades ago for many cancers. This shows the exceptional progress made in cancer control in Canada due to substantial improvements in prevention, screening, and treatment. This study highlights priority areas where more attention and investment are needed to achieve progress.

Next Generation Weight Loss Drugs for the Prevention of Cancer?

Background: Western populations are losing the battle over healthy weight management, and excess body weight is a notable cancer risk factor at the population level. There is ongoing interest in pharmacological interventions aimed at promoting weight loss, including GLP-1 receptor agonists (GLP-1RA), which may be a useful tool to stem the rising tide of obesity-related cancers. Purpose: To investigate the potential of next generation weight loss drugs (NGWLD) like GLP-1RA in population-level chemoprevention.Research Design: We used the OncoSim microsimulation tool to estimate the population-level reductions in obesity and the potentially avoidable obesity-related cancers in Canada over the next 25 years.Results: We estimated a total of 71 281 preventable cancers by 2049, with 36 235 and 35 046 cancers prevented for females and males, respectively. Among the 327 254 total projected cancer cases in 2049, 1.3% are estimated to be preventable through intervention with NGWLD.Conclusions: Pharmacologic intervention is not the ideal solution for the obesity-related cancer crisis. However, these agents and subsequent generations provide an additional tool to rapidly reduce body weight and adiposity in populations that have been extremely challenging to reduce weight with standard diet and exercise approaches. Additional research is needed around approaches to prevent initial weight gain and maintain long-term weight loss.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

BACKGROUND: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. METHODS: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. RESULTS: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). CONCLUSIONS: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

The association between neighborhood-level income and cancer stage at diagnosis and survival in Alberta.

BACKGROUND: Socioeconomic status (SES) is associated with a range of health outcomes, including cancer diagnosis and survival. However, the evidence for this association is inconsistent between countries with and without single-payer health care systems. In this study, the relationships between neighborhood-level income, cancer stage at diagnosis, and cancer-specific mortality in Alberta, Canada, were evaluated. METHODS: The Alberta Cancer Registry was used to identify all primary cancer diagnoses between 2010 and 2020. Average neighborhood income was determined by linking the Canadian census to postal codes and was categorized into quintiles on the basis of income distribution in Alberta. Multivariable multinomial logistic regression was used to model the association between income quintile and stage at diagnosis, and the Fine-Gray proportional subdistribution hazards model was used to estimate the association between SES and cancer-specific mortality. RESULTS: Out of the 143,818 patients with cancer included in the study, those in lower income quintiles were significantly more likely to be diagnosed at stage III (odds ratio [OR], 1.07; 95% CI [confidence interval], 1.06-1.09) or IV (OR, 1.12; 95% CI, 1.11-1.14) after adjusting for age and sex. Lower income quintiles also had significantly worse cancer-specific survival for breast, colorectal, liver, lung, non-Hodgkin lymphoma, oral cavity, pancreas, and prostate cancers. CONCLUSIONS: Disparities were observed in cancer outcomes across neighborhood-level income groups in Alberta, which demonstrates that health inequities by SES exist in countries with single-payer health care systems. Further research is needed to better understand the underlying causes and to develop strategies to mitigate these disparities.

Progress in cancer control leads to a substantial number of cancer deaths avoided in Canada.

It is currently not known how many more cancer deaths would have occurred among Canadians if cancer mortality rates were unchanged following various modern human interventions. The objective of this study was to estimate the number of cancer deaths that have been avoided in Canada since the age-standardized overall cancer mortality rate peaked in 1988. We applied the age-specific overall cancer mortality rates from 1988 to the Canadian population for all subsequent years to estimate the number of expected deaths. Avoided cancer deaths were estimated as the difference between the observed and expected number of cancer deaths for each year. Since 1988, there have been 372 584 (standardized mortality ratio = 0.77) and 120 045 (standardized mortality ratio = 0.90) avoided cancer deaths in males and females, respectively (492 629 total). Nearly half a million cancer deaths have been avoided in Canada since the overall cancer mortality rate peaked, which demonstrates the exceptional progress made in modern cancer control in Canada.

Circulating proteome for pulmonary nodule malignancy.

BACKGROUND: Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules. METHODS: Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated. RESULTS: We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001). CONCLUSIONS: Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

Probability of lung cancer in a population excluded from screening due to low PLCOM2012 risk.

BACKGROUND: Several randomized trials demonstrated have reduced lung cancer mortality with screening using computed tomography. However, there remains debate about the optimal approach for determining screening eligibility, and no evidence yet exists reporting lung cancer rates in those excluded from screening due to too low of a personalized risk. METHODS: This study was based on the Alberta Lung Cancer Screening Study, which received 1737 applicants and enrolled 850 based on the NLST criteria or a PLCOM2012 risk ≥ 1.5%. We excluded 887 applicants who were interested in screening but deemed ineligible. We report lung cancer rates in the screened and unscreened cohorts. RESULTS: We observed 30 and 8 lung cancers in the screened and unscreened groups, respectively. Only 1 of 8 lung cancers were among those considered too low risk (0.14%), while the remaining 7 were among those excluded for other reasons, including symptoms requiring more immediate workup. No NLST eligible but PLCO risk < 1.5% screened individual had a lung cancer detected as part of the study, so that of all applicants contacting the program with risk estimates less than 1.5%, only 1/857 (0.12%) developed lung cancer. CONCLUSION: Our findings indicate that a risk-based approach for screening eligibility is unlikely to miss many lung cancers.

Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement.

PURPOSE: Lung cancer screening programs generate a high volume of low-dose computed tomography (LDCT) reports that contain valuable information, typically in a free-text format. High-performance named-entity recognition (NER) models can extract relevant information from these reports automatically for inter-radiologist quality control. METHODS: Using LDCT report data from a longitudinal lung cancer screening program (8,305 reports; 3,124 participants; 2006-2019), we trained a rule-based model and two bidirectional long short-term memory (Bi-LSTM) NER neural network models to detect clinically relevant information from LDCT reports. Model performance was tested using F1 scores and compared with a published open-source radiology NER model (Stanza) in an independent evaluation set of 150 reports. The top performing model was applied to a data set of 6,948 reports for an inter-radiologist quality control assessment. RESULTS: The best performing model, a Bi-LSTM NER recurrent neural network model, had an overall F1 score of 0.950, which outperformed Stanza (F1 score = 0.872) and a rule-based NER model (F1 score = 0.809). Recall (sensitivity) for the best Bi-LSTM model ranged from 0.916 to 0.991 for different entity types; precision (positive predictive value) ranged from 0.892 to 0.997. Test performance remained stable across time periods. There was an average of a 2.86-fold difference in the number of identified entities between the most and the least detailed radiologists. CONCLUSION: We built an open-source Bi-LSTM NER model that outperformed other open-source or rule-based radiology NER models. This model can efficiently extract clinically relevant information from lung cancer screening computerized tomography reports with high accuracy, enabling efficient audit and feedback to improve quality of patient care.

Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program.

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

Lung Cancer Absolute Risk Models for Mortality in an Asian Population using the China Kadoorie Biobank.

BACKGROUND: Lung cancer is the leading cause of cancer mortality globally. Early detection through risk-based screening can markedly improve prognosis. However, most risk models were developed in North American cohorts of smokers, whereas less is known about risk profiles for never-smokers, which represent a growing proportion of lung cancers, particularly in Asian populations. METHODS: Based on the China Kadoorie Biobank, a population-based prospective cohort of 512 639 adults with up to 12 years of follow-up, we built Asian Lung Cancer Absolute Risk Models (ALARM) for lung cancer mortality using flexible parametric survival models, separately for never and ever-smokers, accounting for competing risks of mortality. Model performance was evaluated in a 25% hold-out test set using the time-dependent area under the curve and by comparing model-predicted and observed risks for calibration. RESULTS: Predictors assessed in the never-smoker lung cancer mortality model were demographics, body mass index, lung function, history of emphysema or bronchitis, personal or family history of cancer, passive smoking, and indoor air pollution. The ever-smoker model additionally assessed smoking history. The 5-year areas under the curve in the test set were 0.77 (95% confidence interval = 0.73 to 0.80) and 0.81 (95% confidence interval = 0.79 to 0.84) for ALARM-never-smokers and ALARM-ever smokers, respectively. The maximum 5-year risk for never and ever-smokers was 2.6% and 12.7%, respectively. CONCLUSIONS: This study is among the first to develop risk models specifically for Asian populations separately for never and ever-smokers. Our models accurately identify Asians at high risk of lung cancer death and may identify those with risks exceeding common eligibility thresholds who may benefit from screening.

Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults.

BACKGROUND: There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown. OBJECTIVE: To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites. DESIGN, SETTING, AND PARTICIPANTS: An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020. MAIN OUTCOMES AND MEASURES: Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors. RESULTS: There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients. CONCLUSION: There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

Efficacy and Safety of First-line Systemic Therapy for Metastatic Renal Cell Carcinoma: A Systematic Review and Network Meta-analysis.

CONTEXT: Considerable advances have been made in the first-line treatment of metastatic renal cell carcinoma (mRCC), with immunotherapy-based combinations including immunotherapy-tyrosine kinase inhibitors (IO-TKIs) and dual immunotherapy (IO-IO) favored. A lack of head-to-head clinical trials comparing these treatments means that there is uncertainty regarding their use in clinical practice. OBJECTIVE: To compare and rank the efficacy and safety of first-line systemic treatments for mRCC with a focus on IO-based combinations. EVIDENCE ACQUISITION: MEDLINE (Ovid), EMBASE, Cochrane Library, Web of Science, and abstracts of recent major scientific meetings were searched to identify the most up-to-date phase 3 randomized controlled trials (RCTs) of first-line IO-based combinations for mRCC up to June 2021. A systematic review and network meta-analysis were completed using the Bayesian framework. Primary endpoints included overall survival (OS) and progression-free survival (PFS). Secondary endpoints included the objective response rate (ORR), complete response (CR), grade 3-4 treatment-related adverse events (TRAEs), treatment-related drug discontinuation (TRDD), and health-related quality of life (HRQoL). The analysis was performed for the intention-to-treat (ITT) population as well as by clinical risk group. EVIDENCE SYNTHESIS: A total of six phase 3 RCTs were included involving a total of 5121 patients. Nivolumab plus cabozantinib (NIVO-CABO) had the highest likelihood of an OS benefit in the ITT population (surface under the cumulative ranking curve 82%). Avelumab plus axitinib (AVEL-AXI) had the highest likelihood of an OS benefit for patients with favorable risk (65%). Pembrolizumab plus AXI (PEMBRO-AXI) had the highest likelihood of an OS benefit for patients with intermediate risk (78%). PEMBRO plus lenvatinib (PEMBRO-LENV) had the highest likelihood of an OS benefit for patients with poor risk (89%). PEMBRO-LENV was associated with a superior PFS benefit across all risk groups (89-98%). Maximal ORR was achieved with PEMBRO-LENV (97%). The highest likelihood for CR was attained with NIVO plus ipilimumab (NIVO-IPI; 85%) and PEMBRO-LENV (83%). The highest grade 3-4 TRAE rate occurred with PEMBRO-LENV (95%) and NIVO-CABO (83%), but the latter was associated with the lowest TRDD rate (2%). By contrast, NIVO-IPI had the lowest grade 3-4 TRAE rate (6%) and the highest likelihood of TRDD (100%). HRQoL consistently favored NIVO-CABO (66-75%), PEMBRO-LENV (44-85%), and NIVO-IPI (65-93%) in comparison to the other treatments. CONCLUSIONS: IO-TKI drug combinations are associated with consistent improvements in clinically relevant outcomes for all mRCC risk groups. This benefit may be at the cost of higher TRAE rates; however, lower TRDD rates suggest a manageable side-effect profile. Longer follow-up is required to determine if the benefits of IO-TKIs will be sustained and if they should be favored in the first-line treatment of mRCC. PATIENT SUMMARY: Combination treatments based on immunotherapy agents continue to show meaningful benefits in the first-line treatment of metastatic kidney cancer. Our review and network meta-analysis shows that immunotherapy combined with another class of agents called tyrosine kinase inhibitors is promising. However, longer follow-up is needed for this treatment strategy to clarify if the benefits are long-lasting.

Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model.

Lung cancer is the leading cause of cancer-related death globally. An improved risk stratification strategy can increase efficiency of low-dose CT (LDCT) screening. Here we assessed whether individual's genetic background has clinical utility for risk stratification in the context of LDCT screening. On the basis of 13,119 patients with lung cancer and 10,008 controls with European ancestry in the International Lung Cancer Consortium, we constructed a polygenic risk score (PRS) via 10-fold cross-validation with regularized penalized regression. The performance of risk model integrating PRS, including calibration and ability to discriminate, was assessed using UK Biobank data (N = 335,931). Absolute risk was estimated on the basis of age-specific lung cancer incidence and all-cause mortality as competing risk. To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (N = 50,772 participants). The lung cancer ORs for individuals at the top decile of the PRS distribution versus those at bottom 10% was 2.39 [95% confidence interval (CI) = 1.92-3.00; P = 1.80 × 10-14] in the validation set (P trend = 5.26 × 10-20). The OR per SD of PRS increase was 1.26 (95% CI = 1.20-1.32; P = 9.69 × 10-23) for overall lung cancer risk in the validation set. When considering absolute risks, individuals at different PRS deciles showed differential trajectories of 5-year and cumulative absolute risk. The age reaching the LDCT screening recommendation threshold can vary by 4 to 8 years, depending on the individual's genetic background, smoking status, and family history. Collectively, these results suggest that individual's genetic background may inform the optimal lung cancer LDCT screening strategy. SIGNIFICANCE: Three large-scale datasets reveal that, after accounting for risk factors, an individual's genetics can affect their lung cancer risk trajectory, thus may inform the optimal timing for LDCT screening.

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility.

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

Determinants of impaired lung function and lung cancer prediction among never-smokers in the UK Biobank cohort.

BACKGROUND: Impaired lung health represents a significant burden on global health, including chronic obstructive pulmonary disease (COPD) and lung cancer. Given its global health impact, it is important to understand the determinants of impaired lung function and its relation to lung cancer risk independent of smoking. However, to date, no study has evaluated determinants of impaired lung function in a cohort exclusively of never-smokers, who also represent a growing proportion of all lung cancers. METHODS: A total of 222,274 never-smokers with reproducible spirograms were identified in the UK Biobank population-based cohort and included in the analysis. Baseline volumetric measures of lung function, including forced expiratory volume in 1-s (FEV1) and forced vital capacity (FVC), were used to define lung function impairment. Determinants of impaired lung function were evaluated using Poisson regression with robust variance estimation. The added value of lung function in lung cancer prediction was evaluated using Fine and Gray regression accounting for the competing risk of all-cause mortality. FINDINGS: Lung function impairment was associated with low birthweight, ambient air pollution (PM2·5 µg/mm3), and overweight, after adjustment for other important risk factors. We observed modest improvement in discrimination by adding lung function to our lung cancer prediction model for never-smokers. The highest optimism-corrected AUC at 3 (0·700, 95% CI: 0·654-0·734) and 5 years (0·694, 95% CI: 0·658-0·736) included FEV1 (% of GLI predicted FEV1), while the highest AUC at 7 years was based on the inclusion of FEV1/FVC (0·722, 95% CI: 0·687-0·762). INTERPRETATION: We identified several modifiable risk factors associated with increased risk of lung function impairment among lifetime never-smokers in UKB. We achieved moderate discrimination for lung cancer risk-prediction for never-smokers, and found modest improvement with the inclusion of lung function. FUND: This study was supported by a Canada Research Chair to RJH.

The association between recreational physical activity, sedentary time, and colorectal polyps in a population screened for colorectal cancer.

PURPOSE: Regular recreational moderate to vigorous physical activity (rMVPA) has been previously associated with a reduced risk of colorectal cancer (CRC), however, few studies have examined the association of rMVPA with colorectal polyps, the pre-malignant precursor lesions. The objective of this study was to examine the associations between physical activity and sitting time and polyps at the time of screening. METHODS: We conducted a cross-sectional study of 2496 individuals undergoing screening-related colonoscopy in Calgary, Alberta, Canada. Physical activity and sitting time were characterized using hours of rMVPA, meeting physical activity recommendations and hours of sitting time using self-reported data obtained from the International Physical Activity Questionnaire. Logistic regression models were used to estimate the crude and adjusted odds ratios (OR) for presence of polyps associated with rMVPA and sitting time. RESULTS: Meeting physical activity guidelines of ≥150 min/week was non-significantly associated with a modest decrease in odds of having ≥1 polyp at screening (ORadj = 0.95, 95% CI: 0.80-1.14). In males, threshold effects for sitting time were observed for up to 20 h/week (ORadj per hour sitting = 1.07, 95% CI: 1.01-1.13). In stratified analysis, larger inverse associations were observed between physical activity and the presence of polyps in females, obese individuals, and ever smokers, compared to pooled findings. CONCLUSIONS: In this large CRC screening population, there was a suggestive association between increased rMVPA and reduced prevalence of polyps at screening, particularly among females. Even low amounts of regular sitting time (0-20 h/day) were associated with the presence of polyps, particularly among males. Further research on rMVPA and sitting time is necessary to better inform strategies to reduce the frequency of pre-malignant colorectal lesions.

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study.

BACKGROUND: A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC. METHODS: This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC. RESULTS: In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80). CONCLUSIONS: Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.

Intake of dietary fibre and lifetime non-steroidal anti-inflammatory drug (NSAID) use and the incidence of colorectal polyps in a population screened for colorectal cancer.

BACKGROUND: There is suggestive evidence that increased intake of dietary fibre and the use of non-steroidal anti-inflammatory drugs (NSAIDs) are generally associated with decreased colorectal cancer risk. However, the effects on precursors of colorectal cancer, such as adenomatous polyps, are mixed. We present the associations between dietary fibre intake and NSAID use on the presence and type of colorectal polyps in a screening population. METHODS: A cross-sectional study of 2548 individuals undergoing colonoscopy at the Forzani & MacPhail Colon Cancer Screening Centre (Calgary, Canada) was conducted. Dietary fibre intake and NSAID use were assessed using the Diet History Questionnaire I or II and the Health and Lifestyle Questionnaire. Colorectal outcomes were documented as a polyp or high-risk adenomatous polyp (HRAP; villous histology, high-grade dysplasia, ≥10 mm or ≥3 adenomas). Crude and ORs and 95% CIs were estimated using unconditional logistic regression. RESULTS: There were 1450 negative colonoscopies and 1098 patients with polyps, of which 189 patients had HRAPs. Total dietary fibre intake was associated with a decreased presence of HRAPs (OR=0.50, 95% CI: 0.29 to 0.86) when comparing the highest to lowest quartiles and was observed with both soluble (OR=0.51, 95% CI: 0.30 to 0.88) and insoluble (OR=0.51, 95% CI: 0.30 to 0.86) fibres. Ever use of NSAIDs was also inversely associated with HRAPs (OR=0.65, 95% CI: 0.47 to 0.89), observed with monthly (OR=0.60, 95% CI: 0.37 to 0.95) and daily (OR=0.53, 95% CI: 0.32 to 0.86) use. CONCLUSIONS: Dietary fibre intake and NSAID use were associated with a decreased risk of having a HRAP at screening.