Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma.

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

How does weight gain since the age of 18 years affect breast cancer risk in later life? A meta-analysis.

Early life factors are important risk factors for breast cancer. The association between weight gain after age 18 and breast cancer risk is inconsistent across previous epidemiologic studies. To evaluate this association, we conducted a meta-analysis according to PRISMA guidelines and the established inclusion criteria. We performed a comprehensive literature search using Medline (Ovid), Embase, Scopus, Cochrane Library, and ClinicalTrials.gov to identify relevant studies published before June 3, 2022. Two reviewers independently reviewed the articles for final inclusion. Seventeen out of 4,725 unique studies met the selection criteria. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS), and all were of moderate to high quality with NOS scores ranging from 5 to 8. We included 17 studies (11 case-control, 6 cohort) in final analysis. In case-control studies, weight gain after age 18 was associated with an increased risk of breast cancer (odds ratio [OR] = 1.25; 95% CI = 1.07-1.48), when comparing the highest versus the lowest categories of weight gain. Menopausal status was a source of heterogeneity, with weight gain after age 18 associated with an increased risk of breast cancer in postmenopausal women (OR = 1.53; 95% CI = 1.40-1.68), but not in premenopausal women (OR = 1.01; 95% CI = 0.92-1.12). Additionally, a 5 kg increase in weight was positively associated with postmenopausal breast cancer risk (OR = 1.12; 95%CI = 1.05-1.21) in case-control studies. Findings from cohort studies were identical, with a positive association between weight gain after age 18 and breast cancer incidence in postmenopausal women (relative risk [RR] = 1.30; 95% CI = 1.09-1.36), but not in premenopausal women (RR = 1.06; 95% CI = 0.92-1.22). Weight gain after age 18 is a risk factor for postmenopausal breast cancer, highlighting the importance of weight control from early adulthood to reduce the incidence of postmenopausal breast cancer.

Predicting the onset of breast cancer using mammogram imaging data with irregular boundary.

With mammography being the primary breast cancer screening strategy, it is essential to make full use of the mammogram imaging data to better identify women who are at higher and lower than average risk. Our primary goal in this study is to extract mammogram-based features that augment the well-established breast cancer risk factors to improve prediction accuracy. In this article, we propose a supervised functional principal component analysis (sFPCA) over triangulations method for extracting features that are ordered by the magnitude of association with the failure time outcome. The proposed method accommodates the irregular boundary issue posed by the breast area within the mammogram imaging data with flexible bivariate splines over triangulations. We also provide an eigenvalue decomposition algorithm that is computationally efficient. Compared to the conventional unsupervised FPCA method, the proposed method results in a lower Brier Score and higher area under the ROC curve (AUC) in simulation studies. We apply our method to data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our approach not only obtains the best prediction performance comparing to unsupervised FPCA and benchmark models but also reveals important risk patterns within the mammogram images. This demonstrates the importance of utilizing additional supervised image-based features to clarify breast cancer risk.

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Avoiding lead-time bias by estimating stage-specific proportions of cancer and non-cancer deaths.

PURPOSE: Understanding how stage at cancer diagnosis influences cause of death, an endpoint that is not susceptible to lead-time bias, can inform population-level outcomes of cancer screening. METHODS: Using data from 17 US Surveillance, Epidemiology, and End Results registries for 1,154,515 persons aged 50-84 years at cancer diagnosis in 2006-2010, we evaluated proportional causes of death by cancer type and uniformly classified stage, following or extrapolating all patients until death through 2020. RESULTS: Most cancer patients diagnosed at stages I-II did not go on to die from their index cancer, whereas most patients diagnosed at stage IV did. For patients diagnosed with any cancer at stages I-II, an estimated 26% of deaths were due to the index cancer, 63% due to non-cancer causes, and 12% due to a subsequent primary (non-index) cancer. In contrast, for patients diagnosed with any stage IV cancer, 85% of deaths were attributed to the index cancer, with 13% non-cancer and 2% non-index-cancer deaths. Index cancer mortality from stages I-II cancer was proportionally lowest for thyroid, melanoma, uterus, prostate, and breast, and highest for pancreas, liver, esophagus, lung, and stomach. CONCLUSION: Across all cancer types, the percentage of patients who went on to die from their cancer was over three times greater when the cancer was diagnosed at stage IV than stages I-II. As mortality patterns are not influenced by lead-time bias, these data suggest that earlier detection is likely to improve outcomes across cancer types, including those currently unscreened.

Modeling correlated pairs of mammogram images.

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Modifiable risk factors in women at high risk of breast cancer: a systematic review.

BACKGROUND: Modifiable risk factors (alcohol, smoking, obesity, hormone use, and physical activity) affect a woman's breast cancer (BC) risk. Whether these factors affect BC risk in women with inherited risk (family history, BRCA1/2 mutations, or familial cancer syndrome) remains unclear. METHODS: This review included studies on modifiable risk factors for BC in women with inherited risk. Pre-determined eligibility criteria were used and relevant data were extracted. RESULTS: The literature search resulted in 93 eligible studies. For women with family history, most studies indicated that modifiable risk factors had no association with BC and some indicated decreased (physical activity) or increased risk (hormonal contraception (HC)/menopausal hormone therapy (MHT), smoking, alcohol). For women with BRCA mutations, most studies reported no association between modifiable risk factors and BC; however, some observed increased (smoking, MHT/HC, body mass index (BMI)/weight) and decreased risk (alcohol, smoking, MHT/HC, BMI/weight, physical activity). However, measurements varied widely among studies, sample sizes were often small, and a limited number of studies existed. CONCLUSIONS: An increasing number of women will recognize their underlying inherited BC risk and seek to modify that risk. Due to heterogeneity and limited power of existing studies, further studies are needed to better understand how modifiable risk factors influence BC risk in women with inherited risk.

Adolescent dietary patterns in relation to later prostate cancer risk and mortality in the NIH-AARP Diet and Health Study.

BACKGROUND: Although adolescent diet has been proposed to contribute to prostate cancer (PCa) development, no studies have investigated the relation between adolescent dietary patterns and PCa risk or mortality. METHODS: Using data from 164,079 men in the NIH-AARP Diet and Health Study, we performed factor analysis to identify dietary patterns at ages 12-13 years and then used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of total (n = 17,861), non-advanced (n = 15,499), advanced (n = 2362), and fatal PCa (n = 832). RESULTS: Although not entirely consistent across analyses, a higher adolescent plant-based pattern (characterised by vegetables, fruits, and dark bread) score was associated with slightly reduced risks of total (fully adjusted HRQ5vs.Q1 = 0.93, 95% CI: 0.89-0.98, p trend=0.003) and non-advanced PCa (HR = 0.91, 95% CI: 0.87-0.96, p trend<0.001), whereas no associations were observed for advanced or fatal PCa, or for Western modern (characterised by sweets, processed meat, beef, cheese, and pizza) or Western traditional (characterised gravy, eggs, potatoes and white bread) patterns. CONCLUSION: We found evidence to support a modest, protective role for a plant-based dietary pattern during adolescence on PCa risk. If confirmed in future studies, our findings may help to inform the development of new, primary prevention strategies for PCa.

Development and validation of the lung donor (LUNDON) acceptability score for pulmonary transplantation.

There is a chronic shortage of donor lungs for pulmonary transplantation due, in part, to low lung utilization rates in the United States. We performed a retrospective cohort study using data from the Scientific Registry of Transplant Recipients database (2006-2019) and developed the lung donor (LUNDON) acceptability score. A total of 83 219 brain-dead donors were included and were randomly divided into derivation (n = 58 314, 70%) and validation (n = 24 905, 30%) cohorts. The overall lung acceptance was 27.3% (n = 22 767). Donor factors associated with the lung acceptance were age, maximum creatinine, ratio of arterial partial pressure of oxygen to fraction of inspired oxygen, mechanism of death by asphyxiation or drowning, history of cigarette use (≥20 pack-years), history of myocardial infarction, chest x-ray appearance, bloodstream infection, and the occurrence of cardiac arrest after brain death. The prediction model had high discriminatory power (C statistic, 0.891; 95% confidence interval, 0.886-0.895) in the validation cohort. We developed a web-based, user-friendly tool (available at https://sites.wustl.edu/lundon) that provides the predicted probability of donor lung acceptance. LUNDON score was also associated with recipient survival in patients with high lung allocation scores. In conclusion, the multivariable LUNDON score uses readily available donor characteristics to reliably predict lung acceptability. Widespread adoption of this model may standardize lung donor evaluation and improve lung utilization rates.

Adherence to healthy diet and risk of cardiovascular disease in adult survivors of childhood cancer in the St. Jude Lifetime Cohort: a cross-sectional study.

BACKGROUND: Whether diet has beneficial effects on cardiovascular disease (CVD) in childhood cancer survivors as in the general population is unknown. Therefore, we examined associations between dietary patterns and risk of CVD in adult survivors of childhood cancer. METHODS: Childhood cancer survivors, 18-65 years old in the St Jude Lifetime Cohort (1882 men and 1634 women) were included in the analysis. Dietary patterns were defined by the adherence to the Healthy Eating Index (HEI)-2015, Dietary Approaches to Stop Hypertension (DASH), and alternate Mediterranean diet (aMED) based on a food frequency questionnaire at study entry. CVD cases (323 in men and 213 in women) were defined as participants with at least one grade 2 or higher CVD-related diagnosis at baseline. Multivariable logistic regression adjusted for confounders was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of CVD. RESULTS: Greater adherence to HEI-2015 (OR=0.88, 95% CI: 0.75-1.03, per 10 score increment), DASH (OR=0.85, 95% CI: 0.71-1.01, per 10 score increment), and aMED (OR=0.92, 95% CI: 0.84-1.00, each score increment) were, albeit trending towards significance, associated with a lower risk of CVD in women. HEI-2015 was associated with a non-significantly lower risk of CVD in men (ORQ5 vs. Q1=0.80, 95% CI: 0.50-1.28). These dietary patterns were also associated with a lower risk of CVD in survivors with high underlying CVD risk. CONCLUSIONS: As recommended to the general population, a diet rich in plant foods and moderate in animal foods needs to be a part of CVD management and prevention in childhood cancer survivors.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature.

PURPOSE: It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk. METHODS: The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool. RESULTS: Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding. CONCLUSION: This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.

Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.

Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data.

Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms.

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

Hormone and receptor activator of NF-κB (RANK) pathway gene expression in plasma and mammographic breast density in postmenopausal women.

BACKGROUND: Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women. METHODS: We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density. RESULTS: One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0-15%, p value = 0.05), 10% (95% CI 0-20%, p value = 0.04) and % (95% CI 0-9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4-19%, p value = 0.003) and 7% (95% CI 0-13%, p value = 0.04) lower non-dense volume, respectively. CONCLUSION: These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies.

Key risk factors for the relative and absolute 5-year risk of cancer to enhance cancer screening and prevention.

BACKGROUND: This study identifies populations who may benefit most from expanded cancer screening. METHODS: Two American Cancer Society prospective cohort studies, Cancer Prevention Study-II Nutrition Cohort and Cancer Prevention Study-3, were used to identify the risk factors associated with a > 2% absolute risk of any cancer within 5 years. In total, 429,991 participants with no prior personal history of cancer were followed for cancer for up to 5 years. Multivariable Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for association. By using these hazard ratios, individualized coherent absolute risk estimation was used to calculate absolute risks by age. RESULTS: Overall, 15,226 invasive cancers were diagnosed among participants within 5 years of enrollment. The multivariable-adjusted relative risk of any cancer was strongest for current smokers compared with never-smokers. In men, alcohol intake, family history of cancer, red meat consumption, and physical inactivity were also associated with risk (p < .05). In women, body mass index, type 2 diabetes, hysterectomy, parity, family history of cancer, hypertension, tubal ligation, and physical inactivity were associated (p < .05). The absolute 5-year risk exceeded 2% among nearly all participants older than 50 years and among some participants younger than 50 years, including current or former smokers (<30 years since quitting) and long-term nonsmokers with a body mass index >25 kg/m2 or a first-degree family history of cancer. The absolute 5-year risk was as high as 29% in men and 25% in women. CONCLUSIONS: Older age and smoking were the two most important risk factors associated with the relative and absolute 5-year risk of developing any cancer.

Joanne Knight Breast Health Cohort at Siteman Cancer Center.

PURPOSE: The Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening to study risk factors and validate models for breast cancer risk prediction. METHODS: Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St. Louis, Missouri. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry, and death register. Routine screening mammograms are collected every 1-2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources. RESULTS: Cohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74% of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person-years of follow-up from date of blood draw per participant. 9,997 women are continuing in follow-up. Data collected at baseline include breast cancer risk factors, plasma and white blood cells, and mammograms prior to baseline, at baseline, and during follow-up. CONCLUSION: This cohort assembled and followed in a routine mammography screening and care setting that serves a diverse population of women in the St. Louis region now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and repeated breast image features into prospective evaluation for breast cancer etiology and outcomes.

Factors associated with initiation and continuation of endocrine therapy in women with hormone receptor-positive breast cancer.

BACKGROUND: Despite benefits of endocrine therapy (ET) for patients with hormone-receptor (HR)-positive breast cancer, many patients do not initiate or discontinue ET against recommendations. METHODS: We identified variables associated with ET initiation and continuation, analyzing pooled data from two longitudinal studies at a National Cancer Institute comprehensive cancer center in St. Louis, Missouri. The sample included 533 women with newly diagnosed, non-metastatic, HR-positive breast cancer who completed interviews at enrollment and 6, 12, and 24 months after definitive surgical treatment. Logistic regression models estimated the adjusted odds ratio and 95% confidence interval (aOR [95% CI]) for each of self-reported ET initiation by the 12-month interview and continuation for ≥12 months by the 24-month interview in association with self-reported diabetes, elevated depressed mood, menopausal-symptom severity and obesity, adjusting for race, age, insurance status, chemotherapy, and radiation therapy. RESULTS: Overall, 81.4% (434/533) of patients initiated ET, and 86.5% (371/429) continued ET ≥12 months. Patients with diabetes had lower odds of initiating ET (0.50 [0.27-0.91]). Patients reporting greater menopausal-symptom severity had lower odds of continuing ET (0.72 [0.53-0.99]). CONCLUSION: Efforts to increase ET initiation among patients with diabetes and better manage severe menopausal symptoms among ET users might promote ET continuation. CLINICAL TRIAL INFORMATION: ClinicalTrials.gov : #NCT00929084.

Increased Reach and Effectiveness With a Low-Burden Point-of-Care Tobacco Treatment Program in Cancer Clinics.

BACKGROUND: Tobacco cessation after a cancer diagnosis can extend patient survival by improving outcomes for primary cancer and preventing secondary cancers. However, smoking is often unaddressed in cancer care, highlighting the need for strategies to increase treatment reach and cessation. This study examined a low-burden, point-of-care tobacco treatment program (ELEVATE) featuring an electronic health record-enabled smoking module and decision support tools to increase the reach and effectiveness of evidence-based smoking cessation treatment. METHODS: This study included adult outpatient tobacco smokers (n=13,651) in medical oncology, internal medicine, and surgical oncology clinics from a large midwestern healthcare system. We examined reach and effectiveness of ELEVATE with 2 comparisons: (1) preimplementation versus postimplementation of ELEVATE and (2) ELEVATE versus usual care. Data were evaluated during 2 time periods: preimplementation (January through May 2018) and postimplementation (June through December 2018), with smoking cessation assessed at the last follow-up outpatient encounter during the 6 months after these periods. RESULTS: The proportion of current tobacco smokers receiving cessation treatment increased from pre-ELEVATE to post-ELEVATE (1.6%-27.9%; difference, 26.3%; relative risk, 16.9 [95% CI, 9.8-29.2]; P<.001). Compared with 27.9% treatment reach with ELEVATE in the postimplementation time period, reach within usual care clinics ranged from 11.8% to 12.0% during this same period. The proportion of tobacco smokers who subsequently achieved cessation increased significantly from pre-ELEVATE to post-ELEVATE (12.0% vs 17.2%; difference, 5.2%; relative risk, 1.3 [95% CI, 1.1-1.5]; P=.002). Compared with 17.2% smoking cessation with ELEVATE in the postimplementation time period, achievement of cessation within usual care clinics ranged from 8.2% to 9.9% during this same period. CONCLUSIONS: A low-burden, point-of-care tobacco treatment strategy increased tobacco treatment and cessation, thereby improving access to and the impact of evidence-based cessation treatment. Using implementation strategies to embed tobacco treatment in every healthcare encounter promises to engage more smokers in evidence-based treatment and facilitate smoking cessation, thereby improving care cancer for patients who smoke.

Implementation of a provider-focused intervention for maximizing human papillomavirus (HPV) vaccine uptake in young cancer survivors receiving follow-up care in pediatric oncology practices: protocol for a cluster-randomized trial of the HPV PROTECT intervention.

BACKGROUND: Childhood cancer survivors are at high risk for developing new cancers (such as cervical and anal cancer) caused by persistent infection with the human papillomavirus (HPV). HPV vaccination is effective in preventing the infections that lead to these cancers, but HPV vaccine uptake is low among young cancer survivors. Lack of a healthcare provider recommendation is the most common reason that cancer survivors fail to initiate the HPV vaccine. Strategies that are most successful in increasing HPV vaccine uptake in the general population focus on enhancing healthcare provider skills to effectively recommend the vaccine, and reducing barriers faced by the young people and their parents in receiving the vaccine. This study will evaluate the effectiveness and implementation of an evidence-based healthcare provider-focused intervention (HPV PROTECT) adapted for use in pediatric oncology clinics, to increase HPV vaccine uptake among cancer survivors 9 to 17 years of age. METHODS: This study uses a hybrid type 1 effectiveness-implementation approach. We will test the effectiveness of the HPV PROTECT intervention using a stepped-wedge cluster-randomized trial across a multi-state sample of pediatric oncology clinics. We will evaluate implementation (provider perspectives regarding intervention feasibility, acceptability and appropriateness in the pediatric oncology setting, provider fidelity to intervention components and change in provider HPV vaccine-related knowledge and practices [e.g., providing vaccine recommendations, identifying and reducing barriers to vaccination]) using a mixed methods approach. DISCUSSION: This multisite trial will address important gaps in knowledge relevant to the prevention of HPV-related malignancies in young cancer survivors by testing the effectiveness of an evidence-based provider-directed intervention, adapted for the pediatric oncology setting, to increase HPV vaccine initiation in young cancer survivors receiving care in pediatric oncology clinics, and by procuring information regarding intervention delivery to inform future implementation efforts. If proven effective, HPV PROTECT will be readily disseminable for testing in the larger pediatric oncology community to increase HPV vaccine uptake in cancer survivors, facilitating protection against HPV-related morbidities for this vulnerable population. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04469569, prospectively registered on July 14, 2020.