Cancer Risk Model Research Articles

Abstract PS07-08: Bazedoxifene plus conjugated estrogen reduces mammary proliferation markers and improves adipocyte size, gut microbiome, and metabolic health: Findings from a preclinical model of obesity and breast cancer risk

Abstract Background: Many women at high risk for breast cancer will not take tamoxifen or aromatase inhibitors for cancer prevention due to concern of side effects including hot flashes. Further, tamoxifen has detrimental metabolic effects in some overweight/obese women. Duavee®, a tissue-selective complex of bazedoxifene + conjugated estrogen, is FDA-approved for relief of hot-flashes and prevention of osteoporosis. Preclinical studies suggest favorable metabolic effects and potential for breast cancer risk reduction. In a single arm clinical trial, BZA+CE reduced Ki-67 and mammographic density (PMID: 31420361), and this combination is currently being assessed further in a Phase IIB trial of postmenopausal high risk women with vasomotor symptoms. Here, we examined the modifying effects of obesity on response to BZA+CE in a rodent model of obesity and breast cancer risk. Methods: Rats received carcinogen at 7-weeks of age to induce mammary tumors and were fed a high-fat diet (46% kcal fat) to promote obesity. Lean and obese rat were selected based on adiposity at 16 weeks. Separate cohorts of lean and obese ovary-intact or ovariectomized (OVX) rats were randomized to a daily dose of BZA+CE or placebo control for 8 weeks. We assessed tumor development throughout the study and end of study RNA from mammary glands was analysed by gene expression microarray (Affymetrix). We also analyzed body weight/composition, markers of metabolic health (circulating glucose, insulin, adiponectin), as well as changes in the gut microbiome (metagenomic sequencing on DNA isolated from baseline and end-of-study fecal samples) in response to BZA+CE. Results: BZA+CE improved metabolic health in both ovary intact and OVX rats, including reduced body weight and % body fat, particularly in the visceral regions. These effects were greater in obese rats compared to lean. BZA+CE reduced the number of large adipocytes and increased small (insulin sensitive) adipocytes in the mammary adipose, indicating benefical changes in local tumor microenvironment. We found no evidence that Duavee® promoted tumor development or growth in ovary-intact or OVX’d rats. Gene set enrichment analysis (GSEA) of microarray data showed enrichment of cell proliferation pathways in MG from obese rats and these same pathways were downregulated with BZA+CE, consistent with anti-cancer effects. Analysis of the microbiome found that BZA+CE increased proportional abudance of Odoribacter laneus in the gut, regardless of lean/obese or intact/OVX status. Previous studies have demonstrated that this bacterim improves glucose control and reduces inflammatory cytokines when administered to obese mice, suggesting a possible mechanistic link in this study. Conclusions: Unlike traditional SERMs that can have negative metabolic effects, BZA+CE improved whole body and mammary gland metabolic health and reduced expression of cell proliferation pathways, particularly in obese rats. Prelminary data suggest that changes in the gut microbiome could contribute, at least in part, to these effects. Together this supports BZA+CE (Duavee) as an agent with potential beneficial effects on breast cancer risk reduction and improvements in metabolic health in women with obesity. Further analyses will guide assessment of outcomes in an ongoing parallel clinical study in postmenopausal women at high risk for breast cancer. Citation Format: Katherine L. Cook, Ramsey Jenshcke, Karen Corleto, Carol Fabian, Stephen Hursting, Bruce Kimler, Danilo Landrock, Erin Giles. Bazedoxifene plus conjugated estrogen reduces mammary proliferation markers and improves adipocyte size, gut microbiome, and metabolic health: Findings from a preclinical model of obesity and breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-08.

Abstract PS10-08: Validation of a clinical image-based AI-risk model for individualized breast cancer screening in a multi-national setting

Abstract Background: Image-derived AI risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across different screening settings and clinical feasibility. Purpose: To investigate the predictive performance of a clinically used image-derived AI-based breast cancer risk model in multiple European screening populations. Methods: Four European mammographic screening populations in three countries screened between 2009-2020 for women aged 45-69 was used to perform a nested case-control study. In total, 739 women with incident breast cancers were included together with 7,812 controls matched to cases on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) for risk assessment were extracted using AI from full-field digital mammograms. Breast cancer occurrence was assessed after two years of follow-up. Absolute risks of breast cancer were predicted using the risk model from negative mammograms at study-entry. Adjusted Area Under the receiver operating characteristic Curves (aAUC) estimated discriminatory performance and, adjusted risk-ratios estimated the stratification performance of women at high/general risk per the clinical guidelines. Results: The overall aAUC of the AI risk model was 0.72 (95%CI 0.70-0.75), range 0.71 (95%CI 0.67-0.75) to 0.74 (95%CI 0.69-0.78) for breast cancers developed in four screening populations. In the 4.6% of women classified at high risk using the NICE guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95%CI 5.6-8.0), compared with the 71% of women classified at general risk by the model. Similar risk-ratios were observed across tertiles of mammographic density. In the high-risk group, 22% of the 2-year future cancers were diagnosed, and 29% of stage 2 and higher cancers, p< 0.01. Conclusions: The AI risk model showed generalizable discriminatory performances across European populations and, captured ~30% of clinically relevant stage 2 and higher breast cancers in ~5% of high-risk women who were sent home with a negative mammogram. Similar results were seen in fatty and dense breasts. An image-derived AI model is feasible for personalized screening to improve screening outcomes. Citation Format: Mikael Eriksson, Marta Román, Axel Gräwingholt, Sylvia Heywang-Köbrunner, Paolo Rossi, Per Hall. Validation of a clinical image-based AI-risk model for individualized breast cancer screening in a multi-national setting [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-08.

Abstract PO2-28-09: Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model

Abstract Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation in Black women of a mammography-driven AI breast cancer risk model (Mirai) originally developed on screening cohorts primarily consisting of White women. In this institutional review board-approved, Health Insurance Portability and Protection Act (HIPAA)-compliant study under a waiver of consent, we retrospectively analyzed a case–cohort sample nested within the core academic breast cancer screening practice of BJC Healthcare, the hospital partner of Washington University in St. Louis. For the purposes of this validation study, relying on 2D digital mammography (DM) images, we focused on Black women presenting for annual DM screening (Selenia or Selenia Dimensions; Hologic) between 2008 and 2018. Eligible breast cancer cases were derived from all women with a breast cancer diagnosis (with associated biopsy-confirmed tumor pathology via institutional cancer registry) after negative (BI-RADS 1 or 2) DM screening 1 to 5 years prior to cancer diagnosis. We also identified a random sample of controls, defined as women who had negative (BI-RADS 1 or 2) DM screening, with 1 to 5 years of screening follow-up without a cancer diagnosis. Risk scores for all DM exams were calculated via the Mirai model. Performance was evaluated using concordance-index (C) analyses and associated 95% confidence intervals (CIs) for the entire cohort, as well as for study subgroups of invasive versus in-situ cancer and cancer molecular subtypes. We analyzed 1368 DM screening exams, including 672 DM exams from 391 women diagnosed with breast cancer (mean age, 58 years; standard deviation, 10 years) and 696 DM exams from 406 controls (mean age, 55 years; standard deviation, 10 years). The overall C-index was 0.62 [95% CIs 0.60–0.64] for all Black women, which was lower compared to previously reported validation results for Mirai in studies of similar design on predominantly White and racially diverse screening cohorts (C-index = 0.67-0.78). There was no evidence of a significant difference between invasive and in-situ cancer (C-index = 0.64 [95% CIs 0.61–0.66] vs. 0.64 [95% CIs 0.61–0.67]). Compared to other cancer molecular subtypes, performance was significantly higher among triple-negative (C-index = 0.67 [95% CIs 0.62–0.71]) and estrogen receptor (ER) negative cancer (C-index = 0.66 [95% CIs 0.61–0.70]). A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, particularly for triple-negative and ER- cancer types. However, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts. Our results suggest that further refinements are needed towards more accurate breast cancer risk assessment in Black women. Long-term breast cancer risk prediction performance (C-indices) of Mirai in the full cohort of Black women and in study subgroups. Citation Format: Juanita Hernandez Lopez, Zhixin Sun, Shirin Shoushtari, Ulugbek Kamilov, Debbie Bennett, Aimilia Gastounioti. Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-09.

Abstract 3464: Development and prospective validation of an estrogen receptor positive breast cancer risk model to identify women who could benefit for risk-reducing therapies

Abstract Background: Breast cancer risk-reducing therapies such as tamoxifen and aromatase inhibitors are effective for ER-positive (ER+) but not for ER-negative disease. As these therapies have side effects it is important to identify women at risk of ER+ disease. We developed and validated ER-specific risk prediction models in a prospective cohort and evaluated the expected gains of predicting ER+ vs overall disease. Methods: The iCARE-Lit model integrates established questionnaire-based risk factors and a 313 variant polygenic risk score. We reparametrized the iCARE-Lit model to estimate risk of ER+ and ER- disease separately by obtaining relative risks from published literature for ages at menarche, menopause, first childbirth, and parity to account for their heterogeneous associations with risk of ER-defined subtypes. We estimated 5-year (yr) absolute risk (AR) for ER+ disease using an overall and ER+ model. We evaluated model performance in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial prospective cohort. This included evaluation of calibration (expected-to-observed (E/O) ratio) and discrimination (area under the receiver operating characteristic curve (AUC)), among 46,571 self-reported non-Hispanic White women aged 50-75 yrs (1,052 ER+ cases with 5 yrs of follow-up from enrollment). We compared projected risk stratification of the overall and ER+ models among eligible US women in terms of proportions of women and future ER+ cases identified above the 3% 5-yr AR threshold recommended for risk-reducing interventions in the US. Results: In predicting ER+ disease in PLCO, on average the overall and ER+ AR were well calibrated; however, models overpredicted risk at the ≥3% 5-yr AR threshold, (E/O = 1.31 (1.20, 1. 42) and 1.23 (1.13, 1.34), respectively. Risk discrimination was similar from the overall and ER+ models (AUC=0.65 (0.64-0.67) and 0.66 (0.64-0.67), respectively. The overall model projected that 16% of women in the US could be eligible for risk-reducing therapies, compared to 11% when using ER+ model, resulting in ~1.4 million less women crossing the risk threshold when using the ER+ score. However, the percentage of ER+ cases in the US population projected to occur within 5-yrs among eligible women was larger for the overall (38%) than ER+ (32%) risk score. Conclusion: Findings suggest that ER-specific risk scores might not offer a substantial advantage compared to overall risk scores to identify women eligible for risk-reducing therapies. Further risk-benefit analyses are needed to quantify the potential gains of predicting ER+ vs overall breast cancer. Citation Format: Thomas U. Ahearn, Srijon Mukhopadhyay, Jeya Balasubramanian, Nilanjan Chatterjee, Montserrat García-Closas, Parichoy Pal Choudhury. Development and prospective validation of an estrogen receptor positive breast cancer risk model to identify women who could benefit for risk-reducing therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3464.

Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study

BackgroundImproving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency. MethodsWe used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01–20.8 years). ResultsWithin 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (p < 0.001 and p = 0.028), and reduced the number needed to predict one LC case (29 versus 40, p < 0.001 and 36 versus 40, p = 0.02), respectively. Applying the HUNT LCM 6-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both p < 0.001). ConclusionsThe HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection.

Abstract B080: Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM)

Abstract Introduction: Advanced breast cancer risk models hold promise in implementing more effective and accurate screening strategies, facilitating early detection while minimizing screening-related harm compared to current guidelines. However, integrating machine learning risk models into clinical practice requires essential steps to be taken, including continual refinement to enhance their accuracy, validation across diverse populations, and demonstrating their ability to optimize clinical workflows. Gabbi Risk Assessment Model (GRAM), an innovative machine learning model, is a binary classifier based on medical claims developed to provide personalized breast cancer risk prediction. GRAM efficiently estimates risk and utilizes potentially missing risk factor information. Furthermore, it offers easy integration into EMR systems, thus optimizing its practical application. Methods: Construction of the GRAM utilized a medical claims dataset of 500k members from IQVIA carefully sampled from the entire IQVIA population (IQVIA acquires ~94% of the US claims data) with 4 years of history. The information extracted from the dataset included demographics, and medical history in the form of diagnostic and treatment codes, laboratory tests, and medications. The dataset consisted of 250k women aged 18-70 with a breast cancer diagnosis and a random sample of 250k women aged 18-70 without a known breast cancer diagnosis. For the selected cohorts, a 4-year history before the first breast cancer diagnosis was obtained. The control group was carefully chosen to possess similar statistical properties as the group with breast cancer. All women had a minimum enrollment period of 4 years before diagnosis with the same insurance provider. The GRAM was developed with 4 main steps: 1) member selection - members with breast cancer based on selection criteria defined above were in the test group, members without in the control group; 2) feature engineering to ensure that GRAM learns from medical history before a first diagnosis; 3) model development &amp; testing - training/fine tuning using the ensemble methods and neural network, followed by hyperparameter tuning using a held out validation dataset; and 4) model performance on the held out validation set and identification of key risk factors contributing to the breast cancer risk. Results and Conclusion: By addressing these challenges and harnessing the potential of machine learning, GRAM has the capacity to revolutionize breast cancer risk estimation. Its integration into clinical practice can lead to more targeted and efficient screening strategies, resulting in earlier detection and improved patient outcomes. Preliminary results have shown that the GRAM surpasses the current standard of care models (GAIL and Tyrer Cusick) by at least 20% in sensitivity, and AUC ROC with no decline in specificity. GRAM achieved 91% AUC ROC (balanced data), 86% sensitivity, and 80% specificity on a held out dataset. As research progresses, continuous refinement and validation, the GRAM model holds promise to make significant contributions in the fight against breast cancer and identifying the correlated risk factors. Citation Format: Priyanshi Jain, Shaleen K Theiler, Kaitlin Christine. Advancing Breast Cancer Risk Prediction: Introducing the Gabbi Risk Assessment Model (GRAM) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B080.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

Tissue-specific dose equivalents of secondary mesons and leptons during galactic cosmic ray exposures for mars exploration

During a human mission to Mars, astronauts would be continuously exposed to galactic cosmic rays (GCR) consisting of high energy protons and heavier ions coming from outside our solar system. Due to their high energy, GCR ions can penetrate spacecraft and space habitat structures, directly reaching human organs. Additionally, they generate secondary particles when interacting with shielding materials and human tissues. Baryon secondaries have been the focus of many previous studies, while meson and lepton secondaries have been considered to a much lesser extent. In this work, we focus on assessing the tissue-specific dose equivalents and the effective dose for males of secondary mesons and leptons for the interplanetary cruise phase and the surface phase on Mars. We also provide the energy distribution of the secondary pions in each human organ since they are dominant compared to other mesons and leptons. For this calculation, the PHITS3.27 Monte Carlo simulation toolkit is used to compute the energy spectra of particles in organs in a realistic human phantom. Based on the simulation data, the dose equivalent has been estimated with radiation quality factors in ICRP Publication 60 and in the latest NASA Space Cancer Risk model (NSCR-2022). The effective dose is then assessed with the tissue weighting factors in ICRP Publication 103 and in the NSCR model, separately. The results indicate that the contribution of secondary mesons and leptons to the total effective dose is 6.1 %, 9.1 %, and 11.3 % with the NSCR model in interplanetary space behind 5, 20, and 50 g/cm2 aluminum shielding, respectively, with similar values using the ICRP model. The outcomes of this work lead to an improved understanding of the potential health risks induced by secondary particles for exploration missions to Mars and other destinations.

Risk Prediction Models for Gastric Cancer: A Scoping Review.

Gastric cancer is a significant contributor to the global cancer burden. Risk prediction models aim to estimate future risk based on current and past information, and can be utilized for risk stratification in population screening programs for gastric cancer. This review aims to explore the research design of existing models, as well as the methods, variables, and performance of model construction. Six databases were searched through to November 4, 2023 to identify appropriate studies. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Data sources included PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, focusing on gastric cancer risk prediction model studies. A total of 29 articles met the inclusion criteria, from which 28 original risk prediction models were identified that met the analysis criteria. The risk prediction model is screened, and the data extracted includes research characteristics, prediction variables selection, model construction methods and evaluation indicators. The area under the curve (AUC) of the models ranged from 0.560 to 0.989, while the C-statistics varied between 0.684 and 0.940. The number of predictor variables is mainly concentrated between 5 to 11. The top 5 most frequently included variables were age, helicobacter pylori (Hp), precancerous lesion, pepsinogen (PG), sex, and smoking. Age and Hp were the most consistently included variables. This review enhances understanding of current gastric cancer risk prediction research and its future directions. The findings provide a strong scientific basis and technical support for developing more accurate gastric cancer risk models. We expect that these conclusions will point the way for future research and clinical practice in this area to assist in the early prevention and treatment of gastric cancer.

Advances in Breast Cancer Risk Modeling: Integrating Clinics, Imaging, Pathology and Artificial Intelligence for Personalized Risk Assessment

Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.

Source, distribution, and risk assessment of polycyclic aromatic hydrocarbons in sediment and fish samples from River Owan, Edo State, Nigeria

Polycyclic aromatic hydrocarbons (PAHs) are persistent environmental contaminants that present several environmental risks including human health. The 16 priority PAHs including its 1-methylnaphthalene, and 2-methylnaphthalene were determined in sediment and fish samples (Clarias anguillaris and Oreochromis niloticus) of River Owan, Edo State, Nigeria using gas chromatography (GC) equipped with flame ionization detector (FID) and other standard laboratory protocols. The isomeric ratio was used for source diagnosis, sediment quality guidelines, and risk models of incremental lifetime cancer were used for risk assessment. 1-methylnaphthalene and 2-methylnaphthalene were most predominant in all sediment samples analysed. The ∑LMW PAHs ranged between 0.093—0.250 μg/kg; ∑HMW PAHs were 0.107—0.579 μg/kg. The sediment samples range for ∑PAHs was 0.280–0.810 μg/kg with concentration order of increase: SE5&amp;gt;SE4&amp;gt;SE3&amp;gt;SE6&amp;gt;SE1&amp;gt;SE2&amp;gt;SE7 for the seven sampling locations. The ∑PAHs for Oreochromis niloticus was 0.190 μg/kg, which is higher than the value of Clarias anguillaris 0.080 μg/kg, and these values were greatly lesser when compared to the European Commission limit of 12.00 μg/kg. The diagnostic ratio indicates that the sources are more pyrogenic than petrogenic, revealing combustion from grass, wood, and bush burning. Sediment quality assessment showed that the ∑PAHs were lower than the regulatory values of sediment quality guidelines (SQG) assessment suggesting no ecotoxicological effects on the benthic organisms in this area at present. The Incremental Life Cancer Risk results were in the range of 9.15 × 10−12—1.46 × 10−6 for children, and 7.78 × 10−12—1.76 × 10−6 for adults considering the three routes of exposure. The incremental life cancer risk assessment showed a negligible risk.

Racial/Ethnic disparities in exposure to neighborhood violence and lung cancer risk in Chicago

BackgroundDespite the lower prevalence and frequency of smoking, Black adults are disproportionately affected by lung cancer. Exposure to chronic stress generates heightened immune responses, which creates a cell environment conducive to lung cancer development. Residents in poor and segregated neighborhoods are exposed to increased neighborhood violence, and chronic exposure to violence may have downstream physiological stress responses, which may explain racial disparities in lung cancer in predominantly Black urban communities. MethodsWe utilized retrospective electronic medical records of patients who underwent a screening or diagnostic test for lung cancer at an academic medical center in Chicago to examine the associations between lung cancer diagnosis and individual characteristics (age, gender, race/ethnicity, and smoking status) and neighborhood-level homicide rate. We then used a synthetic population to estimate the neighborhood-level lung cancer risk to understand spatial clusters of increased homicide rates and lung cancer risk. ResultsOlder age and former/current smoking status were associated with increased odds of lung cancer diagnosis. Hispanic patients were more likely than White patients to be diagnosed with lung cancer, but there was no statistical difference between Black and White patients in lung cancer diagnosis. The odds of being diagnosed with lung cancer were significantly higher for patients living in areas with the third and fourth quartiles of homicide rates compared to the second quartile of homicide rates. Furthermore, significant spatial clusters of increased lung cancer risk and homicide rates were observed on Chicago's South and West sides. ConclusionsNeighborhood violence was associated with an increased risk of lung cancer. Black residents in Chicago are disproportionately exposed to neighborhood violence, which may partially explain the existing racial disparity in lung cancer. Incorporating neighborhood violence exposure into lung cancer risk models may help identify high-risk individuals who could benefit from lung cancer screening.

Extending the Breast Cancer Surveillance Consortium Model of Invasive Breast Cancer.

We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening. We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC). We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m2, the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m2 (7.6%-19.8%). The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.

Distribution and transfer rules of polycyclic aromatic hydrocarbons in soil-wheat ecosystems in China.

The translocation and accumulation patterns of polycyclic aromatic hydrocarbons (PAHs) in the soil-crop system have important implications for the fate of PAHs and human health. This study summarized the concentrations of 16 priority PAHs in the soils and various parts of mature winter wheat in China, sourced from a screening of previous literature in English and Chinese databases. The study analyzes the distribution characteristics, transfer patterns, and human health risks of PAHs in sites studied in Shaanxi, Henan, and Shandong provinces. The results showed that the concentrations of Σ16 PAHs in the rhizosphere soil of wheat ranged from 10.30 to 893.68 ng/g, in descending order of Shaanxi > Henan > average > Shandong. In sites with mild to moderate contamination (200 < Σ16 PAHs < 600 ng/g; i.e., Henan and Shaanxi), the concentration of Σ16 PAHs in the roots was higher than that in the stems or the grains, while in contamination-free sites (Σ16 PAHs < 200 ng/g; i.e., Shandong), the highest concentration of Σ16 PAHs was found in the stems. Generally, the concentrations of PAHs increased in the order of roots-stems-grains. The predominant PAHs in each part of wheat were 2- or 3-ring compounds, with five- or six-ring PAHs being more prevalent in wheat from Shanghe, Shandong. The bioaccumulation factors of different wheat parts from Shaanxi and Henan were consistently smaller than 1, and low- and medium-ring (2-4 rings) PAHs had bigger bioconcentration factors than high-ring (5-6 rings) PAHs. However, the accumulation of PAHs in the aboveground parts of wheat was larger than that in the underground parts of the Shandong sites. The linear regression relationship between the octanol-water partition coefficient and root concentration factor (RCF) of PAHs reflected that low and medium-ring PAHs were more easily absorbed by wheat roots than high-ring PAHs in Shaanxi and Henan. Our assessment of the health risks of oral wheat intake in adults and children by the incremental lifetime cancer risk (ILCR) model found a potential carcinogenic risk for both age groups in each province, with higher risks in adults than in children.

Cancer and circulatory disease risks for the largest solar particle events in the space age

In this paper we use the NASA Space Cancer Risk (NSCR version 2022) model to predict cancer and circulatory disease risks using energy spectra representing the largest SPE's observed in the space age. Because tissue dose-rates behind shielding for large SPE's lead to low dose-rates (<0.2 Gy/h) we consider the integrated risk for several historical periods of high solar activity, including July–November, 1960 events and August–October 1989 events along with the February 1956 and August 1972 events. The galactic cosmic ray (GCR) contribution to risks is considered in predictions. Results for these largest historical events show risk of exposure induced death (REID) are mitigated to < 1.2 % with a 95 % confidence interval with passive radiation shielding of 20 g/cm2 aluminum, while larger amounts would support the application of the ALARA principle. Annual GCR risks are predicted to surpass the risks from large SPEs by ∼30 g/cm2 of aluminum shielding.

Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland.

Throughout Europe, computed tomography (CT) screening for lung cancer is in a phase of clinical implementation or reimbursement evaluation. To efficiently select individuals for screening, the use of lung cancer risk models has been suggested, but their incremental (cost-)effectiveness relative to eligibility based on pack-year criteria has not been thoroughly evaluated for a European setting. We evaluate the cost-effectiveness of pack-year and risk-based screening (PLCOm2012 model-based) strategies for Switzerland, which aided in informing the recommendations of the Swiss Cancer Screening Committee (CSC). We use the MISCAN (MIcrosimulation SCreening ANalysis)-Lung model to estimate benefits and harms of screening among individuals born 1940 to 1979 in Switzerland. We evaluate 1512 strategies, differing in the age ranges employed for screening, the screening interval and the strictness of the smoking requirements. We estimate risk-based strategies to be more cost-effective than pack-year-based screening strategies. The most efficient strategy compliant with CSC recommendations is biennial screening for ever-smokers aged 55 to 80 with a 1.6% PLCOm2012 risk. Relative to no screening this strategy is estimated to reduce lung cancer mortality by 11.0%, with estimated costs per Quality-Adjusted Life-Year (QALY) gained of €19 341, and a €1.990 billion 15-year budget impact. Biennial screening ages 55 to 80 for those with 20 pack-years shows a lower mortality reduction (10.5%) and higher cost per QALY gained (€20 869). Despite model uncertainties, our estimates suggest there may be cost-effective screening policies for Switzerland. Risk-based biennial screening ages 55 to 80 for those with ≥1.6% PLCOm2012 risk conforms to CSC recommendations and is estimated to be more efficient than pack-year-based alternatives.

Source apportionment and source specific health risk assessment of HMs and PAHs in soils with an integrated framework in a typical cold agricultural region in China

A new innovative methodology system framework for source apportionment and source-specific risk assessment has been proposed and actively applied to identify the contamination characteristics, oriented sources and health risks associated with contamination levels of Heavy metals (HMs) and Polycyclic Aromatic Hydrocarbons (PAHs) in soils, a typical cold agricultural region in Northeastern China. To achieve this meaningful goal, a large-scale dataset including 1780 top soil samples, 10 HMs and 16 priority PAHs has been organized and collected from a typical study area in China. The total concentrations of the 10 selected HMs in study area range from 0.05 to 2147.40 mg/kg, with an average of 549.25 ± 541.37 mg/kg. The average concentrations of PAHs for (3–6)-rings are 16.60 ± 18.90, 26.40 ± 28.20, 9.51 ± 13.00 and 1.99 ± 5.30 ng/g, respectively. On the base of optimized literature source fingerprints for HM and PAH, a widely used receptor model, positive matrix factorization (PMF) has been applied to apportion the contamination sources HMs and PAHs in soils. Then source-specific health risk of soil HMs and PAHs have been assessed using the probabilistic incremental lifetime cancer risk model incorporated with source apportionment results data. Fertilizer residues/coke oven comprise the primary contamination source contributors of HMs and PAHs with corresponding contributions of 32.23 % and 27.93 % for HMs and 37.94 % for PAHs. Fertilizer/pesticide residues contributes most to the risks of soil HMs (28.8 %), followed by fossil fuel combustion (24.6 %), mining activities (20.2 %), traffic and vehicle emission (16.3 %) and electroplating/dyeing (14.1 %). Meanwhile, the ranking of health risks from the five identified contamination sources of soil PAHs are resident discharge, coal-fired boilers, coke oven emission, gasoline combustion and power plant, with the contribution of 27.1 %, 25.3 %, 17.3 %, 15.5 % and 14.8 %.And relatively, source-specific risk assessment demonstrates fossil fuel and coal combustion contribute the greatest impact to the total risk of HMs and PAHs (61.7 % and 56.1 %), respectively. This study provides a good example of how the source specific health risk assessment can be utilized to reduce the contamination in soils.

Characteristics, Source and Risk Assessment of Soil Polycyclic Aromatic Hydrocarbons around Oil Wells in the Yellow River Delta, China

The Yellow River Delta (YRD) is the most complete wetland ecosystem in the warm temperate zone of China and is rich in oil resources. However, with petroleum extraction and the development of the economy, pollution of the YRD has been paid increasing attention, in particular, pollution via polycyclic aromatic hydrocarbons (PAHs), as they have caused great harm to human health and the ecosystem balance. Based on the investigations of a research group in 2009, this study re-collected samples according to the same sampling points and analyzed the concentration, composition, source, ecological risk and health risk of PAHs in 2021. The concentration of ΣPAH16 in the surface soil of YRD in 2009 ranged from 2.6 to 8275.46 ng/g, with an average of 1744.41 ng/g. The concentration of ΣPAH16 in 2021 ranged from 56.25 to 582.56 ng/g, with an average of 149.63 ng/g. Therefore, the pollution situation in the YRD in 2021 was significantly improved compared with 2009. The composition of PAHs in soil in 2009 and 2021 was similar, which was dominated by low-ring PAHs. The evaluation results of the toxicity equivalent factor method showed that there was no potential ecological risk in the soil in 2009 and 2021. The evaluation results of the lifetime cancer risk increment model showed that the incremental lifetime cancer risk models (ILCRs) of soil PAHs in 2009 and 2021 were lower than the safety threshold of 10−6; therefore, there was no carcinogenic risk. The existing management measures for oil wells need to be further promoted to protect the regional ecological environment in the YRD.

Updated risk models for lung cancer due to radon exposure in the German Wismut cohort of uranium miners, 1946-2018.

UNSCEAR recently recommended that future research on the lung cancer risk at low radon exposures or exposure rates should focus on more contemporary uranium miners. For this purpose, risk models in the German Wismut cohort of uranium miners were updated extending the follow-up period by 5years to 1946-2018. The full cohort (n = 58,972) and specifically the 1960 + sub-cohort of miners first hired in 1960 or later (n = 26,764) were analyzed. The 1960 + sub-cohort is characterized by low protracted radon exposure of high quality of measurements. Internal Poisson regression was used to estimate the excess relative risk (ERR) for lung cancer per cumulative radon exposure in Working Level Months (WLM). Applying the BEIR VI exposure-age-concentration model, the ERR/100WLM was 2.50 (95% confidence interval (CI) 0.81; 4.18) and 6.92 (95% CI < 0; 16.59) among miners with attained age < 55years, time since exposure 5-14years, and annual exposure rates < 0.5WL in the full (n = 4329 lung cancer deaths) and in the 1960 + sub-cohort (n = 663 lung cancer deaths), respectively. Both ERR/WLM decreased with older attained ages, increasing time since exposure, and higher exposure rates. Findings of the 1960 + sub-cohort are in line with those from large pooled studies, and ERR/WLM are about two times higher than in the full Wismut cohort. Notably, 20-30years after closure of the Wismut mines in 1990, the estimated fraction of lung cancer deaths attributable to occupational radon exposure is still 26% in the full Wismut cohort and 19% in the 1960 + sub-cohort, respectively. This demonstrates the need for radiation protection against radon.

Swiss Pilot Low-Dose CT Lung Cancer Screening Study: First Baseline Screening Results.

This pilot study conducted in Switzerland aims to assess the implementation, execution, and performance of low-dose CT lung cancer screening (LDCT-LCS). With lung cancer being the leading cause of cancer-related deaths in Switzerland, the study seeks to explore the potential impact of screening on reducing mortality rates. However, initiating a lung cancer screening program poses challenges and depends on country-specific factors. This prospective study, initiated in October 2018, enrolled participants meeting the National Lung Cancer Study criteria or a lung cancer risk above 1.5% according to the PLCOm2012 lung cancer risk-model. LDCT scans were assessed using Lung-RADS. Enrollment and follow-up are ongoing. To date, we included 112 participants, with a median age of 62 years (IQR 57-67); 42% were female. The median number of packs smoked each year was 45 (IQR 38-57), and 24% had stopped smoking before enrollment. The mean PLCOm2012 was 3.7% (±2.5%). We diagnosed lung cancer in 3.6% of participants (95%, CI:1.0-12.1%), with various stages, all treated with curative intent. The recall rate for intermediate results (Lung-RADS 3,4a) was 15%. LDCT-LCS in Switzerland, using modified inclusion criteria, is feasible. Further analysis will inform the potential implementation of a comprehensive lung cancer screening program in Switzerland.