bluuebunny/crossref_metadata_2025_split huggingface dataset:
Budget impact model of LungFlag, a predictive risk model for lung cancer screening.
Authors: Gould Michael K. , Choman Eran Netanel , Olghi Nicolò , Obradovic Milan , Ogale Sarika , Heuser Sanmartin Carolina 
Published: June 2024

10534 Background: Lung cancer screening (LCS) programs using low-dose computed tomography (LDCT) for early detection reduce mortality and have been widely recommended. LungFlag is a machine-learning risk prediction model that uses patient-level data to identify individuals at high risk of developing non-small cell lung cancer (NSCLC), prompting their physician to recommend screening with LDCT. Methods: A budget impact model was developed to estimate the costs associated with adoption of LungFlag as an adjunct to existing US Preventive Services Task Force (USPSTF) screening guidelines for a hypothetical US commercial health plan population of 1 million beneficiaries. The model calculates the total expected annual costs of screening for NSCLC with LDCT in scenarios with and without LungFlag, including healthcare resource utilization for detecting NSCLC and treatment of patients diagnosed with NSCLC. Incremental costs were evaluated over a 5-year period. Results: Among 36,803 USPSTF-eligible persons, we assumed that 4600 (12.5%) had already initiated LCS, leaving 32,203 persons who were candidates for pre-screening with LungFlag. The model estimated that 17 additional NSCLC diagnoses per year would be detected by screening when using LungFlag, with most in stage 1. Over 5 years, LungFlag was estimated to result in 33 fewer patients with stage 3 or stage 4 NSCLC at diagnosis and 22 fewer NSCLC-related deaths. Use of LungFlag increased annual costs during the first 2 years and provided cost savings from Year 4 onwards (Table). Cost savings from LungFlag were attributable to reductions in the costs of advanced NSCLC treatment. Conclusions: In a population of 1 million commercial health plan beneficiaries, the adoption of LungFlag as an adjunct to existing screening guidelines for USPSTF-eligible patients was estimated to prevent 22 additional NSCLC-related deaths, with a cost savings of $2.87 million over 5 years from a US commercial payer perspective. [Table: see text]
An emulated target trial assessing the impact of physical activity on 14-year cancer risk: Insights from the Cancer Prevention Study-II.
Authors: Elahy Valeria , McCullough Marjorie , Patel Alpa , Rees-Punia Erika , Peoples Anita R , Wang Ying 
Published: June 2024

10541 Background: The American Cancer Society, along with the World Cancer Research Fund/American Institute of Cancer Research, recommends a weekly regimen of physical activity (at least 75 minutes of vigorous or 150 minutes of moderate-intensity aerobic physical activity) for cancer prevention. The positive impact of physical activity on reducing cancer risk is well-known; however, the causal link between physical activity and cancer risk reduction is not established, primarily due to the observational nature of the current evidence. Given the ethical considerations involved in assigning physical activity levels below the recommended minimum, coupled with the impracticality of conducting decades-long trials due to the latency period of cancer development, there is a need for an innovative approach beyond traditional observational and randomized trial methodologies. This highlights the importance of applying alternative strategies like emulated target trials to produce evidence of the causal effect of physical activity on cancer risk that would otherwise be difficult to obtain. This study aims to estimate the causal effect of adherence to recommended physical activity levels on the 14-year total cancer and obesity-related cancer risk using an emulated target trial framework. Methods: Data from 79,668 participants who were non-current smokers and without prevalent cancer diagnosis at pre-baseline in the Cancer Prevention Study-II were analyzed. This study estimated the effect of different physical activity levels (<7.5, 7.5-<15, and ≥15 MET-hrs/wk) on 14-year cancer risks using a parametric g-formula. Models adjusted for confounders include age, sex, education, years since quitting smoking, family history of cancer, physical activity, body mass index, alcohol consumption, diet quality (measured by the ACS diet score), history of cardiovascular disease, and diabetes. Results: Under no intervention, the estimated 14-year risk for any cancer was 27.9% (95% CI: 27.6%, 28.4%), and for obesity-related cancers, it was 11.2% (95% CI: 11.0%, 11.5%). Compared with no intervention, engaging in 7.5-<15 MET-hrs/wk of physical activity (minimum recommended) was linked with a 0.31% reduction in the risk of any cancer (95% CI: -0.41%, -0.06%) and a 0.19% reduction in the risk of obesity-related cancers (95% CI: -0.37%, -0.04%). Engaging in ≥15 MET-hrs/wk of physical activity (twice the minimum recommended level) was linked with a 1.31% risk reduction for any cancer (95% CI: -1.67%, -1.20%) and a 0.91% risk reduction for obesity-related cancers (95% CI: -1.09%, -0.80%), compared with no intervention. Conclusions: Incremental adherence to physical activity recommendations for cancer prevention could significantly lower the risk of developing cancers, including those related to obesity.
T cell-mediated rejection and cancer risk in 579,905 organ transplant recipients.
Authors: Kim Hye Sung , Woo Wongi , Shin Hyun Joon , Choi Young-Geun , Bharat Ankit , Chae Young Kwang 
Published: June 2024

10552 Background: Prolonged immunosuppression following organ transplant raises post-transplant malignancy (PTM) risk without affecting chronic rejection graft failure (CRGF) rates. Our study investigated if T cell responses during rejection could protect against cancer, whereas T cell suppression in PTM might prevent rejection. Methods: Data from 1987 to 2023 on adult first-time single-organ transplant recipients were collected from the UNOS-OPTN database, excluding those with a history of malignancy. Time-dependent Cox regression and landmark analyses (at 1, 2, 3, 5, 10, 15, and 20 years) were used to explore the link between rejection-induced graft failure (RGF) and PTM. Results: The study included 579,905 recipients (kidney 386,878, liver 108,390, heart 45,046, lung 37,643, pancreas 1,948), with a mean follow-up of 7.3 years. RGF occurred in 9.5% of recipients (kidney 12.1%, liver 1.8%, heart 3.9%, lung 12.0%, pancreas 10.2%; p<0.001), with 2.1% experiencing acute rejection graft failure (ARGF) and 7.4%, CRGF. PTM occurred in 12.0% of recipients (kidney 9.9%, liver 12.4%. heart 20.9%, lung 22.6%, pancreas 11.3%; p<0.001), including breast (0.5%), digestive (1.2%), genitourinary (1.7%), gynecologic (0.1%), head and neck (0.3%), hematologic (1.4%), musculoskeletal (0.1%), neurologic (0.0%), respiratory (1.1%), and skin/Kaposi sarcoma (6.4%). In recipients with RGF, PTM risk significantly decreased (HR 0.21 [0.19-0.24]; p<0.001), with consistent trends observed across seven time landmarks (HR 0.20-0.07; p<0.001). Both ARGF and CRGF were associated with lower PTM risk (HR 0.23 [0.18-0.29], 0.21 [0.19-0.24]; p<0.001). In organ-specific analyses, kidney transplants showed similar outcomes for RGF, ARGF, and CRGF (HR 0.21-0.22; p<0.001). Lung transplants exhibited significant risk reductions with RGF (HR 0.44 [0.22-0.89]; p=0.02) and CRGF (HR 0.47 [0.24-0.94]; p=0.03), with no significant results in other organs. All findings remained consistent even after excluding re-transplantations and deaths due to rejection. Excluding PTM-related deaths from PTM cases significantly lowered RGF risk (HR 0.69 [0.65-0.73]; p<0.001), a trend that was consistent across all time landmarks (HR 0.51-0.80; p<0.001). This inverse pattern was seen in kidney, liver, heart, and lung transplants (HR 0.90 [0.84-0.95], 0.21 [0.11-0.38], 0.21 [0.14-0.31], 0.18 [0.14-0.23]; p<0.001) but not in pancreas transplants (HR 1.62 [0.69-3.77]; p=0.27). Conclusions: This study shows that rejection lowers cancer risk in kidney and lung transplants and that PTM reduces rejection risk in kidney, liver, heart, and lung transplants. Implementing enhanced cancer screening for those who have not experienced rejection may be considered. Further research is needed to elucidate the underlying T cell biology specific to each type of transplant.
Associations between violence, distress, and cancer in transgender and non-transgender people.
Authors: Alpert Ash Blythe , Sayegh Sabrina Jamileh , Cunliffe Scott , Sevelius Jae , Operario Don , Pachankis John , Irwin Melinda , Cerulli Catherine , Griggs Jennifer J. , Strawderman Myla S. 
Published: June 2024

10566 Background: Due to transphobia, transgender people experience extreme rates of violence and distress. In other populations, violence and distress have been linked to increased cancer morbidity and mortality likely due to increased inflammation and higher rates of cancer risk factors. Methods: To evaluate associations between gender identity, violence, distress, cancer risk factors, and cancer, we developed an a priori structural equations model (SEM) and conducted a cross-sectional cohort study using electronic medical record data entered between 2006-2021 at a New York academic hospital. Participants were adults identified as transgender (N=894) by diagnosis codes, keyword searches, and structured gender identity information. We used age and follow-up time to match this cohort with participants not identified as transgender (N=1788) (2:1). We noted experiences of violence using keyword searches, diagnosis codes, and a screening question, “Are you safe at home?” We assessed distress as a latent factor based on mental illness diagnosis codes and keyword searches and diagnosis codes related to childhood violence. Cancer risk factors included ever smoking, body mass index ≥ 30, and history of HIV, HPV, or hepatitis B or C. We made unadjusted comparisons of violence, distress, cancer risk factors, and cancer in transgender and non-transgender people using two-sample chi-square tests. Our SEM included a direct path from gender identity to cancer or precancerous lesions as well as indirect paths through intermediaries of adult violence, distress, and cancer risk factors. We assessed direct paths for statistical significance using z-tests and indirect paths using bias-corrected bootstrap confidence intervals. Results: The association between gender identity and probability of cancer transmitted through the intermediate variables of violence, distress, and having multiple cancer risk factors was significant (p=0.05). Conclusions: In our model, transgender people experienced high rates of violence and distress, both of which were associated with cancer risk factors and cancer. Transphobia and violence experienced by transgender people must be addressed and mitigated. More work is needed to better understand associations between violence, distress, and cancer risk. [Table: see text]
Association between congenital heart disease and cancer: A meta-analysis of 30 million patients.
Authors: Batra Nitya , Jaiswal Vikash , Khosla Atulya Aman , Deb Novonil , Vadhera Ananya , Hanif Muhammad , Sawhney Aanchal , Hameed Maha , Jaiswal Akash , Ezekwudo Daniel , Jaiyesimi Ishmael A. 
Published: June 2024

10568 Background: Birth defects have been associated with increased cancer risk; however, the association between congenital heart disease (CHD) and cancer risk has not been well established with limited and conflicting literature till date. Children afflicted with CHD possess multiple risk factors for oncogenesis, such as chromosomal and genetic abnormalities, ionising radiation exposure during cardiac imaging and interventions, chronic hypoxia and debility. We sought to evaluate the relationship between CHD and cancer risk and its various subtypes. Methods: We performed a systematic literature search on PubMed, EMBASE, and Scopus for relevant articles from inception until February 4th, 2023. Odds ratios (OR) and 95% confidence intervals (CI) were pooled using a random-effects model, and a p-value of <0.05 was considered statistically significant. Results: A total of 5 studies with 30,437,431 patients (CHD group=213,229, and non-CHD group = 30,224,202) were involved in the analysis. Mean age of patients with CHD and non CHD was 48.4 and 51.3 years. The pooled analysis revealed that patients with CHD had a higher risk of skin cancer (OR, 1.16(95%CI: 1.10-1.24), p<0.00001), and thyroid cancer (OR, 1.47(95%CI: 1.20-1.80), p<0.001) compared with non-CHD patients. However, leukemia (OR, 0.74(95%CI: 0.22-2.48), p=0.62), gastrointestinal cancer (OR, 1.50(95%CI: 0.79-2.87), p=0.22), central nervous systems tumor (OR, 1.18(95%CI: 0.74-1.88), p=0.48), respiratory system cancer (OR, 0.94(95%CI: 0.67-1.30), p=0.69), breast cancer (OR, 95%CI: 0.96-1.48), p=0.87, genitourinary system cancer (OR, 1.06(95%CI: 0.73-1.54), p=0.76), and musculoskeletal cancer (OR, 1.40(95%CI: 0.92-2.41), p=0.12) was comparable between CHD and non-CHD groups of patients. Conclusions: Our findings indicate that CHD patients are at higher risk of thyroid and skin cancer compared with non-CHD patients; however, no such association with other cancers was observed. The implications of these results extend to the delineation of subsequent research, and the results underscore the necessity for further exploration into the underlying pathophysiological mechanisms. Potential implications include the establishment of enhanced surveillance and screening protocols to protect this vulnerable patient group. [Table: see text]
Metabolomics signatures of air pollution mixtures and lung cancer risk: A large-scale metabolome-wide association study in the Cancer Prevention Study cohorts.
Authors: Chow Sabrina S , Wang Ying , Sarnat Jeremy A , Diver Ryan , Deubler Emily L , Eick Stephanie M , Walker Douglas I , Tang Ziyin , Kesarwala Aparna H. , Turner Michelle , Liang Donghai 
Published: June 2024

10571 Background: Despite the well-established link between air pollution exposure and lung carcinogenesis, the underlying mechanism remains underexplored. While metabolomics has provided valuable molecular insights, existing air pollution metabolomics studies only assess biological changes to single air pollutants, without examining the join effects of air pollutant mixtures. We sought to identify metabolic signatures mediating the association between ambient air pollution mixtures and lung cancer risk. Methods: A total of 603 matched lung cancer case-control pairs were selected from the Cancer Prevention Study cohorts. Controls were matched to cases on age at blood draw, sex, race/ethnicity, and blood draw date. Plasma metabolomics was conducted using ultrahigh-performance liquid chromatography-tandem mass spectrometry. Air pollution mixtures include carbon monoxide, nitrogen dioxide, particulate matter, sulfur dioxide, and ozone, matched to subjects’ residential address at blood draw. We conducted an untargeted metabolome-wide association study with multivariable-adjusted linear and conditional logistic regression models to assess associations between metabolites and individual air pollutants and lung cancer risk, respectively, adjusting for individual and lifestyle-related covariates. We used a meet-in-the-middle (MITM) approach to identify potential mediators. Quantile g-computation was used to examine the effects of air pollution mixtures on metabolome perturbations to analyze the collective effects of multiple air pollutants concurrently. Results: Among 1,138 extracted metabolic features, 162 were significantly associated with the combined mixture (false discovery rate < 0.2). Using MITM, no metabolites were associated with both air pollution mixture and lung cancer risk at FDR < 0.2. At raw p < 0.05, 16 metabolites were identified as potential mediators of the air pollution mixture and lung cancer risk. These metabolites, including 4-hydroxyphenylacetylglutamine, glutamate, and 3-aminoisobutyrate, were also significantly associated with the mixture at FDR < 0.2. Conclusions: Findings from this analysis demonstrate the feasibility of considering multiple air pollutants as mixtures when conducting air pollution-related metabolomics investigations. Future exploration of the underlying biological mechanisms will be critical for validating pathways that participate in air pollution carcinogenicity. As lung cancer cases in never smokers continue to increase, it is pertinent to study air pollution and other environmental risk factors on cancer incidence.
Joint effects of long-term exposure to multiple air pollutants and breast cancer risk.
Authors: Fervers Béatrice , Giampiccolo Camille , Coudon Thomas , Praud Delphine , Grassot Lény , Faure Elodie , Severi Gianluca , Mancini Francesca Romana , Amadou Amina , Roy Pascal 
Published: June 2024

10572 Background: Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effect of exposure to multiple air pollutants on breast cancer risk. We assessed the joint effects of multiple air pollutants and breast cancer risk. Methods: The study was based on a case-control study nested within the French E3N cohort, involving 5222 incident breast cancer cases and 5222 matched controls. For each woman, the average of the mean annual concentration estimates for eight air pollutants ((benzo(a)pyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO2), particulate matters (PM2.5, PM10), ozone) was estimated from cohort inclusion (in 1990) to the index date. We used Bayesian Profile Regression, which groups individuals according to their exposure profile and risk levels, and assigns a risk to each of these clusters. Odds ratios (OR) and their 95% credible intervals (CrI) were estimated. Results: Overall, 21 clusters were identified. The cluster characterized by low exposures to all pollutants, except ozone was taken as reference. A significant increase in breast cancer risk compared to the reference cluster was observed for 3 clusters, with the OR of 1.61 (CrI: 1.13;2.26), OR (1.00; 1.88), and OR 1.38 (1.00; 1.88) respectively. These three clusters share quite similar exposure profiles characterized by women in urban settings with high exposure to most pollutants, particularly to NO2, PM and PCB153. Conclusions: Overall, there is evidence of a positive joint effect of high level exposures to air several pollutants, namely NO2, PM and PCB153 and increased risk of breast cancer.
Uptake of risk-reduction, surveillance, and therapeutic interventions among patients with breast cancer with pathogenic germline variants.
Authors: Kurian Allison W. , Nielsen Young Sarah M. , Russell Emily M. , Facio Flavia , Moretz Chad , Heald Brandie , Ellsworth Rachel , Pineda-Alvarez Daniel , Esplin Ed , Korn W. Michael , Tung Nadine M. 
Published: June 2024

10579 Background: Management of breast cancer (BC) patients with pathogenic germline variants (PGVs) can include risk-reducing bilateral mastectomy (BLM), PARP inhibitors (PARPi), enhanced breast imaging (MRI/tomosynthesis), risk-reducing salpingo-oophorectomy (RRSO) and pancreatic cancer surveillance (endoscopic ultrasound, EUS/MR cholangiopancreatography, MRCP). We assessed intervention uptake in >1,400 PGV-positive BC patients. Methods: Germline genetic testing (GGT) data (Invitae Corp.) and insurance claims data (Komodo Healthcare Map) were assembled for female patients with BC or DCIS diagnosed 2015-23, GGT <120 days from diagnosis, and >1 year of claims post-GGT. Inclusion/exclusion criteria followed prior work (PMID 32027353). Genes analyzed were those conferring high (>50%; BRCA1, BRCA2, CDH1, PALB2, PTEN, STK11, TP53) or moderate (20-50%; ATM, BARD1, CHEK2, NF1, RAD51C, RAD51D) BC risk. Multivariable logistic regression models of intervention use included PGV risk, race/ethnicity, age, family cancer history, local metastasis, type of insurance, geographic region and time from diagnosis to GGT. Chi-square tests were used to compare intervention uptake rates by PGV risk (Table). Results: 1,432 patients had >1 PGV in a high- (51%) or moderate-risk (49%) gene: 68% White, 32% lymph node involvement, 69% BC family history, 28% Medicare/Medicaid, mean (SD) age at testing: 51 (12) and mean (SD) days from diagnosis to GGT: 40 (26). The intervention with the highest uptake was BLM (60% in all PGV carriers). BLM, RRSO, and PARPi were significantly more frequent in high- vs. moderate-risk PGV carriers. In the multivariable model, patients with BLM uptake were younger (odds ratio, OR 0.9, 95% confidence interval, CI 0.9-1), more often had family cancer history reported (OR 2, 95% CI 1-3) and had high-risk PGV (OR 5, 95% CI 3-7). Patients with RRSO uptake had higher odds of pancreatic (OR 2, 95% CI 1-2) or ovarian (OR 2, 95% CI 1-3) cancer family history and high-risk PGV (OR 6, 95% CI 4-8) and lower odds of Medicare (OR 0.4, 95% CI 0.2-0.8). Conclusions: Overall, the uptake of interventions was higher in patients with high- compared to moderate-risk PGV. More research, however, is needed on the shared decision-making process, as uptake of BLM was relatively high in those with moderate-risk PGV, and uptake of surveillance measures was relatively low.[Table: see text]