Cancer Risk Model Research Articles

Trajectories of premorbid healthcare contact days in veterans with pancreatic cancer.

706 Background: Early detection of pancreatic cancer is a key clinical priority. Although prior work has evaluated machine learning models to identify at-risk patients for pancreatic cancer screening, this is limited by complexity and cost. "Contact days’’— days spent receiving healthcare outside the home for any reason or length of time—have previously been used to measure the ‘’time toxicity’’ of cancer care post-diagnosis. We sought to characterize trajectories of premorbid contact days, alongside standardized risk scores and healthcare costs to explore their use as potential markers for early pancreatic cancer diagnosis. Methods: We identified decedents with pancreatic adenocarcinoma diagnosed between 2012 and 2022 who had at least one primary care visit in the Veterans Affairs (VA) system in each of the two years prior to diagnosis using the VA Clinical Cancer Registry (VACCR) and the Corporate Data Warehouse (CDW). We grouped patients into two cohorts: (1) surgical (receipt of curative intent surgery), and (2) non-surgical and collected sociodemographic and clinical characteristics. During a 2-year premorbid period we calculated monthly contact days (24 data points) and plotted them as percentages (e.g., 3 contact days in a month=10%) and visualized their trajectory using cubic smoothing splines. We plotted trajectories for each sociodemographic and clinical subgroup and visually evaluated differences. We similarly plotted monthly Care Assessment Need (CAN) scores, a validated VA risk score identifying patients at high risk of hospitalization/death, and monthly VA healthcare costs. Results: We included 8,654 patients (649 surgical - median age 69 years, 74% white, 78% stage 1-2 and 8,005 non-surgical - median age 71 years, 72% white, 21% stage 1-2). For both groups, trajectories of contact days were similar. Baseline contact days (-24 to -12 months) were 7-8% (≈2 days/month). Contact days started to rise on average 9 months prior to diagnosis at an approximate rate of 1% per month, reaching 15-20% at diagnosis (≈5-6 days/month). In the surgical group, patients with a Charlson comorbidity score of ≥2, urban residence, and Black race had higher baseline contact days. Those <65 years and rural residence had a steeper slope of rise in contact days. CAN score trajectories closely mirrored contact day trajectories in both groups. Cost trajectories were similar in both groups (≈$1000/month at baseline), rose gradually to ≈$2000/month by -3 months, and then rose sharply to >$6000/month 2 months before diagnosis. Conclusions: In this decade-long national study of Veterans diagnosed with pancreatic cancer, we found that contact days and CAN scores, but not costs, start to rise 9 months prior to diagnosis. These data provide the first evidence supporting their further evaluation in pancreatic cancer risk models which may aid early diagnosis. The similar trajectories in patients who go on to receive curative-intent surgery or not may imply they may be markers for pancreas cancer diagnosis but perhaps not outcomes.

Effects of pyrolysis conditions on sewage sludge-biochar properties and potential risks based on PAH contents

Pyrolysis of sewage sludge can significantly reduce industrial waste while producing high-value biochar for soil improvement. This study aimed to evaluate the quality and safety of biochar from sewage sludge under different pyrolysis conditions. Optimal carbonization conditions (700°C, 60 minutes, 5°C/min) were identified by analyzing the physicochemical properties, elemental composition, structural characteristics, and the specific surface area of biochar. Results show that pyrolysis of waste sludge reduces the total content of priority polycyclic aromatic hydrocarbons (PAHs) by 48%, from 6367 ng/g to 3317 ng/g, mainly due to a reduction in low-molecular-weight compounds. The composition of polyarenes in biochars is represented primarily by low-molecular compounds, among which naphthalene and phenanthrene predominate. At the same time, among high-molecular compounds, fluoranthene, pyrene, and chrysene stand out, significantly dominating the overall picture. According to the Incremental Lifetime Cancer Risk model, the carcinogenic risks associated with biochar usage are primarily driven by hazardous compounds such as chrysene, benzo(a)pyrene, and dibenz(a,h)anthracene, evaluated through toxic equivalent concentrations. It was found that with oral or dermal exposure to these pollutants, the likelihood of cancer in children is 1.1-1.4 times higher than in adults. At the same time, with inhalation, this threat increases by 1.5 times for adults compared to children. However, with increased pyrolysis temperature, heating rate, and holding time of sewage sludge, the carcinogenic risks of biochar decrease. Biochar produced under optimal conditions contains PAH levels below toxic threshold standards set by the International Biochar Initiative. The safe application rate for biochar in Haplic Chernozem soils at 0–20 cm depth is up to 26 t/ha.

The Vienna CATScore for predicting cancer-associated venous thromboembolism: an external validation across multiple time points.

Patients with cancer undergoing systemic therapies have a high risk for venous thromboembolism (VTE). Risk assessment models were developed to select high-risk subgroups that might benefit from primary thromboprophylaxis, yet currently available models reportedly underperform in contemporary cancer treatment populations and risk models across multiple time points throughout therapy are not available. We, therefore, aimed to validate the Vienna CATScore, a nomogram-based model including tumor type and continuous D-dimer levels, in a prospective cohort study of patients initiating contemporary systemic anticancer therapies. The validity of the model was tested at study inclusion, 3 weeks, and 3 months after start of therapy. Overall, 598 patients were included [49% women, median age 62 years (interquartile range 53-70 years)]. Most patients had stage IV disease (68.2%). The 6-month cumulative incidence of VTE was 9.2% [95% confidence interval (CI) 6.8% to 11.5%]. The Vienna CATScore demonstrated good discriminatory ability (c-statistics: 0.69, 95% CI 0.61-0.76) at study baseline and across all evaluated time points (c-statistics: 0.68, 95% CI 0.63-0.73). Applying a 6-month predicted VTE risk threshold of 8%, the CATScore effectively distinguished between low- and high-risk groups at study inclusion (7.1% versus 15.1% observed VTE risk, P= 0.004) and across all three time points (6.3% versus 13.6% observed VTE risk, P < 0.001). Assuming a 50% risk reduction with thromboprophylaxis, this threshold resulted in a number needed to treat (NNT) of 13 and 15, respectively, in the high-risk group, while the NNT was 28 and 32, respectively, in the low-risk group. This external validation of the Vienna CATScore confirms its effectiveness in predicting VTE risk in the initial months of state-of-the-art systemic anticancer therapies and across multiple time points.

Lifetime Risks for Lung Cancer due to Occupational Radon Exposure: A Systematic Analysis of Estimation Components.

Lifetime risk estimates play a key role in many areas of radiation research. Here, the focus is on the lifetime excess absolute risk (LEAR) for dying from lung cancer due to occupational radon exposure based on uranium miners cohort studies. The major components in estimating LEAR were systematically varied to investigate the variability and uncertainties of results. Major components of the LEAR calculation are baseline mortality rates for lung cancer and all causes of death, risk model and exposure scenario. Sex-averaged mortality rates were chosen from a mixed Euro-American-Asian population, in addition to mortality rates to represent heavy and light smokers. Seven radon-related lung cancer risk models derived from different uranium miners cohorts were compared. As exposure scenarios, occupational exposure of two working level months (WLM) from age 18-64 years was considered, and three scenarios from the German uranium miners cohort. Further components were modified in sensitivity analyses. The LEAR was compared to other lifetime risk measures. With a range from less than 0.6 × 10-4 to over 8.0 × 10-4, LEAR per WLM estimates were influenced heavily by the choice of risk models. Notably, mortality rates, particularly lung cancer mortality rates, had a strong impact on LEAR per WLM across all models. The LEAR per WLM exhibited only low variation to changes in exposure scenarios for all risk models, except for the BEIR VI model fitted on the pooled 11 miners study. All assessed lifetime risk measures displayed a monotonically increasing relationship between exposure and lifetime risk at low to moderate exposures, with minor differences between ELR, REID, and LEAR (all per WLM). RADS yields the largest lifetime risk estimates in most situations. There is substantial variation in LEAR per WLM estimates depending on the choice of underlying calculation components. Reference populations and mortality rates should be selected with care depending on the application of lifetime risk calculations. The explicit choice of the lifetime risk measure was found to be negligible. These findings should be taken into consideration when using lifetime risk measures for radiation protection policy purposes.

The Society of Thoracic Surgeons General Thoracic Surgery Database: 2024 Update on Outcomes and Research.

The Society of Thoracic Surgeons General Thoracic Surgery Database (STS GTSD) remains the largest and most comprehensive audited thoracic surgical database in the world. As the STS GTSD grows to nearly 1 million cases, the pulmonary resection for cancer and esophagectomy short-term risk models have been refined to provide participants with benchmarked performance reports to facilitate quality improvement efforts. New for 2025 will be the development of long-term risk models and the online release of both short- and long-term risk calculators. A voluntary module to collect neoadjuvant targeted and immunotherapy data has been created has been accepted by participants and is rapidly accruing data. STS GTSD participant public reporting has increased 50% over the last 2 years following the application of the US News and World Report 3% transparency credit. All GTSD data analyses are now performed internally by the STS Research and Analytic Center, resulting in multiple publications through the Access & Publication, Task Force on Funded Research, and Participant User File mechanisms. Future initiatives include the incorporation of patient-reported outcomes into the STS GTSD, revision of the data collection form to incorporate variables associated with long-term outcomes, and focused efforts to increase the value of STS GTSD participation. This report delineates volume trends, recent initiatives, and the prolific research output emanating from the STS GTSD, reflecting a year of substantial progress and academic productivity.

Investigating the added value of incorporatingmammographic density to an integrated breastcancer risk model with questionnaire-based riskfactors and polygenic risk score.

Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 cases, 19,104 controls): US-based Nurses' Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50-70 years in US and Sweden. For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without density; and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) among older women. The model with density identified 18.4% of US women 50-70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases. Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.

Development and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms.

Current image-based long-term risk prediction models do not fully use previous screening mammogram images. Dynamic prediction models have not been investigated for use in routine care. We analyzed a prospective WashU clinic-based cohort of 10,099 cancer-free women at entry (between November 3, 2008 and February 2012). Follow-up through 2020 identified 478 pathology-confirmed breast cancers (BCs). The cohort included 27% Black women. An external validation cohort (Emory) included 18,360 women screened from 2013, followed through 2020. This included 42% Black women and 332 pathology-confirmed BC excluding those diagnosed within 6 months of screening. We trained a dynamic model using repeated screening mammograms at WashU to predict 5-year risk. This opportunistic screening service presented a range of mammogram images for each woman. We applied the model to the external validation data to evaluate discrimination performance (AUC) and calibrated to US SEER. Using 3 years of previous mammogram images available at the current screening visit, we obtained a 5-year AUC of 0.80 (95% CI, 0.78 to 0.83) in the external validation. This represents a significant improvement over the current visit mammogram AUC 0.74 (95% CI, 0.71 to 0.77; P < .01) in the same women. When calibrated, a risk ratio of 21.1 was observed comparing high (>4%) to very low (<0.3%) 5-year risk. The dynamic model classified 16% of the cohort as high risk among whom 61% of all BCs were diagnosed. The dynamic model performed comparably in Black and White women. Adding previous screening mammogram images improves 5-year BC risk prediction beyond static models. It can identify women at high risk who might benefit from supplemental screening or risk-reduction strategies.

Liver Function Biomarkers and Lung Cancer Risk: A Prospective Cohort Study in the UK Biobank.

As the primary organ of metabolism and detoxification, the liver may contribute to the pathogenesis of lung cancer. We aimed to illuminate the intricate link between liver function biomarkers and lung cancer risk, as well as delineate the role of smoking behavior within this association. We investigated the associations of seven liver function biomarkers levels (alkaline phosphatase [ALP], alanine transaminase [ALT], total bilirubin [TBIL], albumin [ALB], gamma-glutamyltransferase [GGT], aspartate transaminase [AST], and total protein [TP]) with lung cancer risk across the UK Biobank (N = 337 499) through restricted cubic splines and Cox proportional hazards models. Moreover, Mendelian randomization (MR) was utilized to evaluate the causal effect of smoking behavior on these biomarkers. Then a lung cancer risk prediction model was developed among smokers by backward stepwise logistic regression. During a median follow-up of 13.3 years, 3003 lung cancer cases were identified. We found ALP levels positively associated with lung cancer risk, whereas ALT, TBIL, ALB, and AST were inversely correlated; TP exhibited a U-shaped association, whereas GGT displayed a mirrored J-shaped relationship. These associations were amplified among smokers. MR analysis indicated that smoking behavior could increase ALP (odds ratio [OR]: 1.05) and GGT (OR: 1.15) levels while decreasing TBIL (OR: 0.92), ALB (OR: 0.92), and TP (OR: 0.96) levels. The lung cancer risk model incorporating these biomarkers in smokers demonstrated robust discrimination. Our finding provides perspectives and evidences towards the intricate crosstalk between the hepatic and pulmonary systems, as well as the processes through which tobacco catalyzes lung carcinogenesis.

Nomogram prediction model for gastric cancer risk in chronic atrophic gastritis: Role of blood cell ratios

Nomogram prediction model for gastric cancer risk in chronic atrophic gastritis: Role of blood cell ratios

Calculating Future 10-Year Breast Cancer Risks in Risk-Adapted Surveillance: A Method Comparison and Application in Clinical Practice.

The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has successfully implemented risk-adapted breast cancer surveillance for women at high breast cancer risk in Germany. Women with a family history of breast and ovarian cancer but without pathogenic germline variants in recognized breast cancer risk genes are recommended annual breast imaging if their predicted 10-year breast cancer risk is 5% or higher, using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk model, as outlined in the current GC-HBOC guideline. However, women who initially do not meet this risk threshold may do so later, even if there is no new cancer in their family. To determine when this threshold is crossed, one could annually repeat BOADICEA calculations using an aging pedigree: the "prediction by aging pedigree" (AP) approach. Alternatively, we propose a simplified and more practical "'conditional probability" (CP) approach, which calculates future risks based on the initial BOADICEA assessment. Using data from 6,661 women registered with GC-HBOC, both methods were compared. Initially, 74% of women, ages 30 to 48 years, had a 10-year breast cancer risk below 5%, but 53% exceeded this threshold at an older age based on the AP approach. Among the women with an initial risk below the threshold, the CP approach revealed that 99% of women exceeded the 5% threshold at the same or an earlier age compared with the AP approach (88% of cases were within the same year or 1 year earlier). The CP approach has been implemented as a user-friendly web application. Prevention Relevance: The German Consortium for Hereditary Breast Cancer recommends annual breast imaging for women if their 10-year breast cancer risk is 5% or higher. Women who initially do not meet this risk threshold may do so later. We propose a simple method to determine future risks based on initial risk assessments.

The Role of Age in Predictive Models for Colorectal Cancer Risk: Insights From a Community-Based Real-World Study

The Role of Age in Predictive Models for Colorectal Cancer Risk: Insights From a Community-Based Real-World Study

The influence of daily imaging and target margin reduction on secondary cancer risk in image-guided and adaptive proton therapy

Objective. Image-guided and adaptive proton therapy rely on daily CBCT or CT imaging, which increases radiation dose and radiation-induced cancer risk. Online adaptation however also reduces setup uncertainty, and the additional risk might be compensated by reducing the setup robustness margin. This work developed a framework to investigate how much this robustness margin should be reduced to offset the secondary cancer risk from additional imaging dose and applied it to proton therapy for head-and-neck cancer.Approach. For five patients with head-and-neck cancer, voxel-wise CT and CBCT imaging doses were estimated with Monte Carlo radiation transport simulations, calibrated with air and PMMA phantom measurements. The total dose of several image-guided and adaptive treatments protocols was calculated by summing the planning CT dose, daily and weekly CBCT or CT dose, and therapy dose, robustly optimized with setup margins between 0 and 4 mm. These were compared to a reference protocol with 4 mm setup margin without daily imaging. All plans further used 3% range robustness. Organ-wise excess absolute risk (EAR) of cancer was calculated with three models to determine at which setup margin the total EAR of image-guided and adaptive treatment protocols was equal to the total EAR of the reference.Results. The difference between the simulated and measured CT and CBCT doses was within 10%. Using the Monte Carlo models, we found that a 1 mm setup margin reduction was sufficient for most patients, treatment protocols, and cancer risk models to compensate the additional risk imposed by daily and weekly imaging. For some protocols, even a smaller reduction sufficed, depending on the imaging frequency and type. The risk reduction by reducing the margin was mainly due to reducing the risk for carcinomas in the brain and, for some patients, the oral cavity.Significance. Our framework allows to compare an increased imaging dose with the reduced treatment dose from margin reductions in terms of radiation-induced cancer risk. It is extendable to different treatment sites, modalities, and imaging protocols, in clinic-specific or even patient-specific assessments.

Estimation of Radiation Equivalent Dose and Lifetime Attributable Risk from Pediatric CAP CT Examination

Aim: This study aims to estimate equivalent doses (EqDs) and life attributable risks (LARs) for pediatric patients who underwent chest–abdominal–pelvic (CAP) CT examinations in Madinah, Saudi Arabia. Methodology: This retrospective study collected data from 120 pediatric patients who underwent CAP CT examinations. The data were categorized by the age and gender of the pediatric patients. Then, the EqDs were computed using the NCICT (National Cancer Institute dosimetry system for computed tomography) program, and LARs were estimated from the equivalent dose (EqD) results using age- and gender-specific cancer risk models found in the Committee on the Biological Effects of Ionizing Radiation (BEIR) VII Phase 2 (2006). Results: The EqD range was 0.9 to 7.55 mSv for the prostate and colon (males and females), respectively. LARs for female breast and lung cancers were considered to have the highest values among the age groups. Nevertheless, LARs of the colon, liver, and leukemia cancers were higher for males than females. The LAR range of cancer incidence was 0.6 to 63.1 per 100,000 cases for prostate (aged 10–≤15 years) and breast (females aged 1≤–&lt;5 years), respectively. The LAR range of cancer mortality was 0.1 to 41.9 per 100,000 cases for prostate (aged 10–≤15 years) and lung (females aged 1≤–&lt;5 years). Conclusions: LARs of all cancer incidence and mortality from CAP CT examination were higher for pediatric females than males (with an average of 54%). This highlights the importance of considering pediatric patient gender and implementing optimization and protective measures in CAP CT examinations. LARs of breast and lung (for females) and colon (for males) cancers were found to have the highest values among the age groups. However, LARs of cancer incidence and mortality for colon, liver, and leukemia for males were higher than those for females.

Abstract C150: Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort

Abstract Introduction: Breast cancer is the second leading cause of cancer death in women after lung cancer in the United States. While early detection is critical to reducing morbidity and mortality, predication of individualized risk is important in prevention and could also reduce unnecessary costs related to screening. The Susan G. Komen Tissue Bank (KTB) at Indiana University is the only tissue bank in the world that provides breast tissue specimens donated by healthy individuals to further to breast cancer research; however, as donors are volunteers, it is not clear how representative they are with respect to breast cancer risk compared to the general population. Methods: In this study, we calculated Breast Cancer Risk Assessment scores using Tyrer Cuzick (TCZ) and the Breast Cancer Risk Assessment Tool (The Gail Model, GM) in the KTB cohort by race and ethnicity. Recruited between 2007 and 2023, the participants completed a questionnaire encompassing their demographics, personal health and reproductive history, family history, and modifiable risk factors like smoking and alcohol use. In both models, participants with a lifetime risk of breast cancer of more than 15% were classified as having higher than average risk, while those with a risk of less than 15% were classified as having an average risk. Data analysis was performed using SAS v.9.4 software. Statistical significance was determined by p-values &amp;lt;0.05. Results: Out of 5118 participants, 206 with prior breast cancer and 37 male donors were excluded, resulting in a final sample of 4875 participants (aged 18–90) for analysis. The cohort was predominantly non-Hispanic white (NHW, 69.73%), followed by non-Hispanic black (NHB, 16.7%), Hispanic (8. 6%), and Asian individuals (3.3%), with 1.8% reporting multiracial or other race. The GM consistently categorized fewer women as having higher than average risk compared to the TZM. Across both TCZ and GM, the percentages of individuals classified as higher than average risk was greatest for NHW women, with 29.2% being categorized as higher than average risk in the TCZ, compared to 21.0% in the GM. The greatest discordance between the models was seen in NHB women, with the TCZ estimating 25.2% were at greater than average risk, and the GM only reporting 3.7% to be at greater than average risk. Overall, risk assessment scores varied across racial and ethnic groups (p- value&amp;lt;0.001). Factors driving these differences will be presented in additional analyses. Conclusion: There are known limitations in breast cancer risk assessment models by race and ethnicity that were confirmed in the women who comprise the KTB cohort. Donors to the KTB are generally representative of the US population with respect to breast cancer risk. As the KTB cohort continues to grow and mature, it can be utilized to assess breast cancer risk models for various racial and ethnic groups to assist in the identification of high-risk individuals that may benefit from prevention strategies and targeted screening. Citation Format: Vidya Patil, Michele L. Cote, Jenny Cui. Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C150.

Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk.

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited. We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium. The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population. These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification. Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.

Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis

Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts. We analysed 240 137 participants aged 45-80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London-Death (UCLD), the University College London-Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59-0·77) to 0·83 (0·78-0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%-79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON. Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria. US National Cancer Institute, l'Institut National du Cancer, Cancer Research UK.

1193P Ethical lung cancer screening selection? Comparison of the European 4 - In The Long Run (4ITLR) criteria with the 2021 USPSTF (US), and HUNT lung cancer risk model in a large Norwegian prospective population-based study

1193P Ethical lung cancer screening selection? Comparison of the European 4 - In The Long Run (4ITLR) criteria with the 2021 USPSTF (US), and HUNT lung cancer risk model in a large Norwegian prospective population-based study

Ovarian Cancer Risk Prediction - a Clinical Epidemiology Perspective.

Ovarian cancer is a rare and highly heterogeneous disease usually detected at late stages when outcomes are poor. Population-based screening approaches have not been successful at reducing ovarian cancer mortality, but preventive bilateral salpingo-oophorectomy is highly effective at preventing ovarian cancer in high-risk populations. Ovarian cancer risk prediction models may allow identification of populations at increased risk of ovarian cancer for preventive interventions or targeted early detection. We propose a life-course approach to ovarian cancer risk prediction based on the time at which a risk model should be applied and the risk factors that are available. The discriminative ability of ovarian cancer risk prediction models published so far is limited, with areas under the curve ranging from 0.58-0.65 for different combinations of risk factors and genetic susceptibility markers. Currently proposed absolute risk thresholds for preventive surgery are around 4% lifetime risk. The absolute risk predicted by ovarian cancer risk models ranges from 0.6-2.5% lifetime risk in the general population, highlighting the need to improve ovarian cancer risk prediction models and evaluating new preventive approaches that can be offered to individuals at lower risk.

A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system

Objective. To allow the estimation of secondary cancer risks from radiation therapy treatment plans in a comprehensive and user-friendly Monte Carlo (MC) framework. Method. Patient planning computed tomography scans were extended superior-inferior using the International Commission on Radiological Protection’s Publication 145 computational mesh phantoms and skeletal matching. Dose distributions were calculated with the TOPAS MC system using novel mesh capabilities and the digital imaging and communications in medicine radiotherapy extension interface. Finally, in-field and out-of-field cancer risk was calculated using both sarcoma and carcinoma risk models with two alternative parameter sets. Result. The TOPAS MC framework was extended to facilitate epidemiological studies on radiation-induced cancer risk. The framework is efficient and allows automated analysis of large datasets. Out-of-field organ dose was small compared to in-field dose, but the risk estimates indicate a non-negligible contribution to the total radiation induced cancer risk. Significance. This work equips the TOPAS MC system with anatomical extension, mesh geometry, and cancer risk model capabilities that make state-of-the-art out-of-field dose calculation and risk estimation accessible to a large pool of users. Furthermore, these capabilities will facilitate further refinement of risk models and sensitivity analysis of patient specific treatment options.

Early identification of people at high risk of oral cancer-A review of existing risk prediction models.

Prediction plays a ubiquitous role in cancer care. At every stage of the illness, the patient, the physician, and the family must make numerous decisions. Utilizing epidemiological, clinical, biological, lifestyle, and genetic factors, a cancer-specific risk assessment model calculates the likelihood of developing cancer. In India, oral cancer ranks as the fourth most common cancer, affecting nearly 3,000,00 individuals annually. Because it is in the premalignant stage, oral cancer is easily detectable in the oral cavity. Prompt identification of this lesion can result in better outcomes and a higher standard of living. Advanced statistical techniques have been used to develop prediction algorithms or risk scores that identify individuals with a high risk of developing oral cancer. With the aid of these risk assessment models, specific individuals can be screened to aid in the early detection of the disease, which may result in better outcomes and lifestyle modifications. Finding the best model among the current risk models for oral cancer may be aided by a thorough examination of all these models. Finding and assessing the risk model that primary care physicians can use and easily apply in clinical practice will be made easier with a succinct and straightforward comparison of the models. This review compares the current models to determine which has the best performance metrics, which could lead to a better understanding of the advantages and disadvantages of various risk prediction models of oral cancer.