Future Cancer Risk Research Articles

Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance. Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness. We also evaluated the cumulative effect of these parameters by combining the best- and the worst-performing parameters. A subanalysis compared Sybil's performance by CT manufacturer. We considered any LDCT positive if future lung cancer was subsequently confirmed by biopsy or surgical resection. The areas under the curve (AUCs) for each series pair were compared using DeLong's test. There was no difference in Sybil's performance between 1049 pairs of standard versus bone reconstruction filter (AUC at 1 year 0.84 [95% confidence interval (CI): 0.70-0.99] vs 0.86 [95% CI: 0.75-0.98], P = 0.87) and 1961 pairs of standard versus lung reconstruction filter (AUC at 1 year 0.98 [95% CI: 0.97-0.99] vs 0.98 [95% CI: 0.96-0.99], P = 0.81). Similarly, there was no difference in 1288 pairs comparing 2-mm versus 5-mm axial slice thickness (AUC at 1 year 0.98 [95% CI: 0.94-1.00] vs 0.99 [95% CI: 0.97-0.99], P = 0.68). The best-case scenario combining a lung reconstruction filter with 2-mm slice thickness compared with the worst-case scenario combining a bone reconstruction filter with 2.5-mm slice thickness uncovered a significantly different performance at years 2-4 (P = 0.03). Subanalysis showed no significant difference in performance between Siemens and Toshiba scanners. Sybil's predictive performance for future lung cancer risk is robust across different reconstruction filters and axial slice thicknesses, demonstrating its versatility in various imaging settings. Combining favorable reconstruction parameters can significantly enhance predictive ability at years 2-4. The absence of significant differences between Siemens and Toshiba scanners further supports Sybil's versatility.

Body composition as a biomarker for assessing future lung cancer risk.

To investigate if body composition is a biomarker for assessing the risk of developing lung cancer. Low-dose computed tomography (LDCT) scans from the Pittsburgh Lung Screening Study (PLuSS) (n=3,635, 22 follow-up years) and NLST-ACRIN (n=16,435, 8 follow-up years) cohorts were used in the study. Artificial intelligence (AI) algorithms were developed to automatically segment and quantify subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. Cause-specific Cox proportional hazards models were used to evaluate the hazard ratios (HRs). Standard time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the prognostic ability of different models over time. The final composite models were formed by seven variables: age (HR=1.20), current smoking status (HR=1.59), bone volume (HR=1.79), SM density (HR=0.29), IMAT ratio (HR=0.33), IMAT density (HR=0.56), and SAT volume (HR=0.56). The models trained on the PLuSS cohort achieved a mean AUC of 0.76 (95% CI: 0.74-0.79) over 21 follow-up years and 0.70 (95% CI: 0.66-0.74) over the first 7 follow-up years for predicting lung cancer development within the PLuSS cohort. In contrast, models trained on the PLuSS cohort alone, as well as in combination with the NLST cohorts, achieved an AUC ranging from 0.61 to 0.68 in the NLST cohort over a 7-year follow-up period. Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency. Body composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency. This study unveils the significant associations between body tissues and lung cancer risk.The prediction models based on body composition alone, as well as the combination of demographics and body composition features can effectively identify patients at higher risk of developing lung cancer.

7609 Androgen Receptor Positive DCIS in a Transmasculine Individual on Testosterone Therapy

Abstract Disclosure: J. Kelly: None. B. Cohen: None. Introduction: The rate of breast cancer in transgender men is lower than in cisgender women, perhaps due to lower estrogen levels in the setting of suppression from exogenous testosterone, lack of clear guidelines on screening after top surgery, and increase in mastectomies. However, the impact of testosterone therapy on existing breast cancer is not known. Concerns regarding use of testosterone in patients with breast cancer include the potential for conversion of testosterone to estradiol, as well as direct androgen receptor (AR) stimulation. However, for some patients, hormone therapy is lifesaving and stopping gender affirming hormone therapy (GAHT) will negatively impact patient well-being. This creates a challenging clinical scenario when transgender patients on testosterone are diagnosed with breast cancer.Case Presentation: We present a case of a 29-year-old self-identified transmasculine nonbinary individual (assigned female at birth) who underwent chest reconstruction surgery after several months on daily transdermal testosterone therapy. A 4 cm DCIS ER+/AR+ with possible micro invasion was incidentally noted on pathology. They had no family history of breast cancer. The patient underwent chest MRI which was negative for persistent disease. The treating oncologist did not recommend Tamoxifen. The patient chose to discontinue testosterone therapy due to uncertainty regarding future breast cancer risk. However, due to worsening depression and fatigue, the patient chose to resume GAHT after receiving counseling of possible risk, though likely low, of cancer recurrence. Given that they had undergone bilateral mastectomy which, unlike chest reconstruction surgery where some breast tissue is left for contouring, all breast tissue was removed and given that chest MRI was negative for disease, cancer recurrence was felt to be low. Furthermore, though AR positivity was noted, the clinical implications are unclear and it is not known whether testosterone would stimulate tumor growth or potentially have antagonistic effects on ER signaling. Additionally, any stimulatory effects of AR signaling may be balanced by androgen suppression of gonadotroph production and reduction in estrogen production; this is more clearly associated with stimulatory effects on cancer growth. And finally, the potential risks of therapy must be weighed against the benefits provided to the patient’s well-being and mental health. Ultimately, the patient resumed testosterone therapy and has been doing well.Teaching Point: This case illustrates the uncertainties regarding the management of gender affirming testosterone therapy in patients with DCIS or breast cancer. It also highlights the need for a patient centered approach to decisions regarding changes to GAHT, to appropriately weigh the risks and benefits of continuing therapy. Presentation: 6/2/2024

Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study

Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors. In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir-ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores-the current clinical gold standard for breast cancer risk prediction-for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer). 4382 female donors (median age at donation 45 years [IQR 34-57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7-11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10-2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36-0·88]; p=0·013). For the other tissue compartments and the RS model, no significant associations were found (except for stromal tissue via the IR model, had higher odds of developing breast cancer [OR 1·59, 1·03-2·49]). Individuals with both of the adipose risk factors had an OR of 3·32 (1·68-7·03; p=0·0009). Participants with 5-year Gail scores above the median had an OR for development of cancer of 2·33 (1·46-3·82; p=0·0012) compared with those with scores below the median. When combining Gail scores with our adipose AAD risk model, we found that individuals with both of these predictors had an OR of 4·70 (2·29-10·90; p<0·0001). When combining the Gail score with our adipose IR model, we found that individuals with both predictors had an OR of 3·45 (1·77-7·24; p=0·0002). Assessment of senescence-associated nuclear morphologies with deep learning allows prediction of future cancer risk from normal breast biopsy samples. The combination of multiple models improved prediction of future breast cancer compared with the current clinical benchmark, the Gail model. Our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols. Novo Nordisk Foundation, Danish Cancer Society, and the US National Institutes of Health.

Impact of Cardio-Ankle Vascular Index on Future Cancer in Patients With Coronary Artery Disease.

Cardiovascular risk factors are associated with increased risk of future cancer. However, the relationship between quantitative parameters of atherosclerosis and future cancer risk is unclear. A total of 1,057 consecutive patients with coronary artery disease was divided into 2 groups according to the cutoff value of the cardio-ankle vascular index (CAVI) derived by receiver operating characteristic curve analysis: low CAVI group (CAVI <8.82; n=487), and high CAVI group (CAVI ≥8.82; n=570). Patients in the high CAVI group were older and had a higher prevalence of diabetes, chronic kidney disease, anemia and history of stroke compared with patients in the low CAVI group. There were 141 new cancers during the follow-up period. The cumulative incidence of new cancer was significantly higher in the high CAVI group than in the low CAVI group (P=0.001). In a multivariate Cox proportional hazard analysis, high CAVI was found to be an independent predictor of new cancer diagnosis (hazard ratio 1.62; 95% confidence interval 1.11-2.36; P=0.012). In the analysis of individual cancer types, high CAVI was associated with lung cancer (hazard ratio 2.85; 95% confidence interval 1.01-8.07; P=0.049). High CAVI was associated with the risk of future cancer in patients with coronary artery disease.

Incidence of rectal cancer after colectomy for inflammatory bowel disease: nationwide study.

Inflammatory bowel disease increases the risk of colorectal neoplasia. A particular problem arises in patients who have undergone subtotal colectomy leaving a rectal remnant. The risk of future rectal cancer must be accurately estimated and weighed against the risks of further surgery or surveillance. The aim of this study was to estimate the 10-year cumulative incidence of rectal cancer in such patients. A nationwide study using England's hospital administrative data was performed. A cohort of patients undergoing subtotal colectomy between April 2002 and March 2014 was identified. A competing risks survival analysis was performed to calculate the cumulative incidence of rectal cancer. The effect of the COVID-19 pandemic on endoscopic surveillance was investigated using time-trend analysis. A total of 8120 patients were included and 61 patients (0.8%) were diagnosed with cancer. The cumulative incidence of rectal cancer was 0.26% (95% c.i. 0.17% to 0.39%), 0.49% (95% c.i. 0.36% to 0.68%), and 0.77% (95% c.i. 0.57% to 1.02%) at 5, 10, and 15 years respectively. A previous diagnosis of colonic dysplasia (HR 3.34, 95% c.i. 1.01 to 10.97; P = 0.047), primary sclerosing cholangitis (HR 5.42, 95% c.i. 1.34 to 21.85; P = 0.018), and elective colectomy (HR 1.83, 95% c.i. 1.11 to 3.02; P = 0.018) was associated with an increased incidence of rectal cancer. Regarding endoscopic surveillance, there was a 43% decline in endoscopic procedures performed in 2020 (333 procedures) compared with 2019 (585 procedures). The incidence of rectal cancer after subtotal colectomy is low. Asymptomatic patients without evidence of rectal dysplasia should be carefully counselled on the possible benefits and risks of prophylactic proctectomy.

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.

Are Welsh primary schools Sunproofed? Results of a national survey Part 2: sun protection practices in primary schools in Wales

Abstract Background Skin cancer rates are on the rise globally. School sun safety programmes are recommended by the World Health Organisation to reduce the risk of future skin cancer at population level; however, these are encouraged but not mandated in Wales. Objectives To explore current sun protection practices and sun safety education in primary schools in Wales and whether these are linked to the existence of a formal sun safety policy. Methods An online survey to all 1241 Welsh primary schools asking about sun safety practices, education and formal policies. Results 471 (38.0%) schools responded with the profile of responding schools generally matching the profile of schools in Wales. A minority (22,4.7%) of responding schools reported they had sufficient shade for most activities. In the spring and summer terms almost two thirds of schools encourage hat wearing (304, 64.8%) and sunscreen (296, 63.2%). While nearly all schools reported that parents were encouraged to apply sunscreen to students before school (449, 95.7%), there was wide variation in other sunscreen application practices. Less than one third of schools (129, 29.0%) reported that they include sun protection education in the curriculum in every year group, with 11.7% (52) including this in certain years only. Schools with a formal policy were more likely to report more comprehensive sun protection practices including having sufficient shade [OR 1.51, 95% CI 1.04-2.19; p = 0.032], having spare hats for pupils to wear [OR 1.59, 95% CI 1.07-2.37; p = 0.023], providing guidance for staff [OR 5.87, 95% CI 3.05-11.28; p &amp;lt; 0.001], encouraging them to model sun safe behaviours [OR 1.82, 95% CI 1.18-2.80; p = 0.007] and teaching sun protection education as part of the curriculum in every year group [OR 2.56, 95% CI 1.76-3.71; p &amp;lt; 0.001]. With respect to sunscreen, the existence of a formal policy did not seem to affect a school’s practice. Conclusions While in most cases, the existence of a formal policy suggests more comprehensive sun protection practices and education in schools, sun protection measures and education need improvement across the primary school sector in Wales to reverse rising skin cancer rates.

Extent of investigation and management of cases of ‘unexplained’ mismatch repair deficiency (u-dMMR): a UK Cancer Genetics Group consensus

BackgroundMismatch repair deficiency (dMMR) is a characteristic feature of cancers linked to Lynch syndrome. However, in most cases, it results from sporadic somatic events rather than hereditary factors. The term...

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Exploring perceptions of and attitudes towards tanning with school children, parents/carers and educators in Wales: A mixed methods study protocol for the SunChat study.

Skin cancer comprises half of all cancers in England and Wales. Most skin cancers can be prevented with safer sun exposure. As over exposure as a child can greatly increase future skin cancer risk, early and accessible sun safety education and promotion of sun safe behaviours is critical. Scientists agree there is no such thing as a 'safe tan', yet the public, including children, often have positive perceptions of tanned skin. To protect against future skin cancer, it is important to understand and address these misconceptions. The Curriculum for Wales with its area for Health and Well-being, and autonomy for schools in designing curriculum content, presents an ideal way to facilitate this exploration. Gather data regarding perceptions towards tanning to explore the perceived effects of a tan on health.Inform the development and testing of an educational toolkit for integration within the Curriculum for Wales to encourage positive health behaviours and attitudes of school children towards tanning and sun exposure. SunChat is a mixed methods exploratory study comprising three work streams: Workshops with school children to understand their perceptions on tanning.An online multiple-choice survey with parents/carers to understand perceptions, attitudes and behaviours towards tanning both for themselves and their children.An informal focus group with primary school educators to explore challenges in engaging with the school community around the Health and Well-being Area in the Curriculum for Wales. To date, there has been no work in Wales exploring children's, parents/carers', and educators' perceptions of tanning and how healthier attitudes can be encouraged. This study will engage with participants to scope current perceptions on tanning and the perceived effects tanning has on health. Findings will feed into future toolkit and curriculum development for health in schools in Wales and beyond.

Improving lung cancer health equity by applying deep learning to low dose CT screening of minority and disadvantaged patients.

1584 Background: In the US, disparities in lung cancer mortality exist for African American, Hispanic, and other minorities. Standard of care low dose CT screening (LDCT) detects early-stage disease and improves mortality, yet disparities are perpetuated in screening by eligibility criteria derived from cohorts underrepresenting these minorities. One such cohort is the National Lung Screening Trial (NLST) cohort which is 92% White. Consequently, guidelines for lung cancer screening may be insufficient to address the unique needs of diverse populations. We hypothesize that Artificial Intelligence prediction of future lung cancer risk from an individual’s LDCT can partially mitigate racial and ethnic disparities and improve health system practice guidelines by individualizing screening risk as compared to current general guidelines. Here, we benchmark a Resnet18 3D neural network trained on NLST LDCT images, Sybil, on the diverse patient population of the University of Illinois Health system (UIH) which is 20% White and 60% African American. Methods: A real-world cohort from UIH consisting of 1,450 CT studies was evaluated alongside 60,378 CT studies from the NLST cohort. All CT studies evaluated by the model were not used in model training. Using Youden’s J index as a probability cutoff, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were evaluated. Receiver operating characteristic (ROC) and precision-recall (PR) curves were generated to assess model performance between cohorts. NLST data were truncated to achieve equivalent incidence of lung cancer with UIH when generated PR curves. Results: Multi-year prediction performance (ROC-AUC and PR-AUC) between cohorts are summarized (Table). For prediction of lung cancer within 1-year of LDCT in the UIH cohort, the model respectively demonstrated sensitivity, specificity, positive predictive value, and negative predictive value among White (0.80, 0.77, 0.54, 0.92), African American (0.84, 0.78, 0.39, 0.97) races and Hispanic (0.75, 0.73, 0.60, 0.84) and non-Hispanic (0.87, 0.77, 0.42, 0.97) ethnicities. Conclusions: Model performance was similar between the NLST (92% White) and a diverse, real-world cohort at UIH (20% White) though decreases in ROC-AUC performance in year 1 predictions and may be due to insufficient representation of minority populations during model training. Prospective studies involving larger and more representative patient populations should be conducted to further optimize the model and evaluate its clinical utility to improve lung cancer health equity in minority populations.[Table: see text]

Abstract GS01-06: Advancing Evidence of the Associations Between Specific Benign Breast Diagnoses and Future Breast Cancer RIsk

Abstract Background: Benign breast disease (BBD) is a common breast biopsy finding encompassing a diverse spectrum of diagnoses. Breast cancer risk associated with many specific BBD diagnoses and their joint associations with breast density have not been extensively studied. We estimate the future risk of invasive breast cancer associated with specific BBD diagnoses typically categorized into the broad category of proliferative lesions without atypia (PWoA) and evaluate whether associations differ by breast density or by presence of calcifications. Methods: We included 1,313,943 women ages 40-79 years who underwent 5,282,063 mammograms in the Breast Cancer Surveillance Consortium from 1996 to 2019 with no prior history of breast cancer. We fit a Cox proportional hazards model to estimate hazard ratios associated with each combination of PWoA BBD diagnoses, whether calcifications were present on biopsy, and breast density. We used classification trees to group combinations of PWoA diagnoses, breast density, age groups, and presence of calcifications with similar magnitudes of associations with 5-year risk of invasive breast cancer. Results: Compared with women with a PWoA BBD diagnosis without calcifications, presence of calcifications significantly elevated risk for breast cancer (HR=1.19, 95% CI = 1.02 to 1.41, P=0.03). Considering specific PWoA diagnoses, classification trees identified groups of women with higher and lower invasive cancer risk than expected risk for all PWoA combined. Among women ages 60-79 years with heterogeneously or extremely dense breasts, the 2.6% of women with any PWoA diagnosis, except papillomatosis, were at high invasive breast cancer risk (5-year risk &amp;gt;2.5%) (Table 1). Among women ages 40-59 years with dense breasts, presence of calcifications with papillomas, usual ductal hyperplasia, columnar cell hyperplasia, or PWoA NOS diagnoses put these women at high risk (5-year risk &amp;gt;2.5%) compared to women with the same BBD diagnoses without calcifications who were at intermediate or average risk (1-2.49%) (Table 1). Among women ages 60-79 years with non-dense breasts, the 1.6% of women with papilloma, usual ductal hyperplasia, radial scar, or PWoA NOS diagnoses were at high breast cancer risk (Table 2). Among women ages 40-59 years with non-dense breasts, presence of calcifications with usual ductal hyperplasia or PwoA NOS diagnoses put these women at intermediate risk (1.67-2.49%) compared to women with these BBD diagnoses without calcifications who were at average risk (1-1.66%) (Table 2). Women 60-79 years with fatty breasts were at intermediate 5-year risk if they had papillomas, usual ductal hyperplasia, or PWoA NOS diagnoses. Women 40-59 years with fatty breasts were at low risk of breast cancer regardless of PWoA diagnosis (Table 2). Conclusion: Specific BBD diagnoses and the presence of calcifications can change a woman’s predicted 5-year breast cancer risk compared to broad BBD categories alone. This information could be incorporated into risk prediction models to improve model accuracy. Citation Format: Olivia Sattayapiwat, Karla Kerlikowske, Donald Weaver, Alexander Borowsky, Theresa Keegan, Brian Sprague, Diana Miglioretti. Advancing Evidence of the Associations Between Specific Benign Breast Diagnoses and Future 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 GS01-06.

Abstract PO1-20-01: A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery

Abstract Background: The number of individuals who identify as transgender and/or gender-diverse (TGD) is steadily increasing and the number of individuals seeking chest masculinization surgery, commonly called “top-surgery,” is also increasing. In persons assigned female at birth, top surgery removes most breast tissue to allow the chest to appear masculine. However, top surgery is not equivalent to oncologic risk-reducing mastectomies (RRM), which aim to remove all breast tissue to reduce future breast cancer (BC) risk. While top surgery has significant psychological and health benefits, its impact on future BC risk is unclear, especially for those individuals who are at high-risk for future BC development (defined as ≥17% by international consensus guidelines) and/or have a pathogenic germline mutation for BC. The aims of this study are to 1) determine the percentage of TGD persons assigned female at birth who are considering undergoing top surgery and are at elevated BC risk using validated risk models, 2) determine the percentage of TGD persons at elevated BC risk who choose to undergo RRM as part of top surgery, and 3) assess and compare self-perceived BC risk compared to calculated risk. Methods: A single-institution prospective single-arm pilot study is currently underway from March 2023 through January 2024. Individuals assigned female at birth, who are age ≥18 years, and considering undergoing top surgery, but have not yet undergone top surgery, are eligible to enroll (goal accrual N=35). Participants are being actively recruited form our institution’s Comprehensive LGBTQIA+ Inclusion Health Clinic and Plastic Surgery Clinic. Consented participants undergo a personalized BC risk assessment the validated IBIS and Gail (for those ≥35 years old) BC risk models. Those with an average lifetime BC risk (&amp;lt; 17%) are recommended to continue top surgery as planned/no intervention, participants with a moderate lifetime BC risk (17-30%) are counseled they may consider RRM as part of their top surgery operation given that sparse data exists in this space, while those with a high lifetime BC risk ( &amp;gt;30% or pathogenic germline variant) are counseled on the potential benefits of RRM. Participants with a family history suggestive of a hereditary cancer syndrome meet with a certified Genetics counselor for consideration of genetic testing, per NCCN guidelines. Results: We anticipate reporting 1) general demographic description of the cohort, 2) the percentage who have an elevated lifetime BC risk and/or a pathogenic genetic mutation, 3) the percentage of those with an elevated risk who choose to undergo RRM as part of top surgery, and 4) report the differences between self-perceived vs calculated lifetime BC risk. Conclusion: Our findings will inform TGD persons and surgeons on the utility of a personalized BC risk assessment prior to top surgery to assist in surgical decision-making, report the accuracy of self-perceived BC risk in TGD persons, and potentially change the standard pre-operative evaluation for TGD persons considering top surgery to include a formal BC risk assessment to ensure high-risk individuals have the option to undergo RRM to appropriately reduce their future BC risk. Citation Format: Chandler Cortina, Anna Purdy, Samantha Stachowiak, Kristen Klement, Sarah Sasor, Kate Krucoff, Andrew Petroll, Kevin Robertson, Annie Lakatos, Erin Doren. A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery [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 PO1-20-01.

Association of Ideal Cardiovascular Health in Youth with Cancer Risk in Adulthood: A Cardiovascular Risk in Young Finns Study.

Lifestyle factors may affect cancer risk. This study aimed to identify whether the American Heart Association ideal cardiovascular health (ICH) score and its individual variables in youth are associated with subsequent cancer incidence. This study comprised participants of the Cardiovascular Risk in Young Finns Study free of cancer at the analysis baseline in 1986 (n = 1,873). The baseline age was 12 to 24 years, and the follow-up occurred between 1986 and 2018. Among 1,873 participants (mean age 17.3 ± 4.1 years; 53.4% females at baseline), 72 incident cancer cases occurred during the follow-up (mean follow-up time 31.4 ± 3.4 years). Baseline ICH score was not associated with future cancer risk (HR, 0.96; 95% confidence interval, 0.78-1.12 per 1-point increment). Of individual ICH score variables, ideal physical activity (PA) was inversely associated with cancer incidence [age- and sex-adjusted HR, 0.45 (0.23-0.88) per 1-category change (nonideal/ideal)] and remained significant in the multivariable-adjusted model, including body mass index, smoking, diet, and socioeconomic status. A continuous PA index at ages 9 to 24 years and moderate-to-vigorous PA in youth were also related to decreased cancer incidence (P < 0.05). Body mass index, smoking, diet, total cholesterol, glucose, and blood pressure were not related to cancer risk. Of the dietary components, meat consumption was associated with cancer incidence (P = 0.023). These findings indicate that higher PA levels in youth are associated with a reduced subsequent cancer incidence, whereas the American Heart Association's ICH score in youth does not. This finding supports efforts to promote a healthy lifestyle and encourages PA during childhood, yielding a subsequent healthier life.

Prevalence of H. pylori and Gastric Intestinal Metaplasia in BRCA1 and BRCA2 Carriers.

BRCA1 and BRCA2 carriers may be at increased risk for gastric cancer; however, the mechanisms of gastric carcinogenesis remain poorly understood. We sought to determine the prevalence of gastric cancer risk factors Helicobacter pylori (H. pylori) infection and gastric intestinal metaplasia (GIM) among BRCA1/2 carriers to gain insight into the pathogenesis of gastric cancer in this population. A total of 100 unselected BRCA1/2 carriers who underwent endoscopic ultrasound from March 2022 to March 2023 underwent concomitant upper endoscopy with nontargeted gastric antrum and body biopsies. The study population (70% women; mean age 60.1 years) included 66% BRCA2 carriers. H. pylori was detected in one (1%) individual, 7 (7%) had GIM, 2 (2%) had autoimmune atrophic gastritis, and no gastric cancers were diagnosed. Among BRCA1/2 carriers, H. pylori prevalence was low and GIM prevalence was similar to that in the general population; however, identification of H. pylori or GIM may help inform future gastric cancer risk management strategies in BRCA1/2 carriers. Prevention Relevance: Evaluating the burden of H. pylori infection and GIM among BRCA1/2 carriers is warranted to better understand the mechanisms of gastric carcinogenesis and to help inform risk management strategies for gastric cancer among this at-risk population.

Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease

IntroductionBenign breast disease (BBD) and high mammographic breast density (MBD) are prevalent and independent risk factors for invasive breast cancer. It has been suggested that temporal changes in MBD may impact future invasive breast cancer risk, but this has not been studied among women with BBD.MethodsWe undertook a nested case–control study within a cohort of 15,395 women with BBD in Kaiser Permanente Northwest (KPNW; 1970–2012, followed through mid-2015). Cases (n = 261) developed invasive breast cancer > 1 year after BBD diagnosis, whereas controls (n = 249) did not have breast cancer by the case diagnosis date. Cases and controls were individually matched on BBD diagnosis age and plan membership duration. Standardized %MBD change (per 2 years), categorized as stable/any increase (≥ 0%), minimal decrease of less than 5% or a decrease greater than or equal to 5%, was determined from baseline and follow-up mammograms. Associations between MBD change and breast cancer risk were examined using adjusted unconditional logistic regression.ResultsOverall, 64.5% (n = 329) of BBD patients had non-proliferative and 35.5% (n = 181) had proliferative disease with/without atypia. Women with an MBD decrease (≤ − 5%) were less likely to develop breast cancer (Odds Ratio (OR) 0.64; 95% Confidence Interval (CI) 0.38, 1.07) compared with women with minimal decreases. Associations were stronger among women ≥ 50 years at BBD diagnosis (OR 0.48; 95% CI 0.25, 0.92) and with proliferative BBD (OR 0.32; 95% CI 0.11, 0.99).DiscussionAssessment of temporal MBD changes may inform risk monitoring among women with BBD, and strategies to actively reduce MBD may help decrease future breast cancer risk.

Abstract 6619: Dynamic prediction with repeated mammogram images improves 5-year breast cancer risk performance

Abstract Background: Increasing use of digital screening mammography opens potential to add individualized dynamic measures of breast image history to predict future breast cancer risk. Current prediction models use a single mammogram measure, usually breast density, at one point in time, to add to prediction models based on established risk factors. Our previous work has shown that change in each breast over time is related to risk of diagnosis with breast cancer. Methods: We apply a dynamic prediction model that includes full field digital mammogram in each breast to predict future 5-year risk. Data from Joanne Knight Breast Health Cohort (JKBHC) serve as training population with 290 pathology-confirmed breast cancer cases and 658 controls sampled with a total number of 8,712 craniocaudal (CC) view mammograms for analysis. Nurses’ Health Study 2 (NHS2) serves as the external validation with 188 cases and 462 controls sampled with a total number of 3,712 CC-views. The dynamic risk prediction model aims to estimate the probability of future breast cancer risk conditional on the past history of the longitudinal mammograms. We train the model on the JKBH cohort, retaining 20% for testing and 15% for validation. We report a time dependent AUC when evaluating model performance. Results: The mean age of women was 56.6 years at entry to cohort; 81.3% were white. Adding trajectory of mammogram images in each breast to a model improved performance. The mean number of mammograms conditional on 5 years of observation in JKBHC was 3.36 (1.49) and in NHS2 2.36 (1.53). We obtained a 5-year dynamic AUC of 0.81 (0.05) in the JKBHC internal validation and 0.77 (0.06) in the NHS2 external validation. Model performance was comparable in pre- and postmenopausal women. Because breasts with high mammographic density hinder detection of tumors we stratify analyses by dense and non-dense at baseline. Among women with dense breasts at baseline (BIRADS C and D), we observe a dynamic 5-year risk AUC = 0.77. In the subgroup with less dense breasts (BIRADS A and B) we observe AUC = 0.74. Conclusions: With this approach we can accommodate the irregularly observed time-varying images and project only partial information prior to doing the prediction. Adding trajectory of mammogram history in each breast improves 5-year breast cancer risk prediction beyond models used in clinic today. Citation Format: Graham A. Colditz, Rulla M. Tamimi, Bernard Rosner, Shu Jiang. Dynamic prediction with repeated mammogram images improves 5-year breast cancer risk performance [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 6619.

Clinical characteristics and radiation therapy modality of younger patients with early-stage endometrial cancer, a multicenter study in China’s real world

BackgroundEndometrial cancer is a prevalent gynecologic malignancy found in postmenopausal women. However, in the last two decades, the incidence of early-stage has doubled in women under 40 years old. This study aimed to investigate the clinical and pathological characteristics and adjuvant therapeutic modalities of both young and not -young patients with early-stage endometrial cancer in China’s real world.MethodsThis retrospective study analyzed patients with early-stage endometrial cancer at 13 medical institutions in China from 1999 to 2015. The patients were divided into two groups: young (≤ 45 years old) and non-young (> 45 years old). Statistical comparisons were conducted between the two groups for clinical characteristics, pathological features, and survival. The study also identified factors that affect local recurrence-free survival (LRFS) using Cox proportional risk regression analysis. Propensity score matching (1:1) was used to compare the effects of local control between vaginal brachytherapy (VBT) alone and pelvic external beam radiotherapy (EBRT) ± VBT.ResultsThe study involved 1,280 patients, 150 of whom were 45 years old or younger. The young group exhibited a significantly higher proportion of stage II, low-risk, lower uterine segment infiltration (LUSI), and cervical invasion compared to the non-young group. Additionally, the young patients had significantly larger maximum tumor diameters. The young group also had a significantly higher five-year overall survival (OS) and a five-year LRFS. Age is an independent risk factor for LRFS. There was no significant difference in LRFS between young patients with intermediate- to high-risk early-stage endometrial cancer who received EBRT ± VBT and those who received VBT alone.ConclusionsIn the present study, young patients had better characteristics than the non-young group, while they exhibited higher levels of aggressiveness in certain aspects. The LRFS and OS outcomes were better in young patients. Age is an independent risk factor for LRFS. Additionally, VBT alone may be a suitable option for patients under 45 years of age with intermediate- to high-risk early-stage endometrial cancer, as it reduces the risk of toxic reactions and future second cancers while maintaining similar local control as EBRT.

Infertility and risk of postmenopausal breast cancer in the women's health initiative.

Although infertility (i.e., failure to conceive after ≥ 12months of trying) is strongly correlated with established breast cancer risk factors (e.g., nulliparity, number of pregnancies, and age at first pregnancy), its association with breast cancer incidence is not fully understood. Previous studies were primarily small clinic-based or registry studies with short follow-up and predominantly focused on premenopausal breast cancer. The objective of this study was to assess the relationship between infertility and postmenopausal breast cancer risk among participants in the Women's Health Initiative (analytic sample = 131,784; > 25years of follow-up). At study entry, participants were asked about their pregnancy history, infertility history, and diagnosed reasons for infertility. Incident breast cancers were self-reported with adjudication by trained physicians reviewing medical records. Cox proportional hazards models were used to estimate risk of incident postmenopausal breast cancer for women with infertility (overall and specific infertility diagnoses) compared to parous women with no history of infertility. We examined mediation of these associations by parity, age at first term pregnancy, postmenopausal hormone therapy use at baseline, age at menopause, breastfeeding, and oophorectomy. We observed a modest association between infertility (n = 23,406) and risk of postmenopausal breast cancer (HR = 1.07; 95% CI 1.02-1.13). The association was largely mediated by age at first term pregnancy (natural indirect effect: 46.4% mediated, CI 12.2-84.3%). These findings suggest that infertility may be modestly associated with future risk of postmenopausal breast cancer due to age at first pregnancy and highlight the importance of incorporating reproductive history across the life course into breast cancer analyses.