Cancer Risk Assessment Models Research Articles

Computational dissection of the regulatory mechanisms of aberrant metabolism in remodeling the microenvironment of breast cancer.

The composition of T cell subsets and tumor-specific T cell interactions within the tumor microenvironment (TME) contribute to the heterogeneity observed in breast cancer. Moreover, aberrant tumor metabolism is often intimately linked to dysregulated anti-tumor immune function of T cells. Identifying key metabolic genes that affect immune cell interactions thus holds promise for uncovering potential therapeutic targets in the treatment of breast cancer. This study leverages single-cell transcriptomic data from breast cancer to investigate tumor-specific T-cell subsets and their interacting subnetworks in the TME during cancer progression. We further assess the metabolic pathway activities of tumor-specifically activated T-cell subsets. The results reveal that metabolic pathways involved in insulin synthesis, secretion, degradation, as well as fructose catabolism, significantly influence multiple T cell interactions. By integrating the metabolic pathways that significantly up-regulate T cells in tumors and influence their interactions, we identify key abnormal metabolic genes associated with T-cell collaboration and further develop a breast cancer risk assessment model. Additionally, using gene expression profiles of prognosis-related genes significantly associated with aberrant metabolism and drug IC50 values, we predict targeted drugs, yielding potential candidates like GSK-J4 and PX-12. This study integrate the analysis of abnormal T-cell interactions and metabolic pathway abnormalities in the breast cancer TME, elucidating their roles in cancer progression and providing leads for novel breast cancer therapeutic strategies.

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 <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<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.

Feasibility of risk assessment for breast cancer molecular subtypes

PurposeFew breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination.MethodsAmong 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype.ResultsThere were 3,073 ER/PR+ HER2 − , 340 ER/PR +HER2 + , 126 ER/PR−ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR−HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI.ConclusionOur study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

Deep learning vs traditional breast cancer risk assessment models: Are we offering supplemental services to those who benefit the most?

1556 Background: Mammography-based deep learning (DL) risk stratification models improve discriminatory accuracy in predicting future breast cancer compared to traditional risk models; however, decisions for and insurance payment of supplemental services such as MRI are driven by traditional scores. The purpose of this study was to compare risk scores and cancer detection rates in patients identified as increased risk by DL vs traditional models in a large screening mammography cohort. Methods: This multisite study included consecutive patients ≥40y undergoing routine bilateral screening mammography from 9/2017 to 1/2022 at five facilities. Tyrer-Cuzick version 8 (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models and a DL 5-year model were used to assess risk. The following thresholds were used to define intermediate risk: ≥1.67% for TC and BCRAT 5-year, 2.2 for DL model and high risk: ≥20% for TC and BCRAT lifetime, ≥6.0 for DL model. Patients were included if all risk scores were available. Patient demographics were retrieved from electronic medical records and cancer outcomes through regional tumor registry linkage. The proportion of increased-risk patients and cancer detection rates (CDR [cancers per 1000 women screened]) across models were compared using Pearson’s Chi-square test. Results: 148476 exams in 69464 patients (mean age 58.0y [IQR: 50.0-67.0]) were performed. 81.7% (119386/146075) of exams were in White patients and 18.3% (26689/146075) in races other than White. CDR of those classified as intermediate risk by DL was 7.1 (450/63109) vs 4.7 (320/68545; P<0.001) by TC and 4.2 (300/70831; P<0.001) by BCRAT, and as high risk was 20.6 (305/14772) by DL vs 3.9 (64/16442; P<0.001) by TC and 5.0 (29/5782; P<0.001) by BCRAT. 13.0% (19248/148476) were classified as intermediate risk by DL but not by TC or BCRAT, with a CDR of 6.0 in this cohort. 32.0% (47435/148476) were classified as intermediate risk by TC or BCRAT but not by DL with a CDR of 1.1. 8.5% (12577/148476) were classified as high risk by DL but not by TC or BCRAT with a CDR of 20.8. 11.1% (16451/148476) were classified as high risk by TC or BCRAT but not by DL with a CDR of 1.9. Conclusions: A significant proportion of patients identified as increased risk by DL model are not assessed as increased risk by commonly used risk models, effectively excluding them from accessing potentially life-saving supplemental services. DL scores met the American College of Radiology's acceptable CDR on screening mammography (≥2.5) regardless of traditional scores, however, elevated traditional scores in the absence of elevated DL scores did not meet acceptable standards. Risk assessment guidelines incorporating DL models are necessary to capture patients who benefit the most.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic

IntroductionGynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic. MethodsA patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models. ResultsA total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic. ConclusionAmong patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

Next Top Model: An Overview of Breast Cancer Risk Assessment Models

Next Top Model: An Overview of Breast Cancer Risk Assessment Models

Breast cancer: genetic personal risk factors: A review

Determination of cancer risk factors allow us to develop diagnostics tests that improved identification and reduced the rate of mortality of most frequent cancer diseases including breast cancer, prostate cancer, gastrointestinal tumors. Today individual risk of breast cancer considers personal genetics, medical history of patient, lifestyle, and a number of additional factors. Calculation of the first mathematical models for breast cancer risk assessment included anthropometric data, hormonal status, and family history of cancer. The discovery of BRCA1 and BRCA2 genes role in the development of breast cancer and the accumulation of data from population studies contributed to the introduction of the genetic component into mathematical models. The trend of the last decade is the integration of the polygenic component into the scheme for calculating the individual risk of breast cancer. In this review, we have analyzed existing models, assessed their relevance for certain groups of patients, studied the trends in the development of methods for molecular genetic diagnosis of breast cancer and determining the personal risk of developing the disease.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Unlocking learner potential: A course director's experience building an Educational Escape Room for Cancer Risk Assessment.

Using a traditional lecture format alone often limits students' opportunities to engage with and apply the concepts taught in a classroom. This report describes the design, curricular implementation, and application of an innovative educational escape room as an active learning tool for genetic counselor educators to teach cancer risk assessment skills. A Cancer Risk Assessment Escape Room is a novel way to offer experiential learning and to assess a learner's critical thinking and ability to apply cancer risk assessment models to evolving clinical scenarios. This successful implementation of an interactive escape room can be adapted to other genetic counseling competencies. The use of an escape room allows learners to actively engage in and apply content taught in lectures before entering clinical internships.

The History of the Linear No-Threshold Model and Recommendations for a Path Forward

The intent of this paper and the accompanying video series is to inform the scientific community about the historical foundations that underpin the linear no-threshold (LNT) model's use for cancer risk assessment. There is a clear distinction here: this effort is about the history of how LNT came to be the regulatory paradigm and model for cancer risk assessment that it is today and not a discussion of the pros and cons of the LNT model. The overarching goal of this effort is to reframe the conversation around low-dose response models in light of this history and to determine how this history influences the scientific understanding of low-dose radiation responses. The timing of this series is intentional, as the International Commission on Radiological Protection (ICRP) has embarked on a mission to review the entire system of radiation protection. This effort necessarily requires rigorous scientific debate that must be based in fact. The history of the LNT model is paramount to this discussion, and it warrants consideration. Unfortunately, rather than engendering respectful debate, the topic of cancer risks associated with low dose radiation exposures has forged two disparate and sometimes contentious camps: (1) low doses, no matter how low, present some form of health risk and (2) an alternative model better represents the actual risks. The video series, conceived by John Cardarelli II, current President of the Health Physics Society (HPS), features Edward Calabrese, professor of toxicology in the School of Public Health and Health Sciences at the University of Massachusetts at Amherst, being interviewed by HPS Past-President Barbara Hamrick, CHP, JD, with support from Daniel Sowers, the Chair of the HPS Public Information Committee, and HPS Executive Director Brett Burk. Emily Caffrey, the Chief Editor of our Ask-the-Experts website (https://hps.org/publicinformation/ate/), was invited to watch the completed series as an independent peer reviewer. Further, an email address, factcheck@hps.org, was created to allow for peer-review by the scientific community to facilitate ongoing discussion and allow for corrections to the record as necessary. It is the sincere hope of this team that this work inspires new discussions about the system of radiological protection. We encourage everyone in this field to watch all 22 episodes to be informed about the underpinnings of current regulatory policy in the US.

Optimization of Cancer Risk Assessment Models for PM2.5-Bound PAHs: Application in Jingzhong, Shanxi, China.

The accurate evaluation of the carcinogenic risk of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) is crucial because of the teratogenic, carcinogenic, and mutagenic effects of PAHs. The best model out of six models was selected across three highly used categories in recent years, including the USEPA-recommended inhalation risk (Model I), inhalation carcinogen unit risk (Models IIA-IID), and three exposure pathways (inhalation, dermal, and oral) (Model III). Model I was found to be superior to the other models, and its predicted risk values were in accordance with the thresholds of PM2.5 and benzo[a]pyrene in ambient-air-quality standards. Models IIA and III overestimated the risk of cancer compared to the actual cancer incidence in the local population. Model IID can replace Models IIB and IIC as these models exhibited no statistically significant differences between each other. Furthermore, the exposure parameters were optimized for Model I and significant differences were observed with respect to country and age. However, the gender difference was not statistically significant. In conclusion, Model I is recommended as the more suitable model, but in assessing cancer risk in the future, the exposure parameters must be appropriate for each country.

Risk prediction models for head and neck cancer: A rapid review.

Cancer risk assessment models are used to support prevention and early detection. However, few models have been developed for head and neck cancer (HNC). A rapid review of Embase and MEDLINE identified n=3045 articles. Following dual screening, n=14 studies were included. Quality appraisal using the PROBAST (risk of bias) instrument was conducted, and a narrative synthesis was performed to identify the best performing models in terms of risk factors and designs. Six of the 14 models were assessed as "high" quality. Of these, three had high predictive performance achieving area under curve values over 0.8 (0.87-0.89). The common features of these models were their inclusion of predictors carefully tailored to the target population/anatomical subsite and development with external validation. Some existing models do possess the potential to identify and stratify those at risk of HNC but there is scope for improvement.

22 (PB-022) Poster Spotlight - An individualized breast cancer risk assessment model for women attending screening in BreastScreen Norway

Background: The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. In order to improve it, studies have proposed personalized screening strategies based on woman’s individual breast cancer (BC) risk. There is, therefore, a need to create individual risk prediction models through the analysis of large population-based databases. We developed a model that could be used to classify women targeted for mammography screening according to individual BC risk.

Can National Registries Contribute to Predict the Risk of Cancer? The Cancer Risk Assessment Model (CRAM).

Simple SummaryEarly identification of individuals with an increased risk of cancer is an important challenge. Danish administrative registers may be useful in this respect because they cover the entire population and include comprehensive and consistently coded long-term data. We aimed to develop a predictive model based on Danish administrative registers to facilitate the automated identification of individuals at risk of any type of cancer. In addition to age, almost all the included factors contributed statistically significantly, but also only marginally, to the prediction models, which means that we have not overlooked obvious information available in the register. Future prediction studies should focus on specific cancer types where more precise risk estimations might be expected. It is our ultimate ambition that an effective model can be used at the point of care, integrated into electronic patient record systems to alert physicians of patients at a high risk of cancer.Purpose: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. Methods: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007–2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). Results: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81–0.82) for men and 0.75 (95% CI 0.74–0.75) for women. Conclusion: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice.

Epidemiological characteristics and risk factors of lung adenocarcinoma: A retrospective observational study from North China.

BackgroundThe main aim of the study was to determine the risk factors of lung adenocarcinoma and to analyze the variations in the incidence of lung adenocarcinoma according to time, sex, and smoking status in North China.MethodsPatients with lung cancer in local household registries diagnosed and treated for the first time in the investigating hospital were enrolled from 11 cities in North China between 2010 and 2017. Baseline characteristics and tumor-related information were extracted from the patients’ hospital medical record, clinical course records, and clinical examination. Some of the variables, such as smoking, alcohol consumption, medical history, and family history of cancer, were obtained from interviews with the enrolled patients. The statistical method used were the chi-square test and multi-factor logistic regression analysis. The time trend was statistically analyzed using Joinpoint regression models, and p values were calculated.ResultsA total of 23,674 lung cancer cases were enrolled. People in severely polluted cities were at higher risk for lung adenocarcinoma (p < 0.001). Most patients with lung adenocarcinoma had no history of lung-related diseases (p = 0.001). Anatomically, lung adenocarcinoma was more likely to occur in the right lung (p < 0.001). Non-manual labor workers were more likely to develop from lung adenocarcinoma than manual workers (p = 0.015). Notably, non-smokers were more likely to develop lung adenocarcinoma than smokers (p < 0.001). The proportion of lung adenocarcinoma increased significantly in Hebei Province (p < 0.001). Among non-smokers, the proportion of lung adenocarcinoma showed a higher rise than in smokers (p < 0.001).ConclusionsLung adenocarcinoma is the most common histological type of lung cancer in North China (Hebei Province), and the proportion of lung adenocarcinoma is increasing, especially among non-smokers. Lung adenocarcinoma is more common in women, severely polluted cities, individuals with no history of lung-related diseases, in the right lung, and in non-smokers. These can serve as a great guide in determining the accuracy of lung adenocarcinoma high-risk groups and lung cancer risk assessment models.

Cancer risk assessment models versus National Comprehensive Cancer Network guidelines: What is the most comprehensive method to assess cancer risk and triage for genetic testing? (373)

Cancer risk assessment models versus National Comprehensive Cancer Network guidelines: What is the most comprehensive method to assess cancer risk and triage for genetic testing? (373)

Clinical tools and counseling considerations for breast cancer risk assessment and evaluation for hereditary cancer risk.

Breast cancer is a complex and heterogeneous disease. Unfortunately, it is the most common malignancy diagnosed in women in the USA, with 281,550 new cases of invasive breast cancer and 49,290 new cases of noninvasive breast cancer are diagnosed per year. In England, it is currently estimated that approximately 1 in 7 (14%) women will be diagnosed with breast cancer in their lifetime. In the UK in 2017, 54,700 women and 390 men were diagnosed with breast cancer. The risk of breast cancer is influenced by many factors, including but not limited to age, family history, reproductive history, hormonal exposure, proliferative breast lesions, physical activity, alcohol use, tobacco use, breast density, and environmental exposures. Breast cancer risk assessment is a critical part of public health. By identifying women at high risk for breast cancer, personalized recommendations can be deployed with regards to modes of screening, the age to initiate breast screening, and the frequency for completing such screens. In addition, breast cancer risk assessment can assist in determining a woman's eligibility for interventions to reduce risk, either through the use of chemoprevention medications or through surgical means with risk-reducing bilateral mastectomy. This chapter summarizes breast cancer risk assessment models and discusses interventions to reduce breast cancer risk to aid in reducing morbidity and mortality from breast cancer.

Beyond the AJR: Validation of an Artificial Intelligence Breast Cancer Risk Assessment Model Across Diverse International Cohorts

Beyond the <i>AJR</i>: Validation of an Artificial Intelligence Breast Cancer Risk Assessment Model Across Diverse International Cohorts

Laboratory-related outcomes from integrating an accessible delivery model for hereditary cancer risk assessment and genetic testing in populations with barriers to access

PurposeThis study aimed to evaluate the laboratory-related outcomes of participants who were offered genomic testing based on cancer family history risk assessment tools. MethodsPatients from clinics that serve populations with access barriers, who are screened at risk for a hereditary cancer syndrome based on adapted family history collection tools (the Breast Cancer Genetics Referral Screening Tool and PREMM5), were offered exome-based panel testing for cancer risk and medically actionable secondary findings. We used descriptive statistics, electronic health record review, and inferential statistics to explore participant characteristics and results, consultations and actions related to pathogenic/likely pathogenic variants identified, and variables predicting category of findings, respectively. ResultsOf all the participants, 87% successfully returned a saliva kit. Overall, 5% had a pathogenic/likely pathogenic cancer risk variant and 1% had a secondary finding. Almost all (14/15, 93%) participants completed recommended consultations with nongenetics providers after an average of 17 months. The recommended actions (eg, breast magnetic resonance imaging) were completed by 17 of 25 participants. Participant personal history of cancer and PREMM5 score were each associated with the category of findings (history and colon cancer finding, Fisher’s exact P = .02; history and breast cancer finding, Fisher’s exact P = .01; PREMM5TM score; and colon cancer finding, Fisher’s exact P < .001). ConclusionThis accessible model of hereditary cancer risk assessment and genetic testing yielded results that were often acted upon by patients and physicians.

Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.

This study performed and assessed a novel program to improve the accuracy of short-term breast cancer risk prediction by using information from craniocaudal (CC) and mediolateral-oblique (MLO) views of two breasts. An age-matched dataset of 556 patients with at least two sequential full-field digital mammography examinations was applied. In the second examination, 278 cases were diagnosed and pathologically verified as cancer, and 278 were negative, while all cases in the first examination were negative (not recalled). Two generalized linear-model-based risk prediction models were established with global- and local-based bilateral asymmetry features for CC and MLO views first. Then, a new fusion risk model was developed by fusing prediction results of the CC- and MLO-based risk models with an adaptive alpha-integration-based fusion method. The AUC of the fusion risk model was 0.72 ± 0.02, which was significantly higher than the AUC of CC- or MLO-based risk model (P < 0.05). The maximum odds ratio for CC- and MLO-based risk models were 8.09 and 5.25, respectively, and increased to 11.99 for the fusion risk model. For subgroups of patients aged 37-49years, 50-65years, and 66-87years, the AUCs of 0.73, 0.71, and 0.75 for the fusion risk model were higher than AUC for CC- and MLO-based risk models. For the BIRADS 2 and 3 subgroups, the AUC values were 0.72 and 0.71 respectively for the fusion risk model which were higher than the AUC for the CC- and MLO-based risk models. This study demonstrated that the fusion risk model we established could effectively derive and integrate supplementary and useful information extracted from both CC and MLO view images and adaptively fuse them to increase the predictive power of the short-term breast cancer risk assessment model.