Breast Cancer Risk Prediction Research Articles

Radiation doses to the breast and predicted breast cancer risk among patients treated for Hodgkin lymphoma with modern radiotherapy.

To contrast breast radiation exposure from chest radiotherapy in 2006-2021 with 1965-1997, and to compare breast cancer (BC) risk 25 years after treatment predicted by two models. Radiation dose distributions to the breast from 101 chest radiotherapies given 2006-2021 for Hodgkin lymphoma (HL) or other lymphoma in one German and two Dutch hospitals were compared with doses received by 505 Dutch HL patients treated 1965-1997 and sampled into a nested case-control study, weighted to represent a HL patient cohort. Dose-volume histograms, mean dose and doses to 10 breast segments were evaluated. Absolute BC risks 25 years after historic and recent treatments were estimated for low- and high-risk profiles (HL stage III vs I at age 24 in 2010, treatment-induced menopause at age 24 vs none, first live birth at age 20 vs nulliparous, BC family history no vs yes, no HL relapse, non-smoking at HL diagnosis, exposed to anthracyclines and cardiotoxic radiotherapy). Average mean breast dose decreased from 21.4 Gy for historic to 3.0 Gy for recent treatments. The breast volume receiving ≥20 Gy (V20) decreased from 48.7% to 2.2%, and the lowest dose to the highest exposed 20% of breast volume (D20) from 39.0 Gy to 2.8 Gy. Most metrics decreased during 2006-2021. Using quadrant-specific doses, median absolute BC risk 25 years after treatment decreased from 21.0% to 6.9% for historic vs. recent treatments for a high-risk patient, and from 3.1% to 0.9% for a low-risk patient, respectively. Using mean dose, median absolute BC risks decreased from 20.3% to 6.0% and from 3.1% to 0.9% for the high- and low-risk patient, respectively. Modern radiotherapy treatments expose the breast to lower radiation doses and irradiated volumes than those used before 2000. BC risk predictions using mean breast dose were slightly lower than predictions using quadrant-specific doses.

NOTIFICATION: Development of a Novel Approach for Breast Cancer Prediction and Early Detection Using Minimally Invasive Procedures and Molecular Analysis: How Cytomorphology Became a Breast Cancer Risk Predictor

NOTIFICATION: Development of a Novel Approach for Breast Cancer Prediction and Early Detection Using Minimally Invasive Procedures and Molecular Analysis: How Cytomorphology Became a Breast Cancer Risk Predictor

Polygenic score distribution differences across European ancestry populations: implications for breast cancer risk prediction

BackgroundThe 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed.MethodsWe explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction.ResultsThe mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment.ConclusionsOur results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.

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.

Evaluating the performance of the BOADICEA model in predicting 10-year breast cancer risks in UK Biobank.

The BOADICEA model predicts breast cancer risk using cancer family history, epidemiological and genetic data. We evaluated its validity in a large prospective cohort. We assessed model calibration, discrimination and risk classification ability in 217,885 women (6,838 incident breast cancers) aged 40-70 years old of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk (RR) thresholds equivalent to the absolute lifetime risk categories of < 17%, 17-30% and ≥30%, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313-SNP polygenic score and pathogenic variants. Mammographic density data were not available. The PRS was the most discriminative risk factor (AUC=0.65). Discrimination was highest when considering all risk factors (AUC=0.66). The model was well calibrated overall (E/O=0.99, 95%CI=0.97-1.02; calibration slope=0.99, 95%CI:0.99-1.00), and in deciles of predicted risks. Discrimination was similar in women younger and older than 50 years. There was some underprediction in women under age 50 (E/O=0.89, 95%CI=0.84-0.94; calibration slope=0.96, 95%CI:0.94-0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4% and 1.4% of women in RR categories <1.6, 1.6-3.1 and ≥3.1, identifying 25.6% of incident breast cancer cases in category RR ≥ 1.6. BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk which can facilitate risk-stratified screening and personalized breast cancer risk management.

Longitudinal interpretability of deep learning based breast cancer risk prediction.

Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key questions include whether these models can detect breast morphologic changes that lead to cancer. These findings would boost confidence in utilizing BCR models in practice and provide clinicians with new perspectives. In this work, we aimed to determine when oncogenic processes in the breast provide sufficient signal for the models to detect these changes.&#xD;&#xD;Approach: In total, 1210 screening mammograms were collected for patients screened at different times before the cancer was screen-detected and 2400 mammograms for patients with at least ten years of follow-up. MIRAI, a BCR risk prediction model, was used to estimate the BCR. Attribution Heterogeneity was defined as the relative difference between the attributions obtained from the right and left breasts using one of the eight interpretability techniques. Model reliance on the side of the breast with cancer was quantified with AUC. The Mann-Whitney U test was used to check for significant differences in median absolute Attribution Heterogeneity between cancer patients and healthy individuals.&#xD;&#xD;Results: All tested attribution methods showed a similar longitudinal trend, where the model reliance on the side of the breast with cancer was the highest for the 0-1 Years-To-Cancer interval (AUC=0.85-0.95), dropped for the 1-3 Years-To-Cancer interval (AUC=0.64-0.71), and remained above the threshold for random performance for the 3-5 Years-To-Cancer interval (AUC=0.51-0.58). For all eight attribution methods, the median values of absolute attribution heterogeneity were significantly larger for patients diagnosed with cancer at one point (p<0.01).&#xD;&#xD;Significance: Interpretability of BCR prediction has revealed that long-term predictions (beyond three years) are most likely based on typical breast characteristics, such as breast density; for mid-term predictions (one to three years), the model appears to detect early signs of tumor development, while for short-term predictions (up to a year), the BCR model essentially functions as a breast cancer detection model.&#xD.

Evaluation of multiple breast cancer polygenic risk score panels in women of Latin American heritage.

A substantial portion of the genetic predisposition for breast cancer is explained by multiple common genetic variants of relatively small effect. A subset of these variants, which have been identified mostly in individuals of European and Asian ancestry, have been combined to construct a polygenic risk score (PRS) to predict breast cancer risk, but the prediction accuracy of existing PRSs in Hispanic/Latinx individuals (H/L) remain relatively low. We assessed the performance of several existing PRS panels with and without addition of H/L specific variants among self-reported H/L women. PRS performance was evaluated using multivariable logistic regression and the area under the receiver operating characteristic curve (AUC). Both European and Asian PRSs performed worse in H/L samples compared to original reports. The best European PRS performed better than the best Asian PRS in pooled H/L samples. European PRSs had decreased performance with increasing Indigenous American (IA) ancestry while Asian PRSs had increased performance with increasing IA ancestry. The addition of 2 H/L SNPs increased performance for all PRSs, most notably in the samples with high IA ancestry and did not impact the performance of PRSs in individuals with lower IA ancestry. A single PRS that incorporates risk variants relevant to the multiple ancestral components of individuals from Latin America, instead of a set of ancestry specific panels, could be used in clinical practice. Results highlight the importance of population-specific discovery and suggest a straightforward approach to integrate ancestry specific variants into PRS for clinical application.

Plasma prolactin and postmenopausal breast cancer risk: a pooled analysis of four prospective cohort studies.

Prolactin, a hormone produced by the pituitary gland, regulates breast development and may contribute to breast cancer etiology. However, most epidemiologic studies of prolactin and breast cancer have been restricted to single, often small, study samples with limited exploration of effect modification. The Biomarkers in Breast Cancer Risk Prediction consortium includes 8,279 postmenopausal women sampled from four prospective cohort studies, of whom 3,441 were diagnosed with invasive breast cancer after enrollment. Prolactin concentrations were measured for all study participants on plasma samples collected when all women were postmenopausal and before any breast cancer diagnosis using ELISA assays. Pooled, unconditional logistic regression models, adjusted for confounders, estimated odd ratios (OR) for associations of prolactin and postmenopausal breast cancer risk overall and stratified by breast cancer risk factors. Higher plasma prolactin concentrations were positively associated with postmenopausal breast cancer risk (> 13.2 ng/mL vs. < 7.9 ng/mL, OR: 1.20, 95% CI: 1.06, 1.36; P-trend < 0.001). Although associations did not appear to vary by time since blood draw or most breast cancer risk factors, associations were primarily observed in current users of postmenopausal hormones at blood draw (> 13.2 ng/mL vs. < 7.9 ng/mL, current users, OR: 1.58, 95% CI: 1.27, 1.96, P-trend < 0.001; non-current users, OR: 1.08, 95% CI: 0.93, 1.27, P-trend = 0.11; P-heterogeneity = 0.06). Prolactin may be a risk factor for postmenopausal breast cancer, particularly in the context of postmenopausal hormone use. Investigations of prolactin interactions with other hormonal factors may further inform breast cancer etiology.

The polygenic risk score in the breast cancer treatment

Breast cancer (BC) remains the most common cause of death in women worldwide, but advances in science have allowed earlier diagnosis and more comprehensive treatment. This review highlights the impact of extensive molecular genetic testing in assessing the risk of BC susceptibility, as well as possible responses to chemotherapy and radiotherapy. Studies in the literature show that several Single Nucleotide Polymorphisms (SNPs) of genes involved in molecular pathways may become predictors of the risk of developing BC. For example, SNPs in genes such as RAD51 and XRCC3 , already known to be linked with high BC susceptibility, were also correlated with a different response to radiotherapy and related adverse effects. In addition, the SNP ESR1 PvuII (rs2234693), on the ESR1 gene, has been associated with a poor prognosis in advanced BC, but can be a good predictor of the therapeutic effect of hormonal treatment. Therefore, SNPs can be considered as possible new biomarkers for BC risk and prognosis. In this view, it is important to evaluate Polygenic Risk Score, an essential component for accurate BC risk prediction, which may potentially improve screening and prevention strategies. Bioinformatics tools are available to calculate polygenic risk by assessing the presence of SNPs and patients’ family and personal history. This represents an important step forward in the era of personalized medicine for BC.

A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload.

Artificial intelligence improves mammography-based breast cancer risk prediction.

Artificial intelligence (AI) is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with high risk of a future breast cancer diagnosis. Here, we discuss how AI is improving mammographic density-associated risk prediction and shaping the future of screening and risk-reducing strategies.

Prediction of breast cancer risk for adolescents and young adults with Hodgkin lymphoma.

While female survivors of Hodgkin lymphoma (HL) have an increased risk of breast cancer (BC), no BC risk prediction model is available. We developed such models incorporating mean radiation dose to the breast or breast quadrant-specific radiation doses. Relative risks and age-specific incidence for BC and competing events (mortality or other subsequent cancer) were estimated from 1194 Dutch five-year HL survivors, treated at ages 11-40 during 1965-2000. Predictors were doses to ten breast segments or mean breast radiation dose, BC family history, year of and age at HL diagnosis, ages at menopause and first live birth. Models were independently validated using U.S. Childhood Cancer Survivor Study cohort participants. Predicted absolute BC risks 25 years after HL diagnosis ranged from 1.0% for survivors diagnosed at ages 20-24, with <10 Gy mean breast radiation dose and menopausal 5 years after HL diagnosis, to 22.0% for survivors 25-29 years at diagnosis, ≥25 Gy mean breast dose, and no menopause within 5 years. In external validation, the observed/expected BC case ratio was 1.19 (95% confidence interval 0.97 to 1.47) for the breast segment-specific doses model, and 1.29 (1.05 to 1.60) for the mean breast dose model. The areas under the receiver operating characteristic curve were 0.68 (0.63 to 0.74) and 0.68 (0.62 to 0.73), respectively. Breast segment-specific or mean breast radiation dose with personal and clinical characteristics predicted absolute BC risk in HL survivors with moderate discrimination but good calibration, rendering the models useful for clinical decision-making.

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review.

Background Parenchymal Enhancement (BPE) on breast MRI holds promise as an imaging biomarker for breast cancer risk and prognosis. The ability to identify those at greatest risk can inform clinical decisions, promoting early diagnosis and potentially guiding strategies for prevention such as risk-reduction interventions with the use of selective estrogen receptor modulators and aromatase inhibitors. Currently, the standard method of assessing BPE is based on the Breast Imaging-Reporting and Data System (BI-RADS), which involves a radiologist's qualitative categorization of BPE as minimal, mild, moderate, or marked on contrast-enhanced MRI. This approach can be subjective and prone to inter/intra-observer variability, and compromises accuracy and reproducibility. In addition, this approach limits qualitative assessment to 4 categories. More recently developed methods using machine learning/artificial intelligence (ML/AI) techniques have the potential to quantify BPE more accurately and objectively. This paper will review the current machine learning/AI methods to determine BPE, and the clinical applications of BPE as an imaging biomarker for breast cancer risk prediction and prognosis.

The INFLUENCE 3.0 model: Updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy

BackgroundIndividual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST). MethodsWomen, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry. Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. Separate models were developed for NST patients. Discrimination and calibration were assessed by 100x bootstrap resampling. ResultsIn the non-NST and NST group, 49,631 and 10,154 patients were included, respectively. Age, mode of detection, histology, sublocalisation, grade, pT, pN, hormonal receptor status ± endocrine treatment, HER2 status ± targeted treatment, surgery ± immediate reconstruction ± radiation therapy, and chemotherapy were significant predictors for LRR and/or CBC in non-NST patients. For NST patients this was similar, but excluding (y)pT and (y)pN status, and including presence of ductal carcinoma in situ, axillary lymph node dissection and pathologic complete response.For non-NST patients, the Cox and RSF models were integrated in the online tool with 5-year AUCs of 0.77 (95%CI:0.77–0.77) and 0.68 (95%CI:0.67–0.68)] for LRR and CBC prediction, respectively. For NST patients, the RSF model performed best (AUCs 0.77 (95%CI:0.76–0.78) and 0.73 (95%CI:0.69–0.76) for LRR and CBC, respectively). Regarding calibration, observed-predicted differences were all <1 %. ConclusionThis INFLUENCE 3.0 models showed moderate performance in LRR and CBC prediction. The models have been made available as online tool to enable clinical decision support regarding personalised follow-up.

Clinical recommendations for polygenic risk score-enhanced breast cancer screening: Can.Heal project

Abstract Background The application of polygenic risk score (PRS) in breast cancer (BC) screening presents promising opportunities. Developing recommendations for future use and research on this topic is a key focus of the EU4Health project: Building the EU cancer and public health genomics platform (Can.Heal). We aim to provide these recommendations based on the analysis of available evidence through a transparent and rigorous development process. Methods The recommendations adopt the GRADE evidence to decision methodology, leveraging an evidence review team and a multidisciplinary panel of nine experts. A systematic review is being conducted to evaluate the evidence for PRS in BC screening, in the domains of benefits and harms, acceptability, feasibility, equity and cost-effectiveness. Results Regarding benefits and harms of adding PRS to BC standard screening, we identified 63 relevant articles. Forty-five (71%) discussed benefits, while 14 (22%) addressed harms. Forty-two (67%) were observational studies, 18 (28%) modeling studies and 1 non-randomised control trial (2%) that examined the diagnostic accuracy of PRS-enhanced screening for relative BC risk prediction using measures such as net reclassification index and area under the curve. Lastly, two modeling studies (3%) assessed the clinical utility of PRS-enhanced screening in terms of life years gained, BC deaths averted. The other domains are under examination. The panel convened to redefine the research question and outcomes of interest and will reconvene to assess the certainty of evidence collected, and subsequently to draft recommendations. Conclusions The integration of PRS into BC screening shows potential benefits in improving risk prediction. Ongoing trials, such as Wisdom and MyPEBS, are studying the clinical utility of integrating PRS in BC screening. The approach taken by the Can.Heal aims to ensure that the recommendations are based on a thorough and balanced evaluation of the available evidence. Key messages • The Can.Heal project aims to formulate policy and research recommendations for breast cancer screening using PRS through a thorough evidence review. • Upcoming PRS recommendations rely on expert opinion and modeling studies.

Identification of sequence polymorphism in the D-loop region of mitochondrial DNA as a risk factor for breast cancer.

Mitochondrial DNA (mtDNA) variations affect the efficiency of the electron transport chain and production of reactive oxygen species, contributing to carcinogenesis. The D-loop region of mtDNA has emerged as a variation hotspot region in human neoplasia; however, the potential contribution of these variations in breast cancer risk prediction remains unknown. We investigated the relationship between germline single nucleotide polymorphisms (SNPs) in the entire D-loop region and breast cancer risk in Chinese women. Peripheral blood-isolated mtDNA from 2329 patients with breast cancer and 2328 cancer-free controls was examined for SNPs. In the combined cohort, we used traditional risk factors, susceptibility germline polymorphisms, and logistic regression analysis to evaluate the predictive value of susceptibility variants for breast cancer risk. We calculated the area under the receiver operating characteristic curve (AUC) as a measure. We also measured the content of 8-hydroxy-2'-deoxyguanosine (8-OHdG). Individual polymorphisms SNP573 were significantly associated with breast cancer risk in both the discovery and validation cohorts. In the combined cohort, the AUC of the traditional risk factors was 64.3%; after adding susceptibility variants, the AUC was 64.9% (DeLong test, p = 0.007). 8-OHdG levels were significantly higher in patients with breast cancer than in controls and higher in individuals with SNP573 than in those negative for this variation. Overall, oxidative stress might be associated with the risk of breast cancer, and SNP573 might be associated with oxidative stress. Our results indicate the risk potential of polymorphisms in the D-loop region in breast cancer in Southern China.

Validation of the Mirai model for predicting breast cancer risk in Mexican women

ObjectivesTo validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1–5-year period in Mexican women.MethodsThis retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai’s performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai’s continuous risk index based on identifying the top 10% of patients in the high-risk category.ResultsOf 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6–0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5–0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4–0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group.ConclusionsMirai has a moderate performance in predicting future breast cancer among Mexican women.Critical relevance statementProspective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening.Key PointsThe applicability of AI models to non-White, minority populations remains understudied.The Mirai model is linked to future cancer events in Mexican women.Further research is needed to enhance model performance and establish usage guidelines.Graphical

Are Women's Breast Cancer Risk Appraisals in Line with Updated Clinical Risk Estimates Communicated? Results from a UK Family History Risk and Prevention Clinic.

The incorporation of breast density and a polygenic risk score (PRS) into breast cancer risk prediction models can alter previously communicated risk estimates. Previous research finds that risk communication does not usually change personal risk appraisals. This study aimed to examine how women from the family history risk study appraise their breast cancer risk following communication of an updated risk estimate. In the family history risk study, 323 women attended a consultation to receive an updated breast cancer risk estimate. A subset (n = 190) completed a questionnaire, assessing their subjective breast cancer risk appraisals, satisfaction with the information provided, and cancer-related worry. One hundred and three were notified of a decreased risk, 34 an increased risk, and 53 an unchanged risk. Women's subjective risk appraisals were in line with the updated risk estimates provided, with age, a PRS, and breast density explaining most of the variance in these appraisals. Those notified of an increased risk demonstrated higher subjective risk perceptions compared with those whose risk remained unchanged or decreased. Women's subjective breast cancer risk appraisals are amenable to change following updated risk feedback, with new information breast density and a PRS accepted and integrated into existing risk appraisals. Trust in the service, the analogies, and visual communication strategies used may have positively influenced the integration of this new information. Further research is warranted to assess whether similar patterns emerge for other illnesses and in different clinical contexts to determine the best strategies for communicating updated risk estimates.

Post-operative breast imaging: a management dilemma. Can mammographic artificial intelligence help?

BackgroundImaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy.MethodsThis cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes.ResultsMammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy “POM” score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%.ConclusionApplying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.

Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?

Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?