Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic.

PURPOSE: A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment. METHODS: Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations. RESULTS: A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk. CONCLUSION: Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.

Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.

A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations.

OBJECTIVES: Radiologists' visual assessment of breast mammographic density (BMD) is subject to inter-observer variability. We aimed to develop and validate a new automated software tool mimicking expert radiologists' consensus assessments of 2D BMD, as per BI-RADS V recommendations. METHODS: The software algorithm was developed using a concept of Manhattan distance to compare a patient's mammographic image to reference mammograms with an assigned BMD category. Reference databases were built from a total of 2289 pairs (cranio-caudal and medio-lateral oblique views) of 2D full-field digital mammography (FFDM). Each image was independently assessed for BMD by a consensus of radiologists specialized in breast imaging. A validation set of additional 800 image pairs was evaluated for BMD both by the software and seven blinded radiologists specialized in breast imaging. The median score was used for consensus. Software reproducibility was assessed using FFDM image pairs from 214 patients in the validation set to compare BMD assessment between left and right breasts. RESULTS: The software showed a substantial agreement with the radiologists' consensus (unweighted κ = 0.68, 95% CI 0.64-0.72) when considering the four breast density categories, and an almost perfect agreement (unweighted κ = 0.84, 95% CI 0.80-0.88) when considering clinically significant non-dense (A-B) and dense (C-D) categories. Correlation between left and right breasts was high (rs = 0.87; 95% CI 0.84-0.90). CONCLUSIONS: BMD assessment by the software was strongly correlated to radiologists' consensus assessments of BMD. Its performance should be compared to other methods, and its clinical utility evaluated in a risk assessment model. KEY POINTS: • A new software tool assesses breast density in a standardized way. • The tool mimics radiologists' clinical assessment of breast density. • It may be incorporated in a breast cancer risk assessment model.

A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort.

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.