Approximately 80% of all breast cancer cases are estrogen receptor-positive (ER+). This subtype is known to have distant recurrences in a subset of patients after adjuvant endocrine therapy, partially due to the heterogeneity of the disease. ER+ breast cancer has been generally classified as an immune-cold disease. Thus, to better inform treatment decisions and to consider the prospect of immunotherapy, the immune microenvironment needs to be thoroughly characterized. In this study, the proteome and transcriptome of the tumour and tumour microenvironment (TME) were characterized using the GeoMx Digital Spatial Profiler (DSP) and NanoString’s BC360 gene expression panel. Spatially resolved tumour and TME across a patient's lumpectomy demonstrated substantial heterogeneity in the expression of commonly targeted immune and tumour proteins. Results from this study demonstrated heterogeneity across the tumour and TME in ER+ breast cancer, which may be reflective of a variable immune response.

Background:Breast cancer is the most commonly diagnosed cancer among women in Canada. Breast density substantially influences breast cancer risk and mammography performance. However, OncoSim-Breast, a Canadian microsimulation model representing breast cancer control, including cancer onset, screening, and survival, has not previously explicitly accounted for breast density. This study describes the incorporation of density-specific parameters into the OncoSim-Breast model. Data and methods:Breast density-specific inputs were integrated into OncoSim-Breast using data from five Canadian provinces. Three key parameters - prevalence, relative risk of breast cancer, and digital mammography performance (sensitivity and specificity) - were estimated by age group and breast density category, following the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification (categories A to D). Calibration experiments and internal validations were conducted to ensure the updated OncoSim-Breast model aligned with observed data from the Canadian Cancer Registry. Results:The prevalence of dense breasts declined with age: BI-RADS categories C and D accounted for 58% of women younger than 50 years and 26% of those aged 70 and older. Digital mammography sensitivity also decreased with increasing density: among women younger than 50 years, sensitivity was 88% for Category A and 69% for Category D. The updated OncoSim-Breast model accurately replicated age-specific incidence, age-adjusted incidence, and stage distribution based on historical data from the Canadian Cancer Registry (2010 to 2019). Interpretation:Incorporating breast density-specific parameters substantially improved the accuracy and policy relevance of OncoSim-Breast. The updated model provides a validated tool to inform screening policy decisions for Canadian women, allowing consideration for the effect of the variability of breast density among women.

BACKGROUND. Digital breast tomosynthesis (DBT) has shown improved screening performance compared with digital mammography (DM), although the modality is less well-studied in women 40-44 years old and 75 years and older. OBJECTIVE. The purpose of this study was to compare screening performance between DM and DBT in Tomosynthesis Mammographic Imaging Screening Trial (TMIST) Lead-In trial (A4705) participants who were ineligible for transition to the full TMIST trial (EA1151) due to age at trial entry (40-44 or ≥ 75 years). METHODS. A4705, a prospective trial recruiting women 40 years old and older at four Canadian sites from October 2014 to July 2017, randomized participants to undergo multiple screening rounds by DM or DBT. Final EA1151 eligibility incorporated a narrower age range of 45-74 years. This unplanned analysis included a subset of A4705 participants ineligible for transition to EA1151 due to age at entry (40-44 or ≥ 75 years old). Examination-level screening performance metrics were calculated. The reference standard was determined by 1-year follow-up after participants' last screening round. RESULTS. The study included 271 A4705 participants (mean age, 54 ± 17 [SD] years) who were age-ineligible for EA1151; 181 were 40-44 years old (76 and 105 randomized to DM and DBT, respectively), and 90 were 75 years old or older (46 and 44 randomized to DM and DBT, respectively). Participants in the DM and DBT arms underwent 389 and 482 screening examinations, respectively (mean, 3.2 screening rounds per participant in each arm). Eight cancers were diagnosed (seven screen-detected [one by DM; six by DBT]; one interval cancer in the DBT arm). In participants 40-44 years old, DM, compared with DBT, exhibited a recall rate of 13.2% versus 12.0%, a cancer detection rate (CDR) per 1000 examinations of 0.0 versus 14.6, a PPV1 of 0.0% versus 12.2%, and a PPV3 of 0.0% versus 83.3%, respectively. In participants 75 years old and older, DM, compared with DBT, exhibited a recall rate of 10.1% versus 3.6%, a CDR per 1000 examinations of 7.2 versus 7.1, a PPV1 of 7.1% versus 20.0%, and a PPV3 of 50.0% in both arms. CONCLUSION. Screening performance metrics were overall more favorable for DBT than for DM in women 40-44 years old and 75 years old and older. CLINICAL IMPACT. DBT may mitigate limitations of DM and improve screening performance in the evaluated age groups.

To validate a lesion masking prediction model, Mammatus, previously developed on a North American cohort, on a larger retrospective breast cancer screening cohort from a single center in the Netherlands. Mammatus was applied to all digital mammography screening examinations with a unilateral invasive breast cancer that was either diagnosed at screening or within 24 months after a negative screening, called interval cancers. All mammograms were retrospectively evaluated for the visibility of malignant masses using all available imaging and clinical information. The area under the receiver operator characteristic (ROC) curve (AUC) when using Mammatus to distinguish examinations with screen-detected cancers (assumed low masking risk) from interval cancers (assumed high masking risk) was computed. The AUC was compared to that of the original cohort and to that obtained using volumetric breast density (VBD) as a predictor. A second tghree-category ROC analysis was performed, with interval cancers that were retrospectively visible classified as intermediate lesion masking. Mammatus achieved an AUC of 0.69 (95

ImportanceEvolving breast cancer treatments have led to improved outcomes but carry a substantial financial burden. The association of treatment costs with the cost-effectiveness of screening mammography is unknown.ObjectiveTo determine the cost-effectiveness of population-based breast cancer screening in the context of current treatment standards.Design, Setting, and ParticipantsIn this economic evaluation, the Canadian Partnership Against Cancer/Statistics Canada OncoSim-Breast microsimulation model was used to estimate the impact of various screening schedules in terms of clinical outcomes and treatment costs. Breast cancer treatment costs were derived from activity-based costing published in 2023 specific to a publicly funded health system in Ontario, Canada. A single birth cohort of individuals assigned female at birth in 1975 was modeled until death or age 99 years (whichever came first).ExposuresFive screening scenarios were modeled: no screening, biennial (ages 50-74 years and 40-74 years), hybrid (biennial ages 40-49 years and annual ages 50-74 years), and annual screening (ages 40-74 years).Main Outcomes and MeasuresIncremental cost-effectiveness ratios for deaths averted, life-years (LYs) gained, and incremental cost-utility ratios for quality-adjusted life-years (QALYs) gained were determined for screening scenarios. Sensitivity analyses were conducted by varying screening participation rates and reducing recall rates to 5% and the estimated mortality benefits of screening.ResultsEarlier initiation of breast cancer screening at age 40 years (vs age 50 years) was associated with improved clinical outcomes (deaths averted, LYs saved, and QALYs gained) and reduced health care spending on breast cancer treatment. From a health system perspective, incremental cost-effectiveness ratios for biennial screening at ages 40 to 74 years compared with biennial screening at ages 50 to 74 years were cost saving, with CAD$49 759 saved per death averted, $1558 per LY saved, and $2007 saved per QALY gained. Annual screening at ages 40 to 74 years was cost-effective while achieving the best breast cancer outcomes, with costs of $25 501 per death averted, $1100 per LY saved, and $1447 per QALY gained compared with the current Canadian standard of biennial screening at ages 50 to 74 years.Conclusions and RelevanceIn this economic analysis, although screening costs increased according to the number of lifetime screens, they were completely or largely offset by reduced breast cancer therapy costs. Digital mammography was a highly cost-effective tool to reduce breast cancer mortality. These results have important policy implications for all single-payer health systems and call for greater investment in screening programs.

BACKGROUND:Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment. METHODS:Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region. RESULTS:A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated. CONCLUSIONS:Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Background Different guideline panels, and individuals, may make different decisions based in part by their preferences. This systematic review update examined the relative importance placed by patients aged ≥ 35 years on the potential outcomes of breast-cancer screening. Methods We updated our searches to June 19, 2023 in MEDLINE, PsycINFO, and CINAHL. We screened grey literature, submissions by stakeholders, and reference lists. We sought three types of preferences, directly through i) utilities of screening and curative treatment health states (measuring the impact of the outcome on one’s health-related quality of life), and ii) other preference-based data, such as outcome trade-offs, and indirectly through iii) the relative importance of benefits versus harms inferred from attitudes, intentions, and behaviors towards screening among informed patients. For screening we used machine learning as one of the reviewers after at least 50% of studies had been reviewed in duplicate by humans; full-text selection used independent review by two humans. Data extraction and risk of bias assessments used a single reviewer with verification. Our main analysis for utilities used data from utility-based health-related quality of life tools (e.g., EQ-5D) in patients. When suitable, we pooled utilities and explored heterogeneity. Disutilities were calculated for screening health states and between different treatment states. Non-utility data were grouped into categories and synthesized with creation of summary statements. Certainty assessments followed GRADE guidance. Findings Eighty-two studies (38 on utilities) were included. The estimated disutilities were 0.07 for a positive screening result (moderate certainty), 0.03-0.04 for a false positive (FP; “additional testing” resolved as negative for cancer) (low certainty), and 0.08 for untreated screen-detected cancer (moderate certainty) or (low certainty) an interval cancer. At ≤12 months, disutilities of mastectomy (vs. breast-conserving therapy), chemotherapy (vs. none) (low certainty), and radiation therapy (vs. none) (moderate certainty) were 0.02-0.03, 0.02-0.04, and little-to-none, respectively. Over the longer term, there was moderate certainty for little-to-no disutility from mastectomy versus breast-conserving surgery/lumpectomy with radiation and from radiation. There was moderate certainty that a majority (>50%) and possibly large majority (>75%) of women probably accept up to six cases of overdiagnosis to prevent one breast-cancer death.Low certainty evidence suggested that a large majority may accept that screening may reduce breast-cancer but not all-cause mortality, at least when presented with relatively high rates of breast-cancer mortality reductions (n=2; 2 and 5 fewer per 1000 screened), and at least a majority accept that to prevent one breast-cancer death at least a few hundred patients will receive a FP result and 10-15 will have a FP resolved through biopsy. When using data from studies assessing attitudes, intentions, and screening behaviors, across all age groups but most evident for women in their 40s, preferences reduced as the net benefit presented by study authors decreased in magnitude. In a relatively low net-benefit scenario, a majority of patients in their 40s may not weigh the benefits as greater than the harms from screening (low certainty evidence). A large majority of patients aged 70-71 years probably think the benefits outweigh the harms for continuing to screen. A majority of women in their mid-70s to early 80s may prefer to continue screening. Conclusions Evidence across a range of data sources on how informed patients value the potential outcomes from breast-cancer screening will be useful during decision-making for recommendations. Further, the evidence supports providing easily understandable information on possible magnitudes of effects to enable informed decision-making. Systematic review registration : Protocol available at Open Science Framework https://osf.io/xngsu/

Prospective clinical trials on breast cancer screening take many years to provide results and are very costly. It is simply not feasible to answer all important questions by conducting a study. In addition, there are many inter-related variables that can affect screening outcomes and it is often not possible to study these individually through trials. Microsimulation modeling provides a practical alternative which allows the estimates of different key outcomes of screening in response to changes in the underlying human and technical variables. OncoSim Breast is part of a suite of specialized cancer microsimulation models developed by Statistics Canada in collaboration with The Canadian Partnership Against Cancer. The model simulates a cohort of women from birth to death through individual histories. At its heart is a mathematical function describing tumor growth. As women progress through life, at each time point, calculations are performed through random number selection, weighted by empirical probability data for each phenomenon in the simulation. The model is adapted to a particular problem by creating "scenarios" specifying the assumptions regarding the members of the cohort and any screening intervention(s) and treatment. We demonstrate how it can be helpful in optimizing screening regimens, predicting the impact of technical innovations and improvements and studying other problems where trials would be difficult or impossible to perform.

In many mammography facilities only the processed mammograms are preserved to reduce the space requirement and cost of digital archiving. The original unprocessed "raw" mammograms are preferred for quantitative analysis, since they more faithfully represent the x-ray transmission pattern and thus the breast composition. We present the results of a machine learning algorithm that attempts to restore a raw mammogram from its processed version. In this study, 2776 paired sets of the two image types were obtained, corresponding to 635 patients. The machine learning model used was based on a U-Net with attention gates on the long skip connections. A two-pass learning approach was used. The first pass used a mean-squared error loss function with focus on the periphery of the breast, with 5 epochs and a learning rate of 10(-5) to settle the network weights quickly. In a second pass, a perceptual loss function, based on features extracted from a pretrained VGG16 neural net, was used with 15 epochs and a 10(-6) learning rate. When tested on central ROIs, the mean relative absolute difference ( MRAD) and structural similarity index (SSIM) between the original and restored raw images were 0.04 and 0.98, respectively. On the complete (but downsampled) images, MRAD and SSIM were 0.10 and 0.99, respectively. Lesion detectability and cancer masking potential were also measured on the original and restored raw images, showing Pearson correlations of 0.89 in both cases. The algorithm shows potential for using the restored raw images from processed images for the purposes of quantitative analysis. Future work will extend the approach to higher resolution images to preserve detail and more efficient network architectures to reduce memory requirements.

Immune phenotype data, specifically the description of densities and spatial distribution of immune cells are now frequently included in the clinical pathology report as these features of the cells in the tumor microenvironment (TME) have shown to be associated with prognosis. In addition, immune-therapeutics, which aim at manipulating the patients' immune system to kill cancer cells, have recently been approved for treatment of triple-negative breast cancers (TNBCs). Thus, quantifying the immune phenotype of the cancer could be important both for prognostication, and for prediction of therapy response. We have studied the immune phenotype of 42 breast cancers using immunofluorescence protein multiplexing and quantitative image analysis. After sectioning, formalin-fixed paraffin-embedded tissues were sequentially stained with a panel of fluorescently-labelled antibodies and imaged with the multiplexer (Cell DIVE, Leica Biosystems). Composite images of antibody-stained sections were then analysed using specialized digital pathology software (HALO, Indica Labs). Binary thresholding was conducted to identify and quantify densities of various immune lineage subsets (T lymphocytes and macrophages). Their cellular localisation was mapped and the spatial features of cellular arrangement were evaluated using a k-nearest neighbor graph ( KNNG) method and Louvain community-proximity clustering. The spatial relationship of various immune and cancer cell types was quantified to assess whether cellular arrangements and structures differed among breast cancer subtypes. Our work demonstrates the use of molecular and cellular imaging in quantifying features of the tumor microenvironment in breast cancer classification, and the application of KNNG in studying spatial biology.

Abstract Background The United States Preventative Services Task Force in their 2023 recommendations identified areas where more research data is needed to inform future breast cancer screening recommendations. Research areas identified are: improve clinicians and patients understanding and evaluation of dense breast tissue on a screening mammogram, benefits and harms of supplemental screening using ultrasound or MRI for women with dense breasts, health outcomes such as rates of breast cancer diagnosis requiring treatment, rates of advanced breast cancers diagnosed across consecutive screening rounds, and breast cancer-associated morbidity and mortality, causes of increased risk of breast cancer mortality in black women across spectrum of stages and biomarker patterns, understand why black women are more likely to be diagnosed with breast cancers that have biomarker patterns that are indicative of poor health outcomes, assess benefits/harms differences between annual and biennial screening for breast cancer in women overall and if there are differences between black and white women, approaches to reduce the risk of overdiagnosis leading to overtreatment of breast lesions found at screening that may not cause morbidity and mortality, natural history of DCIS, and identify prognostic indicators of breast tumors that are unlikely to affect quality or length of life. Methods The ongoing TMIST study, currently with 88,801 asymptomatic women presenting for screening mammography ages 45-74 enrolled out of 128,905, could contribute to scientific evidence to support the above research areas through existing study aims and planned ancillary studies. Supplemental Screening with US and MRI: TMIST PreSCRIB will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to recommend individualized screening strategies for women. DxMRI is a study where women will get AbMRI at time of Dx work-up. There are plans to use these examinations plus supplemental screening MRIs performed on TMIST subjects in an enriched reader study to evaluate the role of supplemental screening MRI in moderate risk women. Rates of breast cancer treatment, consecutive screening, morbidity, and mortality: TMIST’s primary outcome is the proportion of women experiencing an advanced breast cancer and needing treatment. TMIST is also collecting information on health care utilization following a cancer diagnosis, including types of treatment given, and costs data from the screening and diagnostic work-up visits, and mortality data for study participants. Increased risk of breast cancer mortality in black women: TMIST is performing PAM50 plus p53 status, immune profile, DNA repair phenotype, and 21-gene recurrence assay on all breast cancers and a subset of benign tissue. Blood and buccal smears might also help address this issue. Ongoing work, funded by the Susan B. Komen Foundation, focuses on improving Black participation in TMIST Biorepository (currently about 45% participation of the 21% of TMIST US black subjects). Surveys are planned on perceived racism and social determinates of health as part of DxMRI Study. Screening Frequency: We are developing a collaboration with the UK-based clinical trial PROSPECTS to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening. Overdiagnosis, natural history of DCIS, prognostic indicators of breast tumors not impacting quality of life: PRoGram- will use radiomics, genomics and pathomics to develop a greater understanding of the variability of the non-advanced cancers diagnosed in the TMIST population, including DCIS. It is hoped that this model will provide greater understanding of the risk of poor outcomes for women diagnosed with lower risk cancers, including DCIS. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole, Jean Cormack, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies [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 PO3-19-03.

Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.

Raw cell counts of FISH gene probes for (1) primary breast tumors, (2) metastatic sites, (3) pre- and post-treatment [Hungarian sample], and (4) TNBC cases.

Beginning around 1972 with the introduction of CT, a steady transition from analog to digital imaging in radiology took place. Here, I offer a personal perspective of the exciting multi-institutional and multidisciplinary team effort of developing digital mammography. That effort required the collaboration of visionary individuals in academic research labs, industry, and the clinical arena, catalyzed by a focused commitment from government (NCI and The Office of Women's Health). This collaboration greatly accelerated the timeline from laboratory prototypes to clinical systems and evaluation, resulting in a new imaging modality and, later, several spinoff applications (CAD, contrast-enhanced mammography, tomosynthesis) that provide improved earlier detection of breast cancer.

Supplementary Table 1. Relationship of square-root transformed percent density to age at menarche and late adolescent BMI; Supplementary Table 2. Relationship of cube-root transformed dense area to age at menarche and late adolescent BMI ;Supplementary Table 3. Effect modification by menopause of the associations of late adolescent BMI with percent density and dense area.

Mammography screening is widely used for earlier detection of breast cancer and has been shown to contribute to reduction of mortality and morbidity. Most screen-detected breast cancers are early stage, hormonal receptor-positive, HER2-negative (HR+ HER2-) breast cancers. The majority of HR+ HER2- cancers are assigned to molecular intrinsic subtypes of Luminal A and Luminal B, which generally harbour low recurrence risk, with Luminal B cases being more invasive but still less aggressive than HER2-enriched or Basal-like subtypes. However, some of these Luminal cancers later recur (5+ years after diagnosis) often as advanced and/or metastatic disease. We studied a cohort of screen-detected breast cancers (42 cases) at Sunnybrook Health Sciences Centre (Toronto, ON Canada) with integrated cross-platform radiomics, molecular and proteomic analysis in an attempt to better characterise these cancers and their proclivity for late recurrence. Utilising a Nanostring 200-gene assay, the molecular subtypes (PAM50 and MammaTyper-like) and a range of recapitulated clinical prognostic scores (50-Gene, 70-Gene and 21-Gene Risk) were determined for these cancers. While a majority of cases (29 out of 42) were subtyped as Luminal A cancers by PAM50, a fraction (12 out of 29) of these were either subtyped as Luminal B/HER2+ based on MammaTyper-like results, or measured as intermediate to high risk of recurrence based on the 70-Gene or 21-Gene Risk classification. Although discordant results from multi-parametric assays are not uncommon, we postulate that here, discordance could suggest presence of heterogeneous cellular or molecular elements with invasive phenotype that could lead to aggressive transformations in the long run. Differential expression analysis between Luminal A cases with consistent subtyping and low risk prognostication and those with discordant subtyping or prognostication results revealed a panel of genes with significant changes in expression. Further analysis of RNA expression of these genes via Receiver Operating Characteristic (ROC) curve demonstrated that some genes showed high Area Under the Curve (AUC) scores in the classification between the two groups of Luminal A breast cancers, further supporting the existence of distinct molecular phenotypes. Targeted sequencing with Oncomine Comprehensive Assay V3 did not reveal particular mutational patterns between the two Luminal A groups. Nevertheless, radiomic analysis of mammographic images of the cancer, as well as single cell phenotyping of the tumor microenvironment with protein multiplexing will be incorporated to further characterise elements that are phenotypically different, and potentially identify mechanistic drivers contributing to late recurrence in Luminal A cancers. Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Yutaka Amemiya, Elzbieta Slodkowska, James G. Mainprize, Jane Bayani, Arun Seth, John Bartlett, Martin J. Yaffe. Integrative cross-platform characterisation of mammographic screen-detected breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5622.