Benign Breast Disease Research Articles

Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

Multiplicity of benign breast disease lesions and breast cancer risk in African American women.

The risk of developing subsequent breast cancer is higher in women diagnosed with benign breast disease (BBD) but these studies were primarily performed in non-Hispanic white populations. Still, these estimates have been used to inform breast cancer risk models that are being used clinically across all racial and ethnic groups. Given the high breast cancer mortality rates among African American (AA) women, it is critical to study BBD in this population, to ensure the risk models that include this information perform adequately. This study utilized data from AA women who underwent benign breast biopsies at a hospital served by the University Pathology Group in Detroit, Michigan, from 1998 to 2010. Patients were followed for subsequent breast cancers through the population-based Metropolitan Detroit Cancer Surveillance System (MDCSS). BBD lesion scores were assigned to represent the severity or extent of benign breast lesions, with higher scores indicating a greater number of distinct lesion types. Of 3,461 eligible AA women with BBD in the cohort, 6.88% (n=238) subsequently developed breast cancer. Examined individually, six of the eleven lesions (apocrine metaplasia, ductal hyperplasia, lobular hyperplasia, intraductal papilloma, sclerosing adenosis, columnar alterations and radial scars) were significantly associated with increased risk of breast cancer after adjustment for age and year of biopsy and were further considered in multiple lesion models. For every different type of benign breast lesion, subsequent risk of breast cancer increased by 25% (RR=1.25, 95% CI: 1.10, 1.42) after adjustment for age at biopsy and proliferative versus non-proliferative disease. In summary, this study affirms the increased breast cancer risk in AA women with BBD, particularly in those with multiple lesions. These findings have implications for the management of breast cancer risk in millions of women affected by BBD, a high risk group that could benefit from personalized surveillance and risk reduction strategies.

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

ObjectiveTo evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree’s outcomes. Materials and MethodsImaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model. ResultsSize, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05). ConclusionThe diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions

ObjectiveThe aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics.MethodsA systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated.ResultsThis study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83–0.92) and 0.79 (95% CI 0.72–0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1–5.7), 0.15 (95% CI: 0.10–0.22), and 29.0 (95% CI 15–55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87–0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes.ConclusionMRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients’ medical outcomes and quality of life.

Risk factors for breast cancer in patients with benign breast diseases: a 10-year observational study

Aim. To assess risk factors for breast cancer in a large population of women with benign breast dysplasia (BBD) followed up for a long time.Materials and methods. This retrospective study included 504 women with BBD followed up for 10 years. Study participants were divided into two groups: 1) BBD patients who did not develop breast cancer within 10 years (n = 261); 2) BBD patients who developed breast cancer within 10 years (n = 243). We conducted comprehensive retrospective analysis of demographic, clinical, and laboratory characteristics, as well as treatment details for BBD. Patient age varied between 39 and 49 years. The ten-year follow-up interval was chosen to perform adequate comparison of the groups. The differences were considered significant at p &lt;0.05.Results. The main difference between the two groups was related to conservative treatment for BBD and its duration. Almost all (93.4 %) patients in the group without breast cancer received some conservative treatment for BBD for 78.0 ± 1.7 months, whereas among those who developed breast cancer, only 37.4 % received conservative therapy for 15.0 ± 0.8 months (p &lt;0.001). Other significant risk factors for breast cancer in group 2 included obesity, gynecological pathology, long-term stress, and no surgical treatment for benign breast changes (p &lt;0.001). The analysis of laboratory parameters demonstrated significantly higher levels of prolactin in patients who developed breast cancer (31 % versus 13 %; p &lt;0.001).Conclusion. Potential risk factors for breast cancer in BBD patients include overweight, benign gynecological diseases, elevated prolactin, and long-term stress. Timely diagnosis and treatment of BBD and elimination of the abovementioned factors can significantly reduce the probability of breast cancer.

Mammography of suspicious calcifications among ductal carcinoma in situ and benign breast disease.

Most ductal carcinoma in situ (DCIS) lesions manifest early as calcifications, which could be benign or malignant. The classified group of suspicious calcifications among DCIS and benign breast disease is clinically important to early evaluate patient risk factors and plan treatment options. To compare imaging features of suspicious calcifications between DCIS and benign breast disease. A retrospective study of 101 suspicious calcifications was performed at Thammasat University Hospital from June 2011 to October 2020. The calcifications were surgically excised by mammography-guided wire localisation. The mammographic features of the suspicious calcifications were reviewed according to the fifth edition of the American College of Radiology Breast Imaging-Reporting and Data System lexicon. For comparing between two groups, the student t-test, Fisher's exact test and Mann-Whitney U test were used for statistical analyses. The logistic regression analysis was calculated for DCIS prediction. The pathologic results of all 101 suspicious calcifications were DCIS (30 cases) and benign breast disease (71 cases). Linear morphology and segmental distribution correlated significantly with DCIS (p = 0.003 and p = 0.024, respectively). After multivariable analysis, fine linear calcification still significantly elevated the risk of DCIS (odd ratios, 51.72 [95% confidence interval: 2.61, 1022.89], p-value of 0.01), however, the odds of predicting DCIS was not statistically significant different among any distribution. Ductal carcinoma in situ calcification has contrasting morphology and distribution features compared to benign breast disease. The calcification descriptor is considered an important implement for early diagnosis and distinguishes DCIS from other benign breast conditions. Calcification descriptor is considered an important implement for early diagnosis and distinguishment of DCIS from other benign breast conditions.

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions.

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

Efficacy of the minimal-invasive vacuum-assisted biopsy under direct visualization with ultrasound for impalpable breast lesions in Taiwanese female: A retrospective case-control study

Abstract Background Although surgical excisional biopsy provides robust diagnostic results, the ultrasound-assisted vacuum-assisted biopsy (VABB) offers an alternative for Asian candidates with dense breasts and lesions which cannot be approached by stereotactic biopsy. The study aims to investigate the efficacy of the ultrasound-assisted VABB for impalpable mammographic-detected lesions. Methods A retrospective case-control study is conducted in a group of Taiwanese female patients who have impalpable breast lesions such as microcalcifications at screening mammography that are limited to BI-RADS category 4. Specifically, the lesions are categorized into three groups: 4a, 4b, 4c, and the histology from B2 through B5. The cases underwent ultrasound-assisted VABB and the controls had lumpectomies. The diagnostic accuracy, rate of discordance, excited tumor size, and the proportion of cancer cases is evaluated and compared using Fisher’s Exact test, Cohen’s kappa test, Chi-Square test, and the use of IBM SPSS statistical programs. Results The histology of the majorities of the collected specimen are B2 benign fibrocystic changes (60-70%). In cases of patients who are diagnosed with breast cancer, most are ductal carcinoma in situ and early-stage diseases. The cancer risks for lesions categorized under BI-RADS 4a, 4b, 4c respectively are consistent with clinical practice. Meanwhile, the diagnostic accuracy of VABB is similar to lumpectomy, as the histologic discordance rate did not differ significantly. Conclusions Ultrasound-assisted VABB does offer an option for patients who are not candidates for stereotactic biopsy, as it has shown to have non-inferior diagnostic values and cosmetic benefits when compared to surgical excision. The use of sonography monitors the biopsy process and guides the needle insertion thus is capable of obtaining samples from multiple directions, without risking the pectoralis muscles underneath. However, VABB may be intended for biopsies in lower-risk findings such as BI-RADS category 4a because doubts persist as complete excisional rates are operator-dependent.

Abstract PS07-02: Benign breast disease and microcalcifications in percutaneous biopsies and breast cancer risk

Abstract Objectives: Mammographic microcalcifications (calcs) are a sentinel marker of in-situ and invasive breast cancer (BC) but most calcs reflect benign breast disease (BBD). BBD comprises a diverse set of lesions, variably associated with increased BC risk. While calc frequency data for the modern era are limited, historically, calcs were reported in 11% of BBD biopsies. Some studies suggest that calcs in BBD impact BC risks; however, the frequency and BC risks associated with calcs during the percutaneous biopsy era have not been clearly established. Accordingly, we assessed the association of calcs with specific BBD lesions and BC risk in a large contemporaneous BBD cohort including detailed pathology review. Methods: A cohort of 4,819 patients with percutaneously diagnosed BBD from 2002-2013 at Mayo Clinic, were identified. The cohort was followed from 6 months after biopsy until censoring, BC diagnosis, or December 2021. Mean follow-up was 11.3 (SD=5.1) years for BBD patients who remained cancer-free in follow-up compared with 7.4 (SD=4.3) years for cases. Histology of all biopsies were reviewed microscopically to record pathologic findings. A natural language processing algorithm (NLPA) was developed and utilized to identify the presence of calcs based on pathology reports. NLPA results were compared to 300 matched biopsy reports, taken as ground truth. We performed age-adjusted Cox proportional hazards regression to assess hazard ratios (HRs) with 95% confidence intervals (CIs) for specific BBD lesions, stratified by the presence (calc+) or absence of calcs (calcs-), with estimation of an interaction term to test for differences in risks by calc strata. Standardized incidence ratios (SIRs) estimated risks for calc+ and calc- BBD lesions versus matched strata for Iowa SEER population-based BC incidence rates. Results: The NLPA achieved sensitivity=0.993 and specificity=0.986. Overall, 42.8% of BBD biopsies were calc+, including 49.1% of biopsies preceding BC and 42.4% of biopsies not preceding BC (Chi-square p=0.02). Overall, risks of BC among women with BBD calc+ biopsies did not differ significantly from those of women with calc- BBD: HR=1.13 (95% CI: 0.90,1.41), p=0.290. SIRs were also similar for the two groups; women with BBD calc+ biopsies had an SIR =2.00 (95% CI: 1.72, 2.33) and BBD calc- biopsies yielded an SIR= 1.91 (95% CI: 1.64,2.22) (Table 1). HRs were similar for specific calc+ and calc- BBD lesions, with progressively higher risks for non-proliferative disease (NP), proliferative disease without atypia (PDWA) and atypical hyperplasia (AH) (pinteraction=0.55). Interaction terms were not significant for any specific lesion, apart from a marginally significant result for dilated ducts, wherein risks were marginally higher for calc- lesions (pInteraction =0.048), perhaps representing a chance finding. SIRs for calc+ versus calc- BBD lesions were also similar. Overall, 77.3% of AH was calc+; for AH calc+, SIR=3.76 (95%CI: 2.74-5.17) and for AH calc-, SIR=4.41 (95%CI: 2.56-7.59). Conclusion: In this recent, larger percutaneous BBD cohort, calcs were present in 42.8% of all specimens, representing a marked increase compared with historical BBD data, particularly those dominated by surgical biopsy for diagnosis and preceding implementation of full-field digital mammography. In this study, calcs in percutaneously diagnosed BBD were not associated with increased BC risk. Table 1: Comparison of standard incidence ratio of subsequent breast cancer with and without microcalcifications in benign breast disease by histologic impression (NP = non-proliferative disease, PDWA = proliferative disease without atypia, and AH = atypical hyperplasia). (1) compared to Iowa SEER population Citation Format: Sarah Schrup, Robert Vierkant, Stacey Winham, Celine Vachon, Derek Radisky, Jodi Carter, Laura Pacheco-Spann, Matthew Jensen, Amy Degnim, Mark Sherman. Benign breast disease and microcalcifications in percutaneous biopsies and breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-02.

Abstract PO3-13-08: D17104: Aryl Hydrocarbon Receptor Signaling and Other Biomarkers of Fat Metabolism in Human Mammary Adipose Tissue from Women with Malignant Breast Disease

Abstract Background: Obesity is a recognized risk factor for the development of metabolic disease and breast cancer possibly due to chronic inflammation mediated by cytokines secreted by adipose tissue. Given this, our group examined differential expression of several markers of chronic inflammation in adipose tissue of both benign and malignant breast tissue from patients. The aryl hydrocarbon receptor (AhR) is an important cytosolic, ligand-dependent transcription factor found within hepatocytes and adipocytes that is upregulated in many types of cancer. AhR promotes the initiation, progression, and metastasis of cancer cells allowing certain cancers to evade immune recognition (Xue et al. Front. Immunology 2018). Indoleamine 2,3-dioxygenase 1 (IDO1) is an enzyme that generates kynurenine (KYN) from tryptophan (TRP), which acts as an AhR agonist. We sought to determine expression of IDO1 via immunohistochemistry (IHC) in mammary adipose tissue associated with benign or malignant breast. We hypothesized that adipose tissue adjacent to malignant breast tissue may have heightened expression of IDO1 in addition to Cyp1b1, and SPP1; both of which are also markers of increased aryl-hydrocarbon transcriptional activity, compared to adipose tissue noncontiguous to malignant lesions. KYN affects gene expression for insulin resistance and levels are elevated in plasma of older obese women compared to lean women; however, the relationship to breast malignancy is not known. Methods: IDO1 (Cell Signaling, 1:200), Cyp1b1 (Thermo Fisher, 1:300), and SPP1 (Thermo Fisher, 1:200) IHC was performed according to the manufacturers' protocols. Adipose tissue immediately adjacent to, and &amp;gt;2 mm away from, tumors (three DCIS, 11 invasive carcinomas) was evaluated in 14 patients. Adipose tissue in separate tissue blocks from benign breast tissue (three ipsilateral, 11 contralateral) in the same patients was also evaluated. Human plasma KYN levels were assayed by HPLC in the Dartmouth Clinical Pharmacology Shared Resource as described previously (Rojas et al. Obesity 2021). Results: In this preliminary pilot study, there was no difference in staining pattern for IDO1 in adipose tissue present in malignant and benign samples. Two patients showed increased Cyp1b1 and SPP1 staining immediately adjacent to the invasive carcinoma (Figure 1). Patients with benign breast disease did not show increased Cyp1b1 or SPP1 staining. Results will be confirmed using qPCR for mRNA. Plasma samples from patients with benign disease showed a lower level of KYN expression on average (0.370 ug/mL, N = 8) compared to plasma from patients with malignant disease (0.424, N = 10). Additionally, the ratio of KYN:TRP was lower in the benign group (0.065) compared to the cohort with malignancy (0.080). Univariate linear regression will be used to further assess relationships between continuous variables in the study population (e.g., patient age, BMI, KYN plasma levels, benign vs malignant breast disease). Regression lines and the associated slope estimates (b-value) and p-values for regression models will be provided on scatter plots. Conclusions: While there was no difference in IDO1 staining patterns in adipose tissue of malignant and benign samples in this group, tissue samples from patients with invasive carcinoma demonstrated increased Cyp1b1 and SPP1 staining adjacent to the malignant tumor; while benign tissue samples did not show a positive stain. This suggests increased aryl-hydrocarbon transcriptional activity in invasive breast carcinoma—further linking AhR signaling with adipose tissue immune dysregulation and invasive malignancy. Additionally, KYN plasma levels were higher in patients with malignant disease indicating a possible relationship between insulin resistance and breast malignancy. Additional research may offer new insights relating to this important pathway and ultimately treatments aimed at blunting its nefarious effects. Citation Format: Adam Ephraim, Jonathan Pirruccello, Scott Palisoul, Jonathan Marotti, Mary Chamberlin. D17104: Aryl Hydrocarbon Receptor Signaling and Other Biomarkers of Fat Metabolism in Human Mammary Adipose Tissue from Women with Malignant Breast Disease [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 PO3-13-08.

Abstract PO5-14-04: FTIR Spectroscopy Analysis of Lipid Region: Distinguishing Breast Cancer from Benign Breast Diseases Using PCA-SVM

Abstract Background: Breast cancer (BC) is the most diagnosed type worldwide, with 2.26 million new cases in 2020. Mammography is the preferred screening method, but it has limitations such as radiation exposure and low sensitivity in dense breasts. Therefore, there is a need for accurate diagnostic methods to detect breast cancer and distinguish benign conditions. Objective: This study aimed to develop an automated tool for the comparison of lipid spectral region in benign breast disease (BBD) and breast cancer (BC) to determine their potential diagnostic value using Fourier Transform Infrared Spectroscopy (ATR-FTIR). Methods: This study was conducted at a Clinical Hospital in Uberlandia, MG, Brazil, after approval by the Human Research Ethics Committee of the Federal University of Uberlandia, and all subjects provided written informed consent. Women who went to the Clinical Hospital for breast surgery were invited to participate in the study as volunteers. After surgery and histopathological analysis, the tumors and lesions found were classified according to histological type, staging, and the status of ER, PR, and HER2 receptors. A total of 60 women participated in the study, of whom 27 had BBD, and 33 had BC. Spectra were measured in the wavenumber range of 4000 cm-1 to 650 cm-1 using an Agilent Cary 600 Series FTIR spectrometer coupled to an MCT detector. The air spectrum was used as a background before each sample analysis. The sample spectra were obtained in triplicate, with a spectral resolution of 4 cm-1, and 128 scans were performed for each measurement. The infrared spectra were analyzed after undergoing preprocessing, which involved positive baseline rubberband normalization, normalization by minimum and maximum, and application of the second derivative. Results: The region of 3050-2800 cm-1 was able to differentiate BC from BBD using principal component analysis (PCA) followed by the support vector machine algorithm (PCA-SVM) with an accuracy of 80%, sensitivity of 88%, and specificity of 70%. This range corresponds to the vibration of lipids, and it is known that changes in the contents of biomolecules in the serum, an increase or decrease, can be related to the presence or absence of BC. The implementation of this approach in clinical practice may automate the diagnosis of BC, monitoring whether changes in therapy or interventions are necessary throughout the treatment. FTIR can potentially assist in clinical decision-making regarding follow-up or biopsy recommendations, avoiding unnecessary biopsies that are more invasive and expensive than blood collection, and which often cause significant anxiety to the patient even if the lesion is not highly suspicious. Conclusion: This study shows that it is possible to detect BC with good accuracy through a minimally invasive, fast, and cost-effective method analyzing the lipid spectral region using principal component analysis (PCA) followed by the support vector machine algorithm (PCA-SVM). Citation Format: Juliana Pereira, Alinne Faria, Izabella Ferreira, Letícia Santos, Donizeti Santos, Marcelo Maia, Ohanna Costa, Raul Freitas, Carlos Paiva, Yara Maia. FTIR Spectroscopy Analysis of Lipid Region: Distinguishing Breast Cancer from Benign Breast Diseases Using PCA-SVM [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 PO5-14-04.

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

Abstract Background: Benign breast disease (BBD) is a common breast biopsy finding encompassing a diverse spectrum of diagnoses. Breast cancer risk associated with many specific BBD diagnoses and their joint associations with breast density have not been extensively studied. We estimate the future risk of invasive breast cancer associated with specific BBD diagnoses typically categorized into the broad category of proliferative lesions without atypia (PWoA) and evaluate whether associations differ by breast density or by presence of calcifications. Methods: We included 1,313,943 women ages 40-79 years who underwent 5,282,063 mammograms in the Breast Cancer Surveillance Consortium from 1996 to 2019 with no prior history of breast cancer. We fit a Cox proportional hazards model to estimate hazard ratios associated with each combination of PWoA BBD diagnoses, whether calcifications were present on biopsy, and breast density. We used classification trees to group combinations of PWoA diagnoses, breast density, age groups, and presence of calcifications with similar magnitudes of associations with 5-year risk of invasive breast cancer. Results: Compared with women with a PWoA BBD diagnosis without calcifications, presence of calcifications significantly elevated risk for breast cancer (HR=1.19, 95% CI = 1.02 to 1.41, P=0.03). Considering specific PWoA diagnoses, classification trees identified groups of women with higher and lower invasive cancer risk than expected risk for all PWoA combined. Among women ages 60-79 years with heterogeneously or extremely dense breasts, the 2.6% of women with any PWoA diagnosis, except papillomatosis, were at high invasive breast cancer risk (5-year risk &amp;gt;2.5%) (Table 1). Among women ages 40-59 years with dense breasts, presence of calcifications with papillomas, usual ductal hyperplasia, columnar cell hyperplasia, or PWoA NOS diagnoses put these women at high risk (5-year risk &amp;gt;2.5%) compared to women with the same BBD diagnoses without calcifications who were at intermediate or average risk (1-2.49%) (Table 1). Among women ages 60-79 years with non-dense breasts, the 1.6% of women with papilloma, usual ductal hyperplasia, radial scar, or PWoA NOS diagnoses were at high breast cancer risk (Table 2). Among women ages 40-59 years with non-dense breasts, presence of calcifications with usual ductal hyperplasia or PwoA NOS diagnoses put these women at intermediate risk (1.67-2.49%) compared to women with these BBD diagnoses without calcifications who were at average risk (1-1.66%) (Table 2). Women 60-79 years with fatty breasts were at intermediate 5-year risk if they had papillomas, usual ductal hyperplasia, or PWoA NOS diagnoses. Women 40-59 years with fatty breasts were at low risk of breast cancer regardless of PWoA diagnosis (Table 2). Conclusion: Specific BBD diagnoses and the presence of calcifications can change a woman’s predicted 5-year breast cancer risk compared to broad BBD categories alone. This information could be incorporated into risk prediction models to improve model accuracy. Citation Format: Olivia Sattayapiwat, Karla Kerlikowske, Donald Weaver, Alexander Borowsky, Theresa Keegan, Brian Sprague, Diana Miglioretti. Advancing Evidence of the Associations Between Specific Benign Breast Diagnoses and Future Breast Cancer RIsk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS01-06.

Abstract PO5-07-08: A Serum Biomarker for the Early Detection of Breast cancer

Abstract Background: After decades of use, clinicians and medical societies are still debating on the clinical utility of mammography, the standard of care for breast cancer (BC) screening, because of its limitations such as subjective visual interpretation, high rate of false-positive and false-negative (in dense breast) and concerns over radiation exposure. There are 55 million women, and about 45% of them considered dense breast, in need of an accurate, easy to use test for the screening of BC regardless of breast tissue complexities to differentiate between BC and benign findings such as calcification and fibrosis. To this date there is no blood protein biomarker (BM) in clinical use for the early detection of BC. We have developed a novel BC specific protein biomarker -based blood test to detect patients with early-stage BC and established herein the diagnostic performance of our BM in discriminating BC patients from healthy individuals and benign breast cases. Methods: A total of 433 serum samples (318 primary BC, 32 benign breast and 83 normal breast) were obtained from the Cooperative Human Tissue Network and from the IRCCS San Raffaele Pisana Research Center (blind samples). 247 BC samples were provided as blind samples and were used to validate the screening utility. Serum samples were tested with our BC specific BM by sandwich ELISA. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic performance, by determining sensitivity (SE), specificity (SP), area under the curve (AUC), and confidence interval (CI). Statistical analysis, ROC curves and scatter plots were performed with GraphPad Prism 7.0. Results: BM elevation in early cancer cases (stage I-II, Ductal Carcinoma) was found to be statistically significant (p&amp;lt; 0.0001) as compared to healthy and benign cases. Our test discriminated early BC patients from non-cancer cases with 87.3% SE at 85.2% SP and was further validated in a blind set including 247 BC (Ductal &amp; Lobular Carcinoma, stage I-III), 30 benign and 83 normal breast cases (88%SE, 86%SP). The sensitivity for early-stage (I-II) and late stage (III) BC was 86.8% and 94.3%, respectively. While lobular carcinoma is particularly not well detected by mammography with a sensitivity as low as 57% and much lower for women with dense breast, it was detected with high sensitivity (90%) in our test. 80% of fibrocystic changes (FCC), the most common form of benign breast disease in women over 50 years of age were negative with our test. Although it is a benign condition, FCC may be misdiagnosed as a malignant lesion by physical examination and imaging. Conclusion: Our results support the utility of our blood-based test for the early detection of breast cancer which presents improved features over mammography such as higher lobular cancer sensitivity, lower detection rate of common form of benign condition often misdiagnosed by imaging, absence of radiation and expected high compliance. Further testing is needed to confirm accuracy of our test in the dense breast population. Citation Format: Christine Chavany, Jeffrey Dea, Monica Guevara, Rafael Hernandez-Gonzalez, Patrizia Ferroni, Fiorella Guadagni, Rosaura Valle, Moncef Jendoubi. A Serum Biomarker for the Early Detection of Breast cancer [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 PO5-07-08.

The value of elastography strain rate ratio in benign and malignant BI-RADS-US 3-4 breast masses.

In the realm of breast diagnostics, Breast Imaging Reporting and Data System (BI-RADS) serves as a systematic framework, guiding a methodical exploration into the nuanced narratives of masses. The current study aims to investigate the value of strain rate ratio (SR) of elastography for benign and malignant breast masses categorized by BI-RADS for Ultrasonography (BI-RADS-US) category 3-4. Ultrasonographic data of 1099 breast masses that underwent both elastography and pathological examination were retrospectively analyzed by non-parametric test, consistency analysis and receiver operating characteristic (ROC) curve sequentially. ROC curve was used to evaluate the diagnostic effect of SR method in different BI-RADS categories of breast lesions. The maximum Youden index obtained from the ROC curve was 0.845. At a cut-off value of 3.57, the SR values' diagnostic sensitivity, specificity, accuracy, positive predictive value and negative predictive value for distinguishing benign and malignant breast lesions were 85.7 %, 98.8 %, 93.6 %, 97.9 % and 91.45 %, respectively. Consistency analysis showed that the consistency between SR value and pathological diagnosis was 93.6 % (κ = 0.864). In addition, the SR values between benign and malignant lesions of BI-RADS 3 and 4 were statistically different (P < 0.001). ROC analysis indicated that the diagnostic area under curve (AUC) for SR value in BI-RADS 3, 4a, 4b and 4c lesions were 0.985, 0.866, 0.793 and 0.916, respectively. In addition, the study observed 58 cases of missed diagnosis and 20 cases of misdiagnosis in evaluating benign and malignant breast lesions. The elastic SR ratio method has a good diagnostic value for the evaluation of breast masses, particularly for lesions with a score of 3-4. The elastic SR ratio method is instrumental in enhancing the accuracy of diagnosis.

Abstract PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P &amp;lt; 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P &amp;lt; 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P &amp;lt; 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [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 PO5-08-10.

Abstract PS10-09: Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium

Abstract Risk prediction models that have been developed for overall breast cancer risk are based on a limited number of premenopausal cases in individual cohorts. As some risk factors differ in their associations with pre- versus postmenopausal breast cancer, a distinct risk prediction model is needed for premenopausal breast cancer. We developed a risk prediction model for premenopausal breast cancer using 779,601 participants and 9,665 incident cases from 18 prospective studies within the Premenopausal Breast Cancer Collaborative Group (PBCCG), across North America (N=9), Europe (N=6), Australia (N=1), and Asia (N=2). Data were split, within each cohort, into training (2/3) and testing (1/3) datasets. Individual risk was assessed in five-year intervals, using variables reported at the start of the time interval. Cox proportional hazards regression was used to model risk factors in a backwards-selection method, stratified by cohort: age at menarche, age at first birth, parity, breastfeeding (months), height (cm), BMI (kg/m2), BMI at age 18, recent weight change (kg), alcohol consumption (drinks/week), first-degree family history of breast cancer, and personal history of benign breast disease. To enable the use of information from all cohorts despite differences in missing variables by design, cohorts were grouped by available variables and risk models were fit for each group. In cohorts with incomplete data, model coefficients were adjusted based on the correlation of covariates with the missing variables in the complete case dataset. Coefficients were meta-analyzed with inverse variance weighting to obtain final coefficients. Discrimination was evaluated in the testing dataset by calculation of the c-index. Work is ongoing to calibrate the model based on five-year absolute risk using GLOBOCAN continent- and age-specific incidence rates to represent baseline risk. The final model included age at menarche, parity, height, BMI, BMI at age 18, first-degree family history of breast cancer, and history of benign breast disease. Young adulthood BMI and BMI (at start of the 5-year risk interval) were associated with a decreased risk (Hazard Ratio (HR) (95% confidence interval (CI)) per 5 kg/m2 = 0.87 (0.81-0.93) and 0.90 (0.86-0.95), respectively) as was parity (HR (95% CI) = 0.92 (0.90-0.94)). Height was associated with increased risk (HR (95% CI) per 10 cm = 1.14 (1.07-1.21)), while history of benign breast disease and family history were associated with larger increases in risk (HR (95% CI) = 1.64 (1.30-2.06) and 1.76 (1.63-1.90), respectively). Model discrimination was higher than that reported for women under 50 years in existing breast cancer risk prediction models considering clinical factors (AUC (95% CI) = 0.61 (0.59-0.62)). Calibration of absolute 5-year risk is ongoing. Several factors driving risk prediction of postmenopausal breast cancer have similar influence on risk of premenopausal breast cancer, while family history has a stronger influence on premenopausal breast cancer risk. Our model demonstrates acceptable discrimination and will enable individual 5-year absolute risk prediction for premenopausal breast cancer. Citation Format: Kristen Brantley, Michael Jones, Minouk Schoemaker, Hazel Nichols, Anthony Swerdlow, Robert MacInnis, Roger Milne, Tess Clenenden, Yu Chen, Xiao-Ou Shu, Wei Zheng, Woon-Puay Koh, Jian-Min Yuan, Cari Kitahara, Martha Linet, Dale Sandler, Bernard A Rosner, Peter Kraft, A. Heather Eliassen. Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium [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 PS10-09.

A Case Series of Round Block Techniques for Large, Recurrent, and Multicentric Benign Breast Diseases.

Benign breast diseases are a common presentation in the breast clinic outpatient department. These diseases, including giant fibroadenoma, multiple fibroadenoma in different quadrants, and large or recurrent phyllodes tumors, pose challenges in surgical management. We present a case series of 16 patients aged 19 to 63 years (average age, 41.5 years) who presented with breast lumps and underwent surgery using the round block technique for benign breast diseases at our institute between November 2019 and March 2024. Prior to surgery, all patients had clinical, radiological, and pathological assessments. Age, duration of lump, and detailed menstrual, obstetric, and family history of each patient were recorded. Eight (50%) of the patients had phyllodes tumor, four (31.25%) had fibroadenoma, three (18.75%) had both fibroadenoma and phyllodes tumor, and one (6.25%) had adenolipoma. The average size of tumors was 7.5 cm in our study. During postsurgical follow-up, none of the patients had nipple areola necrosis, and they reported that nipple sensation was acceptable. A mastectomy was avoided in all circumstances. Good cosmetic outcomes and clear margin status are achievable using the round block technique.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Atherogenic indices among women with benign and malignant breast lesions in Ile-Ife, Nigeria

Atherogenic indices among women with benign and malignant breast lesions in Ile-Ife, Nigeria

The therapeutic approach to fibrocystic breast disease in the MCF-10A cell culture model: Striking efficacy of polyphenols

The therapeutic approach to fibrocystic breast disease in the MCF-10A cell culture model: Striking efficacy of polyphenols