Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Significance: During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim: In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach: We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results: We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions: DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging.

BACKGROUND: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Decorin (DCN) Downregulation Activates Breast Stromal Fibroblasts and Promotes Their Pro-Carcinogenic Effects through the IL-6/STAT3/AUF1 Signaling.

Decorin (DCN), a member of the small leucine-rich proteoglycan gene family, is secreted from stromal fibroblasts with non-cell-autonomous anti-breast-cancer effects. Therefore, in the present study, we sought to elucidate the function of decorin in breast stromal fibroblasts (BSFs). We first showed DCN downregulation in active cancer-associated fibroblasts (CAFs) compared to their adjacent tumor counterpart fibroblasts at both the mRNA and protein levels. Interestingly, breast cancer cells and the recombinant IL-6 protein, both known to activate fibroblasts in vitro, downregulated DCN in BSFs. Moreover, specific DCN knockdown in breast fibroblasts modulated the expression/secretion of several CAF biomarkers and cancer-promoting proteins (α-SMA, FAP- α, SDF-1 and IL-6) and enhanced the invasion/proliferation abilities of these cells through activation of the STAT3/AUF1 signaling. Furthermore, DCN-deficient fibroblasts promoted the epithelial-to-mesenchymal transition and stemness processes in BC cells in a paracrine manner, which increased their resistance to cisplatin. These DCN-deficient fibroblasts also enhanced angiogenesis and orthotopic tumor growth in mice in a paracrine manner. On the other hand, ectopic expression of DCN in CAFs suppressed their active features and their paracrine pro-carcinogenic effects. Together, the present findings indicate that endogenous DCN suppresses the pro-carcinogenic and pro-metastatic effects of breast stromal fibroblasts.

Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments.

This review provides unique insights to the scientific scope and clinical visions of the inventors and pioneers of the SoftVue breast tomographic ultrasound (BTUS). Their >20-year collaboration produced extensive basic research and technology developments, culminating in SoftVue, which recently received the Food and Drug Administration's approval as an adjunct to breast cancer screening in women with dense breasts. SoftVue's multi-center trial confirmed the diagnostic goals of the tissue characterization and localization of quantitative acoustic tissue differences in 2D and 3D coronal image sequences. SoftVue mass characterizations are also reviewed within the standard cancer risk categories of the Breast Imaging Reporting and Data System. As a quantitative diagnostic modality, SoftVue can also function as a cost-effective platform for artificial intelligence-assisted breast cancer identification. Finally, SoftVue's quantitative acoustic maps facilitate noninvasive temperature monitoring and a unique form of time-reversed, focused US in a single theranostic device that actually focuses acoustic energy better within the highly scattering breast tissues, allowing for localized hyperthermia, drug delivery, and/or ablation. Women also prefer the comfort of SoftVue over mammograms and will continue to seek out less-invasive breast care, from diagnosis to treatment.

Assessing breast arterial calcification in mammograms and its implications for atherosclerotic cardiovascular disease risk.

PURPOSE: Breast arterial calcifications (BAC) are incidentally observed on mammograms, yet their implications remain unclear. We investigated lifestyle, reproductive, and cardiovascular determinants of BAC in women undergoing mammography screening. Further, we investigated the relationship between BAC, coronary arterial calcifications (CAC) and estimated 10-year atherosclerotic cardiovascular (ASCVD) risk. METHODS: In this cross-sectional study, we obtained reproductive history and CVD risk factors from 215 women aged 18 or older who underwent mammography and cardiac computed tomographic angiography (CCTA) within a 2-year period between 2007 and 2017 at hospital. BAC was categorized as binary (present/absent) and semi-quantitatively (mild, moderate, severe). CAC was determined using the Agatston method and recorded as binary (present/absent). Adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated, accounting for age as a confounding factor. ASCVD risk over a 10-year period was calculated using the Pooled Cohort Risk Equations. RESULTS: Older age, systolic and diastolic blood pressures, higher parity, and younger age at first birth (≤28 years) were significantly associated with greater odds of BAC. Women with both BAC and CAC had the highest estimated 10-year risk of ASCVD (13.30 %). Those with only BAC (8.80 %), only CAC (5.80 %), and no BAC or CAC (4.40 %) had lower estimated 10-year risks of ASCVD. No association was detected between presence of BAC and CAC. CONCLUSIONS: These findings support the hypothesis that BAC on a screening mammogram may help to identify women at potentially increased risk of future cardiovascular disease without additional cost and radiation exposure.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34.

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Activation of the YY1-UGT2B7 Axis Promotes Mammary Estrogen Homeostasis Dysregulation and Exacerbates Breast Tumor Metastasis.

Metastasis is the most common pathway of cancer death. The lack of effective predictors of breast cancer metastasis is a pressing issue in clinical practice. Therefore, exploring the mechanism of breast cancer metastasis to uncover reliable predictors is very important for the clinical treatment of breast cancer patients. In this study, tandem mass tag quantitative proteomics technology was used to detect protein content in primary breast tumor tissue samples from patients with metastatic and nonmetastatic breast cancer at diagnosis. We found that the high expression of yin-yang 1(YY1) is strongly associated with poor prognosis in high-grade breast cancer. YY1 expression was detected in both clinical tumor tissue samples and tumor tissue samples from mammary-specific polyomavirus middle T antigen overexpression mouse model mice. We demonstrated that upregulation of YY1 expression was closely associated with breast cancer metastasis and that high YY1 expression could promote the migratory invasive ability of breast cancer cells. Mechanistically, YY1 directly binds to the UGT2B7 mRNA initiation sequence ATTCAT, thereby transcriptionally regulating the inhibition of UGT2B7 expression. UGT2B7 can regulate the development of breast cancer by regulating estrogen homeostasis in the breast, and the abnormal accumulation of estrogen, especially 4-OHE2, promotes the migration and invasion of breast cancer cells, ultimately causing the development of breast cancer metastasis. In conclusion, YY1 can regulate the UGT2B7-estrogen metabolic axis and induce disturbances in estrogen metabolism in breast tumors, ultimately leading to breast cancer metastasis. Disturbances in estrogen metabolism in the breast tissue may be an important risk factor for breast tumor progression and metastasis SIGNIFICANCE STATEMENT: In this study, we propose for the first time a regulatory relationship between YY1 and the UGT2B7/estrogen metabolism axis and explore the molecular mechanism. Our study shows that the YY1/UGT2B7/estrogen axis plays an important role in the development and metastasis of breast cancer. This study further elucidates the potential mechanisms of YY1-mediated breast cancer metastasis and the possibility and promise of YY1 as a predictor of cancer metastasis.

The Impact of Health Inequities on Population-Based Breast Cancer Survival in a Colombian Population, 2008-2015.

OBJECTIVE: To obtain breast cancer survival estimates in Manizales, Colombia, considering socioeconomic level, health insurance regime and residential area, while adjusting for age, histology and stage at diagnosis. METHODS: Analytical cohort study based on breast cancer incident cases recorded by the Population-based Manizales Cancer Registry between 2008-2015. Patients were followed-up for 60 months. Cause-specific survival was calculated using the Kaplan-Meier method for variables of interest, with the Wilcoxon-Breslow-Gehan test for differences. Cox multivariate regression models were fitted. RESULTS: 856 breast cancer cases were included. The 5-year cause-specific survival for the entire cohort was 78.2%. It was higher in women with special/exception health insurance, high socioeconomic level, <50 years old, ductal carcinoma, and stages I and II. Residential area did not impact survival. In Cox models, the subsidized health insurance regime (HR: 4.87 vs contributory) and low socioeconomic level (HR: 2.45 vs high) were predictors of the hazard of death in women with breast cancer, adjusted for age, histology, stage and interactions age-stage and insurance-stage. A positive interaction (synergistic effect modification) between health insurance regime and stage regarding to survival was observed. CONCLUSION: Socioeconomic factors significantly contribute to the inequities in breast cancer survival, independent of the stage at diagnosis. This suggests the need for comprehensive interventions to remove barriers to accessing the health system. This research provides evidence of survival gaps mediated by certain social determinants of health and generates data on the overall performance of the Colombian health system.

Breast cysticercosis - a case report.

SUMMARY: Isolated incidences of human cysticercosis have been reported world-wide, but it remains a major public health concern in endemic areas such as Mexico, Africa, South-East Asia, Eastern Europe, and South America. Cysticercosis most commonly involves the skeletal muscle, subcutaneous tissue, brain, and eyes. The breast is an uncommon site of presentation for cysticercosis. Due to its rare occurrence, breast cysticercosis is often initially mistaken for other common breast lesions such as cysts, abscess, malignant tumours and fibroadenomas. We report a case of breast cysticercosis in a young South African woman.

Gestational gigantomastia complicated by pseudo-angiomatous stromal hyperplasia - a multidisciplinary management approach.

SUMMARY: Gestational gigantomastia is a rare condition typified by disproportionate bilateral breast enlargement in pregnant women, resulting in skin thinning, ulceration, and bleeding. Less than sixty cases have been documented worldwide, and only one other in South Africa. Pseudo-angiomatous stromal hyperplasia (PASH) is a rare benign proliferation of stromal tissue in a tumorous or diffuse pattern. This, to the best of our knowledge, is the first published case, a 27-year-old human immunodeficiency virus (HIV) positive woman, to present with both conditions concurrently. Medical management with cabergoline was initiated and, seven months post-delivery, a novel Goldilocks mastectomy was performed with acceptable outcomes.

Case report: Primary vulvar adenocarcinoma of mammary gland type-its genetic characteristics by focused next-generation sequencing.

Mammary-like vulvar adenocarcinoma (MLVA) is an exceedingly rare subtype of vulvar adenocarcinoma that shares features with mammary gland tissue. Due to its rarity and lack of consensus, MLVA presents diagnostic challenges to pathologists. We present the case of a 59-year-old female with an ulcerated mass on the right side of the external genitalia, diagnosed as MLVA. Comprehensive immunohistochemistry (IHC) and gene sequencing studies were performed to characterize the tumor. IHC analysis revealed triple expression of hormonal receptors (estrogen receptor, progesterone receptor, and HER2), supporting the mammary gland origin of the tumor. Gene sequencing identified unique genetic mutations associated with the expression of hormonal markers. One fusion gene (ERBB2-NAGLU) has not been reported in any tumors, and other mutations with unique mutation types have not been previously reported in MLVA. Our findings shed light on the molecular characteristics of MLV and may help improve the diagnosis and treatment of this rare type of vulvar adenocarcinoma.

[Non-primary solid malignancies of breast in needle core biopsy: a clinicopathological analysis of 23 cases].

Objective: To investigate the accurate diagnosis and differential diagnosis of non-primary solid malignant tumors in breast needle core biopsy. Methods: Twenty-three cases of breast, axilla or neck lymph nodes pathologically diagnosed as non-primary solid malignant tumors were collected at the First Affiliated Hospital of Nanjing Medical University, Nanjing, China from January 2013 to March 2023. The differential diagnoses and diagnostic features were analyzed, based on combining clinical data, histology, and expression characteristics of biomarkers. Results: All patients were female, with age ranging from 29 to 75 years (average 56 years). The average time from the diagnosis of primary tumor to the current diagnosis was 21 months (0 to 204 months).The primary sites included the ovary (9 cases), the lung (5 cases), the gastrointestinal tract (4 cases), the pancreas, intrahepatic bile duct, thyroid gland, nasal cavity and forearm skin (1 case each). No carcinoma in situ was found in any of the cases. The morphological differences were significant among the tumors, but similar to the primary tumors. The tumors of neuroendocrine and female reproductive tract had great morphological and immunophenotypic overlaps with breast cancer. Metastatic lung cancer cells showed obvious atypia and tumor giant cells. The morphology and immunophenotype of metastatic serous carcinoma of female reproductive system might resemble invasive micropapillary carcinoma of the breast. Metastatic adenocarcinoma of the gastrointestinal tract often had features of mucous secretion. Metastatic neuroendocrine tumors were bland in appearance and morphologically similar to solid papillary carcinoma of breast, but negative for ER. TRPS1 was mostly negative (18/23) and variably positive in ovarian (4/9) and intrahepatic bile duct (1/1) tumors. Conclusions: The diagnosis of breast needle core biopsy specimen should be combined with clinical history, imaging study, and careful examination of histological features, such as presence of in situ component, morphological similarity between the primary and metastatic tumors, and using appropriate markers to differentiate the primary from metastatic tumors.

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era.

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

The Vitamin D Receptor as a Prognostic Marker in Breast Cancer-A Cohort Study.

Previous research has indicated an association between the presence of the vitamin D receptor (VDR) in breast cancer tissue and a favorable prognosis. This study aimed to further evaluate the prognostic potential of VDR located in the nuclear membrane or nucleus (liganded). The VDR protein levels were analyzed using immunohistochemistry in tumor samples from 878 breast cancer patients from Lund, Sweden, included in the Breast Cancer and Blood Study (BCBlood) from October 2002 to June 2012. The follow-up for breast cancer events and overall survival was recorded until 30 June 2019. Univariable and multivariable survival analyses were conducted, both with complete case data and with missing data imputed using multiple imputation by chained equations (MICE). Tumor-specific positive nuclear membrane VDR(num) staining was associated with favorable tumor characteristics and a longer breast cancer free interval (BCFI; HR: 0.64; 95% CI: 0.44-0.95) and overall survival (OS; HR: 0.52; 95% CI: 0.34-0.78). Further analyses indicated that VDRnum status also was predictive of overall survival when investigated in relation to ER status. There were significant interactions between VDR and invasive tumor size (Pinteraction = 0.047), as well as mode of detection (Pinteraction = 0.049). VDRnum was associated with a longer BCFI in patients with larger tumors (HR: 0.36; 95% CI: 0.14-0.93) or clinically detected tumors (HR: 0.28; 95% CI: 0.09-0.83), while no association was found for smaller tumors and screening-detected tumors. Further studies are suggested to confirm our results and to evaluate whether VDR should and could be used as a prognostic and targetable marker in breast cancer diagnostics.

Serum Levels of Copper and Zinc and Survival in Breast Cancer Patients.

There is emerging interest in the relationship between several serum micronutrients and the prognosis of patients with breast cancer. The relationship between serum zinc and copper levels and breast cancer prognosis is unclear. In our study, we included 583 patients with breast cancer diagnosed between 2008 and 2015 in the region of Szczecin, Poland. In a blood sample obtained before treatment, serum zinc and copper levels were quantified by mass spectroscopy. Each patient was assigned to one of four categories (quartiles) based on the distribution of the elements in the entire cohort. Patients were followed from diagnosis to death over a mean of 10.0 years. The 10-year overall survival was 58.3% for women in the highest and 82.1% for those in the lowest quartile of serum copper/zinc ratio (p < 0.001). The multivariate hazard ratio (HR) for breast cancer death was 2.07 (95% CI 1.17-3.63; p = 0.01) for patients in the highest quartile of serum copper/zinc ratio compared to those in the lowest. There is evidence that the serum zinc level and copper/zinc ratio provide an independent predictive value for overall survival and breast cancer-specific survival after breast cancer diagnosis.