For many years, patient age, axillary lymph node status, tumor size, histological features (especially histological grade and lymphovascular invasion), hormone receptor status, and HER2 status have ...been the major factors used to categorize patients with breast cancer in order to assess prognosis and determine the appropriate therapy. These factors are most often viewed in combination to group patients into various risk categories. Although these risk categories are useful for assessing prognosis and risk in groups of patients with breast cancer, their role in determining prognosis and evaluating risk in an individual patient is more limited. Therefore, better methods are required to help assess prognosis and determine the most appropriate treatment for patients on an individual basis. Recently, various molecular techniques, particular gene expression profiling, have been increasingly used to help refine breast cancer classification and to assess prognosis and response to therapy. Although the precise role of these newer techniques in the daily management of patients with breast cancer continues to evolve, it is clear that they have the potential to provide value above and beyond that provided by the traditional clinical and pathological prognostic and predictive factors.
Concerns about overdiagnosis and overtreatment have led to interest in de-escalating treatment for ductal carcinoma in situ (DCIS). This article reviews the epidemiology, natural history, and current ...treatment options for DCIS and discusses ongoing efforts to further de-escalate treatment for these patients.
To convene a multidisciplinary panel of breast experts to examine the relationship between margin width and ipsilateral breast tumor recurrence (IBTR) and develop a guideline for defining adequate ...margins in the setting of breast conserving surgery and adjuvant radiation therapy.
A multidisciplinary consensus panel used a meta-analysis of margin width and IBTR from a systematic review of 33 studies including 28,162 patients as the primary evidence base for consensus.
Positive margins (ink on invasive carcinoma or ductal carcinoma in situ) are associated with a 2-fold increase in the risk of IBTR compared with negative margins. This increased risk is not mitigated by favorable biology, endocrine therapy, or a radiation boost. More widely clear margins than no ink on tumor do not significantly decrease the rate of IBTR compared with no ink on tumor. There is no evidence that more widely clear margins reduce IBTR for young patients or for those with unfavorable biology, lobular cancers, or cancers with an extensive intraductal component.
The use of no ink on tumor as the standard for an adequate margin in invasive cancer in the era of multidisciplinary therapy is associated with low rates of IBTR and has the potential to decrease re-excision rates, improve cosmetic outcomes, and decrease health care costs.
High-risk breast lesions, which comprise benign lesions and in situ carcinomas (lobular carcinoma in situ and ductal carcinoma in situ), are clinically, morphologically, and biologically ...heterogeneous and are associated with an increased risk of invasive breast cancer development, albeit to varying degrees. Recognition and proactive management of such lesions can help to prevent progression to invasive disease, and might, therefore, reduce breast cancer incidence, morbidity, and mortality. However, this opportunity comes with the possibility of overdiagnosis and overtreatment, necessitating risk-based intervention. Notably, despite the progress in defining the molecular changes associated with carcinogenesis, alterations identifying the individuals with high-risk lesions that will progress to invasive carcinoma remain to be identified. Thus, until reproducible clinicopathological or molecular features predicting an individual's risk of breast cancer are found, management strategies must be defined by population-level risks as determined by models such as the Gail or IBIS models, as well as patient attitudes toward the risks and benefits of interventions. Herein, we review the contemporary approaches to diagnosis and management of high-risk breast lesions. Progress in this area will ultimately be dependent on the ability to individualize risk prediction through better definition of the key drivers in the carcinogenic process.
Image-directed core needle biopsies of the breast are routinely used in current clinical practice for the initial assessment of non-palpable breast lesions. This article provides an update on several ...important issues regarding evaluation of breast core needle biopsies.
The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced ...pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.
A breast pathology diagnosis provides the basis for clinical treatment and management decisions; however, its accuracy is inadequately understood.
To quantify the magnitude of diagnostic disagreement ...among pathologists compared with a consensus panel reference diagnosis and to evaluate associated patient and pathologist characteristics.
Study of pathologists who interpret breast biopsies in clinical practices in 8 US states.
Participants independently interpreted slides between November 2011 and May 2014 from test sets of 60 breast biopsies (240 total cases, 1 slide per case), including 23 cases of invasive breast cancer, 73 ductal carcinoma in situ (DCIS), 72 with atypical hyperplasia (atypia), and 72 benign cases without atypia. Participants were blinded to the interpretations of other study pathologists and consensus panel members. Among the 3 consensus panel members, unanimous agreement of their independent diagnoses was 75%, and concordance with the consensus-derived reference diagnoses was 90.3%.
The proportions of diagnoses overinterpreted and underinterpreted relative to the consensus-derived reference diagnoses were assessed.
Sixty-five percent of invited, responding pathologists were eligible and consented to participate. Of these, 91% (N = 115) completed the study, providing 6900 individual case diagnoses. Compared with the consensus-derived reference diagnosis, the overall concordance rate of diagnostic interpretations of participating pathologists was 75.3% (95% CI, 73.4%-77.0%; 5194 of 6900 interpretations). Among invasive carcinoma cases (663 interpretations), 96% (95% CI, 94%-97%) were concordant, and 4% (95% CI, 3%-6%) were underinterpreted; among DCIS cases (2097 interpretations), 84% (95% CI, 82%-86%) were concordant, 3% (95% CI, 2%-4%) were overinterpreted, and 13% (95% CI, 12%-15%) were underinterpreted; among atypia cases (2070 interpretations), 48% (95% CI, 44%-52%) were concordant, 17% (95% CI, 15%-21%) were overinterpreted, and 35% (95% CI, 31%-39%) were underinterpreted; and among benign cases without atypia (2070 interpretations), 87% (95% CI, 85%-89%) were concordant and 13% (95% CI, 11%-15%) were overinterpreted. Disagreement with the reference diagnosis was statistically significantly higher among biopsies from women with higher (n = 122) vs lower (n = 118) breast density on prior mammograms (overall concordance rate, 73% 95% CI, 71%-75% for higher vs 77% 95% CI, 75%-80% for lower, P < .001), and among pathologists who interpreted lower weekly case volumes (P < .001) or worked in smaller practices (P = .034) or nonacademic settings (P = .007).
In this study of pathologists, in which diagnostic interpretation was based on a single breast biopsy slide, overall agreement between the individual pathologists' interpretations and the expert consensus-derived reference diagnoses was 75.3%, with the highest level of concordance for invasive carcinoma and lower levels of concordance for DCIS and atypia. Further research is needed to understand the relationship of these findings with patient management.
Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on ...the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient decision making in which potentially suitable treatment options are increasingly available. One of the best-established prognostic factors in breast cancer is histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. Genome-wide microarray-based expression profiling studies have unraveled several characteristics of breast cancer biology and have provided further evidence that the biological features captured by histological grade are important in determining tumor behavior. Also, expression profiling studies have generated clinically useful data that have significantly improved our understanding of the biology of breast cancer, and these studies are undergoing evaluation as improved prognostic and predictive tools in clinical practice. Clinical acceptance of these molecular assays will require them to be more than expensive surrogates of established traditional factors such as histological grade. It is essential that they provide additional prognostic or predictive information above and beyond that offered by current parameters. Here, we present an analysis of the validity of histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers.
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