Faculty Bibliography

BACKGROUND:A positive lumpectomy margin after breast-conserving surgery (BCS) is a significant predictor for ipsilateral cancer recurrence. The MarginProbe, a Food and Drug Administration (FDA)-approved device for intraoperative assessment of lumpectomy margins, is associated with a reduction in re-excision surgery. This study aimed to evaluate the relationship of mammographic breast density (MBD) and clinicopathologic characteristics with margin status in women undergoing BCS with the MarginProbe. METHODS:The institutional database was queried for patients with breast cancer who had BCS with the MarginProbe from 2013 to 2017. Clinicopathologic characteristics were collected. The study defined MBD as less dense (Breast Imaging Reporting and Data System [BI-RADS] A and B) and more dense (BI-RADS C and D). A positive margin was defined as smaller than 1 mm. Pearson Chi square and uni- and multivariate logistic regression were performed. RESULTS:Of 1734 patients, 341 met the study criteria. The median patient age was 63 years. The patients with higher mammographic density were younger (p < 0.0001) and had a lower body mass index (BMI) (p < 0.0001). The patients with higher MBD were more likely to present with a palpable mass (p = 0.0360). Of the 341 patients, 135 (39.6%) had one or more positive margins on the main specimen, and 101 (74.8%) were converted to final negative margins after the MarginProbe directed re-excisions. Positive final margins were associated with larger tumor size (p = 0.0242) and more advanced stage of disease at diagnosis (p = 0.0255). CONCLUSIONS:In this study of patients undergoing BCS, breast density was not correlated with the likelihood of a positive margin. The presence of positive final lumpectomy margins was associated with older age and more extensive disease.

PURPOSE/OBJECTIVE:Genomic assays, or tissue gene profiling tests, are widely used to predict recurrence of early stage invasive breast cancer and guide systemic therapy. The purpose of our study was to examine the current national trends of genomic testing in male breast cancer (MBC) and its association with systemic therapy. MATERIALS AND METHODS/METHODS:The National Cancer Database (NCDB) includes 6227 cases of pathologic T1/T2 and N0/N1 MBC from 2008 to 2014 with known genomic testing status. Results of the tests were grouped as low, intermediate, and high risk of recurrence scores (RRS). Statistical analysis included multivariate logistic regression and Chi-square tests. A supplemental analysis in female breast cancer was provided as reference. RESULTS:Of the 6227 cases of MBC age 18-90, 1478 (23.74%) underwent genomic testing. Testing was significantly associated with age, race, tumor grade, year of diagnosis, receptor status, and nodal status. Distribution of RRS in MBC was 59.3% low, 27.4% intermediate, and 13.3% high. RRS in men were significantly associated with tumor grade and size, but not nodal status. Those with a low RRS were 7-times more likely to be treated with hormone therapy alone (AOR 7.18, CI 5.78-8.91, P < 0.001). Those with a high RRS were five times more likely to receive a combination of hormone and chemotherapy (AOR 5.16, CI 3.60-7.40, P < 0.001). CONCLUSION/CONCLUSIONS:Rates of testing and distribution of RRS in men and women with early stage invasive breast cancer are similar. Treatment patterns in MBC varied significantly based on genomic testing results, even when adjusted for other clinicopathologic features. Further investigation is required to determine the prognostic and predictive nature of genomic testing in male breast cancer.

BACKGROUND:The treatment of ductal carcinoma in situ (DCIS) remains controversial and may be particularly difficult for patients with minimal disease. There is a dearth of information regarding patients who have been diagnosed with DCIS on core needle biopsy (CNB), who have no residual disease in the lumpectomy specimen. The purpose of this study was to explore the frequency of this presentation and short-term outcomes in these patients. METHODS:Our institutional Breast Cancer Database was queried for all women who were diagnosed with pure DCIS from 2010 to 2016 and treated with lumpectomy. Variables included patient and tumor characteristics, adjuvant treatment, and ipsilateral breast tumor recurrence (IBTR). Statistical analyses included Pearson's chi-square, Fisher's exact tests, and Kaplan-Meier analysis. RESULTS:Of 547 patients with pure DCIS, 50 (14%) had DCIS on CNB only. Of the patients with DCIS on CNB only, 15 were treated with lumpectomy and radiation therapy (RT), while 35 underwent lumpectomy without RT. At a median follow-up of 4 years, there were 3 (6%) IBTR all within the same quadrant as the original lumpectomy site. None of the patients who recurred received adjuvant RT or hormonal therapy. CONCLUSIONS:Despite the minimal extent of disease exhibited in these cases, 6% of patients with DCIS on CNB only had IBTR at a median follow-up of 4 years. These data suggest that even minimal DCIS represents a significant risk of recurrence to the patient. Size and margins are not sufficient criteria to stratify risk and guide decisions for adjuvant therapies.

BACKGROUND:Recent data support the use of post-mastectomy radiation therapy (PMRT) in women with one to three positive lymph nodes; however, the benefit of PMRT in patients with micrometastatic nodal disease (N1mi) is unknown. We evaluated the survival impact of PMRT in patients with N1mi within the National Cancer Database. METHODS:The pattern of care and survival benefit of PMRT was examined in women with pT1-2N1mi breast cancer who underwent mastectomy without neoadjuvant chemotherapy. Univariable and multivariable Cox proportional hazard models were employed for survival analysis, and subanalyses of high-risk patients and a propensity score-matched (PSM) cohort were completed. RESULTS:From 2004 to 2014, we identified 14,019 patients who fitted the study criteria. PMRT was delivered in 18.5% of patients and its use increased over the study period. Patients treated with PMRT were younger, had better performance status and larger primaries, were estrogen receptor (ER)-negative, had higher grade, lymphovascular invasion and positive surgical margins, and more often received systemic therapy. PMRT was significantly associated with overall survival (OS) in univariable analysis (hazard ratio [HR] 0.75 [0.64-0.89]), but was not significant in multivariable analysis (adjusted HR 1.01 [0.84-1.20]). There was no survival benefit to PMRT in ER-negative, high-grade, and/or young patients. There were 2 (0.9%) death events in the sentinel lymph node biopsy (SLNB) + PMRT group versus 21 (2.9%) in the SLNB-alone group (log-rank p = 0.053), and 8 (3.9%) death events in the axillary lymph node biopsy (ALNB) + PMRT group versus 27 (3.6%) in the axillary lymph node dissection-alone group (p = 0.82). There was no significant association between PMRT and OS within the PSM subgroup. CONCLUSION/CONCLUSIONS:In this largest reported retrospective study, no OS differences were associated with PMRT, which suggests that PMRT may not benefit every patient with microscopic nodal disease.

Background/UNASSIGNED:In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. Methods/UNASSIGNED:A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. Results/UNASSIGNED:Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. Conclusions/UNASSIGNED:A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.