Gauging the efficacy of neoadjuvant endocrine therapy in breast cancer patients with known axillary disease.

BACKGROUND AND OBJECTIVES: Neoadjuvant endocrine therapy (NET) for ER+ breast cancer can downstage primary tumors. We evaluated NET efficacy in node-positive patients. METHODS: Node-positive patients undergoing NET for ER+ breast cancer from 2012 to 2019 were reviewed. Primary endpoints included rates of axillary lymphadenectomy (ALND), pathologic complete response (pCR), and final nodal staging. RESULTS: Thirty-nine patients were included. Before NET, all were clinically node-positive (cN1 = 36, 94%; cN2 = 2, 5%; cN3 = 1, 3%; Stage II = 23, 59%, Stage III = 16, 41%). After NET, nine (23%) had clinically persistent axillary disease necessitating ALND. The remaining 30 (77%) underwent sentinel lymph node biopsy (SLNB). Of these, 25 (83%) were SLNB+ on frozen section, undergoing immediate ALND. Five patients were negative on frozen section: one had a confirmed axillary pCR, and four had residual nodal disease on permanent pathology. One underwent delayed ALND, and for the remaining three patients, decision was made to forgo ALND. Final overall axillary staging was: N0 (pCR) = 1, 3%, pN1mic = 1, 3%, pN1 = 20, 51%, pN2 = 12, 30%, pN3 = 5, 13%; Stage II = 16, 41%, Stage III = 23, 59%. CONCLUSIONS: While NET is reported to downstage primary tumors, downstaging of the axilla was unsuccessful in the majority of patients.

Direct care TeleDiabetes practice in the Phoenix VA healthcare system: Innovation report.

The Phoenix Veterans Affairs Healthcare System implemented a TeleDiabetes program in 2018 to provide multidisciplinary care to rural Veterans with type 2 diabetes. Here, we introduce the TeleDiabetes program as a novel telemedicine model with integrated remote physical exam, and we share results demonstrating Veteran satisfaction with the program.

Characterizing Occult Nodal Disease Within a Clinically Node-Negative, Neoadjuvant Breast Cancer Population.

BACKGROUND: Neoadjuvant therapy aims to preoperatively downstage breast cancer patients. We evaluated nodal upstaging in clinically node-negative (cN0) patients receiving neoadjuvant chemotherapy (NAC) and neoadjuvant endocrine therapy (NET). METHODS: cN0 patients undergoing neoadjuvant therapy from 2009 to 2018 were reviewed. Univariate and multivariate analyses evaluated rates of nodal upstaging. RESULTS: A total of 228 cN0 patients with a mean age of 55 years underwent neoadjuvant therapy for Stage I-III invasive carcinoma. Subtypes included ER+/HER2- = 93 (40%), HER2+ = 61 (27%), and triple negative (TNBC) = 74 (33%). Among ER+/HER2- patients, 65 (70%) underwent NET. Overall, 49 patients (21%) were upstaged due to occult nodal disease. Factors associated with higher rates of occult nodal disease included advanced stage on initial presentation (P = .008), larger presenting tumor size (P = .009), low/intermediate tumor grade (P = .025), and ER+/HER2- subtype (P < .001); incidence of occult nodal disease by subtype included: ER+/HER2- = 37%, HER2+ = 15%, TNBC = 8%. Patients experiencing a breast pCR had a significantly lower rate of nodal upstaging compared to those with residual tumor (4% vs. 96%, P < .001). On multivariate analysis, ER+/HER- patients exhibited higher risk of occult nodal disease when compared to patients with HER2+ (odds ratio [OR] = 3.4, 95% CI, 1.2-9.8, P = .003) and TNBC (OR = 5.7, 95% CI, 1.7-19.6, P = .003). Comparing NAC vs. NET in ER+/HER2- patients showed no difference in rates of occult nodal disease (39% vs. 35%, P = .13). CONCLUSIONS: ER+/HER2- subtype carries higher risk for occult nodal disease after neoadjuvant therapy; NAC versus NET in these patients does not affect nodal upstaging.

Machine learning of biomarkers and clinical observation to predict eosinophilic chronic rhinosinusitis: a pilot study.

BACKGROUND: Subtyping chronic rhinosinusitis (CRS) by tissue eosinophilia has prognostic and therapeutic implications, and is difficult to predict using peripheral eosinophil counts or polyp status alone. The objective of this study was to test machine learning for prediction of eosinophilic CRS (eCRS). METHODS: Input variables were defined as peripheral eosinophil count, urinary leukotriene E4 (uLTE4) level, and polyp status. The output was diagnosis of eCRS, defined as tissue eosinophil count >10 per high-power field. Patients undergoing surgery for CRS were retrospectively reviewed for complete datasets. Univariate analysis was performed for each input as a predictor of eCRS. Logistic regression and artificial neural network (ANN) machine learning models were developed using random and surgeon-specific training/test datasets. RESULTS: A total of 80 patients met inclusion criteria. In univariate analysis, area under the receiver operator characteristic curve (AUC) for peripheral eosinophil count and uLTE4 were 0.738 (95% confidence interval [CI], 0.616 to 0.840) and 0.728 (95% CI, 0.605 to 0.822), respectively. Presence of polyps was 94.1% sensitive, but 51.7% specific. Logistic regression models using random and surgeon specific datasets resulted in AUC of 0.882 (95% CI, 0.665 to 0.970) and 0.945 (95% CI, 0.755 to 0.995), respectively. ANN models resulted in AUC of 0.918 (95% CI, 0.756 to 0.975) and 0.956 (95% CI, 0.828 to 0.999) using random and surgeon-specific datasets, respectively. Model comparison of logistic regression and ANN was not statistically different. All machine learning models had AUC greater than univariate analyses (all p < 0.003). CONCLUSION: Machine learning of 3 clinical inputs has the potential to predict eCRS with high sensitivity and specificity in this patient population. Prospective investigation using larger and more diverse populations is warranted.