Yingjian He

Early prediction of pathological outcomes to neoadjuvant chemotherapy in breast cancer patients using automated breast ultrasound.

Objective Early assessment of response to neoadjuvant chemotherapy (NAC) for breast cancer allows therapy to be individualized. The optimal assessment method has not been established. We investigated the accuracy of automated breast ultrasound (ABUS) to predict pathological outcomes after NAC. Methods A total of 290 breast cancer patients were eligible for this study. Tumor response after 2 cycles of chemotherapy was assessed using the product change of two largest perpendicular diameters (PC) or the longest diameter change (LDC). PC and LDC were analyzed on the axial and the coronal planes respectively. Receiver operating characteristic (ROC) curves were used to evaluate overall performance of the prediction methods. Youdens indexes were calculated to select the optimal cut-off value for each method. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC) were calculated accordingly. Results ypT0/is was achieved in 42 patients (14.5%) while ypT0 was achieved in 30 patients (10.3%) after NAC. All four prediction methods (PC on axial planes, LDC on axial planes, PC on coronal planes and LDC on coronal planes) displayed high AUCs (all>0.82), with the highest of 0.89 [95% confidence interval (95% CI), 0.83-0.95] when mid-treatment ABUS was used to predict final pathological complete remission (pCR). High sensitivities (85.7%-88.1%) were observed across all four prediction methods while high specificities (81.5%-85.1%) were observed in two methods used PC. The optimal cut-off values defined by our data replicate the WHO and the RECIST criteria. Lower AUCs were observed when mid-treatment ABUS was used to predict poor pathological outcomes. Conclusions ABUS is a useful tool in early evaluation of pCR after NAC while less reliable when predicting poor pathological outcomes.

Value of pre-treatment biomarkers in prediction of response to neoadjuvant endocrine therapy for hormone receptor-positive postmenopausal breast cancer

OBJECTIVE To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy (NET) in postmenopausal breast cancer. METHODS Consecutive 160 postmenopausal women with T1-3N0-1M0 hormone receptor (HR)-positive invasive breast cancer were treated with anastrozole for 16 weeks before surgery. New slides of tumor specimens taken before and after treatment were conducted centrally for biomarker analysis and classified using the Applied Imaging Ariol MB-8 system. The pathological response was evaluated using the Miller & Payne classification. The cell cycle response was classified according to the change in the Ki67 index after treatment. Multivariable logistic regression analysis was used to calculate the combined index of the biomarkers. Receiver operating characteristic (ROC) curves were used to determine whether parameters may predict response. RESULTS The correlation between the pathological and cell cycle responses was low (Spearman correlation coefficient =0.241, P<0.001; Kappa value =0.119, P=0.032). The cell cycle response was significantly associated with pre-treatment estrogen receptor (ER) status (P=0.001), progesterone receptor (PgR) status (P<0.001), human epidermal growth factor receptor 2 (Her-2) status (P=0.050) and the Ki67 index (P<0.001), but the pathological response was not correlated with these factors. Pre-treatment ER levels [area under the curve (AUC) =0.634, 95% confidence interval (95% CI), 0.534-0.735, P=0.008] and combined index of pre-treatment ER and PgR levels (AUC =0.684, 95% CI, 0.591-0.776, P<0.001) could not predict the cell cycle response, but combined index including per-treatment ER/PR/Her-2/Ki67 expression levels could (AUC =0.830, 95% CI, 0.759-0.902, P<0.001). CONCLUSIONS The combined use of pre-treatment ER/PgR/Her-2/Ki67 expression levels, instead of HR expression levels, may predict the cell cycle response to NET.

Is Surgical Excision Necessary in Breast Papillomas 10 mm or Smaller at Core Biopsy

Background: The objective of this study was to propose managements for breast papillomas of 10 mm or smaller initially diagnosed at core biopsy. Method: We reviewed the data of patients in our center from 2004 to 2013. 116 lesions of 10 mm or smaller as measured by ultrasound (US) were diagnosed as papillomas at core needle biopsy (CNB) or vacuum-assisted biopsy (VAB). 74 of the papillomas diagnosed by CNB were surgically excised, the others were followed by imaging surveillance. Result: 13 of 116 lesions were found to be malignant, with an upgrade rate of 11.2%. Analyzing the difference between malignant and nonmalignant lesions, patients with malignant lesions were older than those with nonmalignant lesions (56.6 vs. 46.6 years, p = 0.002). Papillomas with atypia had a significantly higher upgrade rate than without, both in the surgical results (p = 0.030) and overall (p = 0.0392). None of 16 papillomas larger than 5 mm upgraded to malignancy. Breast papillomas diagnosed by CNB had a significantly higher upgrade rate (16.5%) than those diagnosed by VAB (0%) (p = 0.021). Conclusion: Our finding suggests that breast papillomas of 6-10 mm at initial CNB need additional surgical excision, but imaging surveillance may be suitable for papillomas no larger than 5 mm and papillomas detected by VAB.

Feasibility of using negative ultrasonography results of axillary lymph nodes to predict sentinel lymph node metastasis in breast cancer patients

Knowledge of the pathology of axillary lymph nodes (ALN) in breast cancer patients is critical for determining their treatment. Ultrasound is the best noninvasive evaluation for the ALN status. However, the correlation between negative ultrasound results and the sentinel lymph nodes (SLN) pathology remains unknown. To test the hypothesis that negative ultrasound results of ALN predict the negative pathology results of SLN in breast cancer patients, we assessed the association between ALN ultrasonography‐negative results and the SLN pathology in 3115 patients with breast cancer recruited between October 2010 and April 2016 from a single cancer center, prospective database. Of these patients who met the inclusion criteria, 2317 (74.4%) had no SLN pathological metastasis. In the univariate analysis, other 798 patient with positive SLN tended to be under age 40 and premenopausal, having large tumor sizes (>2 cm), higher histological grade of primary tumor, positive hormone receptors, and negative HER‐2 status (P < .05 for all). In the multivariate analysis, menstrual status, tumor size, ER status and histological types of primary tumor remained to be independent predictors for SLN pathological metastasis. The area under curve (AUC) was 0.658 (95% CI = 0.637‐0.679), P > .05. In conclusion, only a 74.4% consistency between ALN ultrasonography‐negative results and negative pathological SLN results, although menstrual status, tumor size, histologic subtypes of primary tumor and ER status were found to be statistically independent predictors of positive SLN among patients negative for ALN ultrasonography. Therefore, the present study suggests that negative ultrasound results of ALN do not adequately predict the negative pathology results of SLN in breast cancer patients.

A new model to predict risk of nonsentinel lymph node status in Chinese sentinel lymph node-positive patients after neoadjuvant chemotherapy

There is no previous predictive model to assess risk of nonsentinel lymph node metastases (NSLN) in sentinel lymph node (SLN)‐positive breast cancer patients after neoadjuvant chemotherapy (NAT). Our goal was to develop a new predictive model for SLN‐positive patients after NAT, and validate this new model. A series of 513 patients with metastases in SLN who received NAT were used to evaluate factors affecting NSLN status. Logistic regression analysis was performed to develop a predictive model, which was validated by a subsequent prospective 138 patients. There were 115 (22.4%) patients with metastases in NSLN followed by axillary lymph node dissection (ALND). Multivariate analysis indicated that tumor (T) stage, number of positive SLN,micrometastases, extracapsular extension (ECE), and clinical response of primary tumor after NAT were significant independent predictors for the NSLN metastases. Area under the curve (AUC) of the model was 0.795 (95% CI, 0.734‐0.861). When applied to the prospective series, the model accurately predicted the risk of NSLN disease, AUC was 0.772 (95% CI, 0.653‐0.845). We present a new predictive model to assess the risk of NSLN status in Chinese SLN‐positive breast cancer patients after NAT. The predictive model performed well in prospective validation but needs to be further studied in external center patients before application to clinical use.