Clinical feasibility and oncological safety of non-radioactive targeted axillary dissection after neoadjuvant chemotherapy in biopsy-proven node-positive breast cancer: a prospective diagnostic and prognostic study.

BACKGROUND: Targeted axillary dissection (TAD) includes biopsy of clipped lymph node and sentinel lymph nodes. However, clinical evidence regarding clinical feasibility and oncological safety of non-radioactive TAD in a real-world cohort remains limited. METHODS: In this prospective registry study, patients routinely underwent clip insertion into biopsy-confirmed lymph node. Eligible patients received neoadjuvant chemotherapy followed by axillary surgery. Main endpoints included the false-negative rate (FNR) of TAD and nodal recurrence rate. RESULTS: Data from 353 eligible patients were analyzed. After completion of neoadjuvant chemotherapy, 85 patients directly proceeded to axillary lymph node dissection (ALND), furthermore, TAD with or without ALND was performed in 152 and 85 patients, respectively. Overall detection rate of clipped node was 94.9% (95% CI, 91.3-97.4%) and FNR of TAD was 12.2% (95% CI, 6.0-21.3%) in our study, with FNR decreasing to 6.0% (95% CI, 1.7-14.6%) in initially cN1 patients. During a median follow-up of 36.6 months, 3 nodal recurrences occurred (3/237 with ALND; 0/85 with TAD alone), with a 3-year freedom-from-nodal-recurrence rate of 100.0% among the TAD-only patients and 98.7% among the ALND patients with axillary pathologic complete response ( P =0.29). CONCLUSIONS: TAD is feasible in initially cN1 breast cancer patients with biopsy-confirmed nodal metastases. ALND can safely be foregone in patients with negativity or a low volume of nodal positivity on TAD, with a low nodal failure rate and no compromise of 3-year recurrence-free survival.

In-depth mining of clinical data: the construction of clinical prediction model with R.

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.