Effect of a machine learning prediction model on the false-negative rate of sentinel lymph node biopsy for clinically node-positive breast cancer after neoadjuvant chemotherapy.

Background: Machine learning (ML) models can be used to predict axillary pathological complete responses (pCRs) in clinically node-positive (cN+) breast cancer after neoadjuvant chemotherapy (NAC). We developed an ML model combining clinicopathological characteristics and axillary ultrasound features before and after NAC to predict the possibility of axillary pCR in NAC-treated cN + breast cancer.

Methods: Patients with cN + breast cancer who received NAC were categorized into training and verification cohorts (7:3 ratio).

miR-7 Reverses Breast Cancer Resistance To Chemotherapy By Targeting MRP1 And BCL2.

Background: MicroRNAs (miRNAs) are a class of non-coding RNAs that have been linked with breast cancer chemoresistance, which is a major clinical problem causing disease relapse and poor prognosis. miR-7 exerts several tumor suppressive activities.

Purpose: This study was designed to clarify whether and how miR-7 regulates breast cancer chemoresistance.