PURPOSE Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast
cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response …

PURPOSE The optimal characteristics among patients with breast cancer to recommend
neoadjuvant chemotherapy is an active area of clinical research. We developed and …

Purpose To determine whether a multivariate machine learning-based model using
computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic …

The achievement of the pathologic complete response (pCR) has been considered a metric
for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of …

Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of
treatment and likelihood of a specific status of an individual patient, has been used for …

Background Generalizable population-based studies are unable to account for individual
tumor heterogeneity that contributes to variability in a patient's response to physician-chosen …

Background Neoadjuvant chemotherapy (NAC) has become the standard therapeutic option
for early high-risk and locally advanced breast cancer. However, response rates to NAC vary …

Purpose The aim of this meta-analysis was to determine the diagnostic accuracy of machine
learning (ML) models with MRI in predicting pathological response to neoadjuvant …

The goal of this study was to employ novel deep-learning convolutional-neural-network
(CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and …

Objective To employ machine learning methods to predict the eventual therapeutic response
of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials …