Simple Summary Despite the increased use of neoadjuvant chemotherapy in the early
setting of breast cancer, there is a clinical need for predictive markers of response in daily …

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 …

Purpose To predict pathological complete response (pCR) after neoadjuvant chemotherapy
using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple …

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant
chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict …

We performed a systematic review and a meta-analysis of studies using MRI-radiomics for
predicting the pathological complete response in breast cancer patients undergoing …

Background Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer
patients is essential for prognosis and treatment planning. Current radiological staging of …

Bias in applications of machine learning (ML) to healthcare is usually attributed to
unrepresentative or incomplete data, or to underlying health disparities. This article identifies …

Background Achieving pathological complete response (pCR) to neoadjuvant
chemotherapy (NAC) improves survival outcomes for breast cancer patients. Currently …

Lung cancer is the most common and second leading cause of cancer with lowest survival
rate due to lack of efficient diagnostic tools. Currently, researchers are devising artificial …