Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

(2021) Lal Hussain et al.   Biomedical Engineering Online

Multicontrast MRI-based radiomics for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with early triple negative breast cancer

(2021) Angeline Nemeth et al.   MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

(2021) Sunghoon Joo et al.   Scientific Reports

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

(2020) Jiali Zhou et al.   BMC CANCER

Introduction to Radiomics

(2020) Marius E Mayerhoefer et al.   JOURNAL OF NUCLEAR MEDICINE

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method

(2020) Yu‐Hong Qu et al.   Thoracic Cancer

The diagnostic performance of CESM and CE-MRI in evaluating the pathological response to neoadjuvant therapy in breast cancer: a systematic review and meta-analysis

(2020) Sudan Tang et al.   BRITISH JOURNAL OF RADIOLOGY

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images

(2020) Mohammed El Adoui et al.   International Journal of Computer Assisted Radiology and Surgery

Radiomics of multi-parametric MRI for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study

(2019) Zhenyu Liu et al.   CLINICAL CANCER RESEARCH

Machine learning in breast MRI

(2019) Beatriu Reig et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Overview of radiomics in breast cancer diagnosis and prognostication

(2019) Alberto Stefano Tagliafico et al.   BREAST

Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer

(2019) Na Lae Eun et al.   RADIOLOGY

Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

(2018) Matthew D. F. McInnes et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Neoadjuvant chemotherapy in breast cancer: more than just downsizing

(2018) Marloes G M Derks et al.   LANCET ONCOLOGY

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

(2018) Elizabeth Hope Cain et al.   BREAST CANCER RESEARCH AND TREATMENT

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset

(2018) Richard Ha et al.   JOURNAL OF DIGITAL IMAGING

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients

(2018) Amirhessam Tahmassebi et al.   INVESTIGATIVE RADIOLOGY

The diagnostic accuracy of serological tests for Lyme borreliosis in Europe: a systematic review and meta-analysis

(2016) M. M. G. Leeflang et al.   BMC INFECTIOUS DISEASES

Multi-parametric MRI in the early prediction of response to neo-adjuvant chemotherapy in breast cancer: Value of non-modelled parameters

(2016) Elizabeth AM O’Flynn et al.   EUROPEAN JOURNAL OF RADIOLOGY

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging

(2014) Nariya Cho et al.   RADIOLOGY

QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies

(2013) Penny F. Whiting   ANNALS OF INTERNAL MEDICINE

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

(2013) Subramani Mani et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

Neoadjuvant chemotherapy for breast cancer: any progress?

(2013) Marco Colleoni et al.   LANCET ONCOLOGY

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: Preliminary results

(2013) Richard G. Abramson et al.   MAGNETIC RESONANCE IMAGING

Early prediction of pathologic response to neoadjuvant therapy in breast cancer: Systematic review of the accuracy of MRI

(2012) M.L. Marinovich et al.   BREAST

Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIAL

(2012) Nola M. Hylton et al.   RADIOLOGY

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

(2010) Lori R. Arlinghaus et al.   Journal of Oncology