Area of research
Biomedical Engineering · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Breast cancer, Medicine, Ultrasound, Chemotherapy, Radiomics, and Computer science.
Synthesizing Ultrasound B-mode Images from Subsampled RF Data: A Data-Driven Deep Learning Approach <sup>*</sup>
Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results
QUANTITATIVE ULTRASOUND RADIOMICS GUIDED ADAPTIVE NEOADJUVANT CHEMOTHERAPY IN BREAST CANCER: A RANDOMIZED STUDY
Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study
Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy
Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning
Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment
Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study
Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results
Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer
A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning
Effect of Treatment Sequencing on the Tumor Response to Combined Treatment With <scp>Ultrasound‐Stimulated</scp> Microbubbles and Radiotherapy
Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers
Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound
Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis
Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models
Predictive Quantitative Ultrasound Radiomic Markers Associated With Treatment Response in Head and Neck Cancer