| Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI Z Zhou, BE Adrada, RP Candelaria, NA Elshafeey, M Boge, ... Scientific reports 13 (1), 1171, 2023 | 13 | 2023 |
| Assessment of response to neoadjuvant systemic treatment in triple-negative breast cancer using functional tumor volumes from longitudinal dynamic contrast-enhanced MRI B Panthi, BE Adrada, RP Candelaria, MS Guirguis, C Yam, M Boge, ... Cancers 15 (4), 1025, 2023 | 10 | 2023 |
| A radiomics model based on synthetic MRI acquisition for predicting neoadjuvant systemic treatment response in triple-negative breast cancer KP Hwang, NA Elshafeey, A Kotrotsou, H Chen, JB Son, M Boge, ... Radiology: Imaging Cancer 5 (4), e230009, 2023 | 4 | 2023 |
| Diffusion Tensor Imaging for Characterizing Changes in Triple‐Negative Breast Cancer During Neoadjuvant Systemic Therapy BC Musall, DE Rauch, RMM Mohamed, B Panthi, M Boge, RP Candelaria, ... Journal of Magnetic Resonance Imaging, 2024 | 1 | 2024 |
| Hydrophobicity of small alkane molecules (propane dimer) in solvents: a classical molecular dynamics study B Panthi, N Pantha Bibechana 17, 1-12, 2020 | 1 | 2020 |
| Abstract PS05-07: Early prediction of response to Neoadjuvant Immunotherapy in Triple Negative Breast Cancer (TNBC) with DCE-MRI G Rauch, M Guirguis, M Patel, R Candelaria, R Mohamed, T Moseley, ... Cancer Research 84 (9_Supplement), PS05-07-PS05-07, 2024 | | 2024 |
| Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer Z Xu, DE Rauch, RM Mohamed, S Pashapoor, Z Zhou, B Panthi, JB Son, ... Cancers 15 (19), 4829, 2023 | | 2023 |
| Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs Z Zhou, BE Adrada, RP Candelaria, NA Elshafeey, M Boge, ... 2023 45th Annual International Conference of the IEEE Engineering in …, 2023 | | 2023 |
| EARLY PREDICTION OF NEOADJUVANT SYSTEMIC THERAPY RESPONSE IN TRIPLE NEGATIVE BREAST CANCER USING FUNCTIONAL MAGNETIC RESONANCE IMAGING B Panthi | | 2023 |
| Abstract P6-01-06: Multi-Parametric MRI-Based Radiomics Models from Tumor and Peritumoral Regions as Potential Predictors of Treatment Response to Neoadjuvant Systemic Therapy … RM Mohamed, B Panthi, B Adrada, R Candelaria, MS Guirguis, W Yang, ... Cancer Research 83 (5_Supplement), P6-01-06-P6-01-06, 2023 | | 2023 |
| Abstract P6-01-35: A Pre-operative Dynamic Contrast Enhanced MRI-Based Radiomics Models as Predictors of Treatment Response after Neoadjuvant Systemic Therapy in Triple … RM Mohamed, B Panthi, B Adrada, R Candelaria, MS Guirguis, W Yang, ... Cancer Research 83 (5_Supplement), P6-01-35-P6-01-35, 2023 | | 2023 |
| Abstract P6-01-34: Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients B Panthi, RM Mohamed, B Adrada, R Candelaria, MS Guirguis, W Yang, ... Cancer Research 83 (5_Supplement), P6-01-34-P6-01-34, 2023 | | 2023 |
| Abstract P1-05-15: DCE-MRI for early prediction of excellent response versus chemoresistance in triple negative breast cancer MS Guirguis, B Adrada, M Patel, F Perez, R Candelaria, W Yang, J Sun, ... Cancer Research 83 (5_Supplement), P1-05-15-P1-05-15, 2023 | | 2023 |
| Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer B Panthi, RM Mohamed, BE Adrada, M Boge, RP Candelaria, H Chen, ... Frontiers in Oncology 13, 2023 | | 2023 |
| A cross-modality deep learning model for esophageal cancer segmentation and quantitation on 18 F-FDG PET/CT and diffusion weighted MRI Z Zhou, B Panthi, DE Rauch, JB Son, CC Wu, SH Lin, MD Pagel, J Ma | | |
| The video domain transfer deep learning network with error correction for Dixon Imaging with consistent slice-to-slice water and fat separation JB Son, D Rauch, B Panthi, Z Zhou, B Musall, M Scoggins, M Pagel, J Ma | | |
| Predicting breast cancer treatment response using a hybrid deep learning network on multislice SyntheticMR images JB Son, KP Hwang, DE Rauch, JH Ahn, J Lee, Z Zhou, B Panthi, B Adrada, ... | | |
