Multimodal Spatiotemporal Deep Learning Framework to Predict Response of Breast Cancer to Neoadjuvant Systemic Therapy
Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given tr...
Main Authors: | Verma, Monu, Abdelrahman, Leila, Collado-Mesa, Fernando, Abdel-Mottaleb, Mohamed |
---|---|
Other Authors: | Massachusetts Institute of Technology. Media Laboratory |
Format: | Article |
Published: |
Multidisciplinary Digital Publishing Institute
2023
|
Online Access: | https://hdl.handle.net/1721.1/151112 |
Similar Items
-
Outcome after neoadjuvant chemotherapy in Asian breast cancer patients
by: Lim, L.Y., et al.
Published: (2016) -
Role of vascular density and normalization in response to neoadjuvant bevacizumab and chemotherapy in breast cancer patients
by: Tolaney, Sara M., et al.
Published: (2017) -
Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer
by: Tan, Hong Qi, et al.
Published: (2022) -
Re-Excision Rates in Breast-Conserving Surgery for Invasive Breast Cancer after Neoadjuvant Chemotherapy with and without the Use of a Radiopaque Tissue Transfer and X-ray System
by: Jamaris, Suniza, et al.
Published: (2018) -
HARIRAYA: a novel breast cancer pseudo-color feature for multimodal mammogram using deep learning
by: Ahmad Nazri, Azree Shahrel, et al.
Published: (2018)