Skip to main content
Thomas Ren on Muck Rack

Thomas Ren

As seen in: PLOS ONE, JAMA Network
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Thomas

Contact Thomas, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Artificial Intelligence–Based Risk Prediction Models for Complications After Tongue Cancer Surgery

|
By Dany Matar, Anahita Nimbalkar, John Lee, William Huang, Thomas Ren, Samuel Knoedler, Iman Ghanad, Dennis P. Orgill, Gabriel Hundeshagen, Adriana C Panayi, Anthony Y. Matar, Charbel G. Saad
| JAMA Network Verified
Artificial Intelligence–Based Risk Prediction Models for Complications After Tongue Cancer Surgery Key Points Question Can machine learning models provide accurate, clinically interpretable, and individualized risk prediction for major 30-day postoperative complications following glossectomy for tongue cancer?
Open in Who Shared Issue with article?

Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

|
By Albert Einstein College, Richard Adam, Thomas Ren, Aaquib Syed
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Abstract Purpose To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. Material and methods This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy.
Open in Who Shared Issue with article?

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients

|
By Albert Einstein College, Tim Q. Duong, Hongyi Dammu, Thomas Ren
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Abstract 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 progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs.
Open in Who Shared Issue with article?
See all 3 articles