Supplementary Table 5 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer

Authors: Eleftherios P. Mamounas, Ming Chen, Joseph A. Sparano et al.

Publication:

Published: Unknown date

Source: Crossref

Back to Search View Original Cite This Article

Abstract

<jats:p><p>Prognostic performance of MI Clarity M3T model for breast cancer-free interval (BCFI), disease-free survival (DFS) and recurrence-free interval (RFI).</p></jats:p>

Keywords

interval pprognostic performance clarity model

Supplementary Table 5 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer

Abstract

Keywords

Related Articles

From Medical Device Fraud to Brand Repositioning: Rebuilding Social Trust in the Korean Breast Implant Industry (Preprint)

Trends in indications and techniques of corneal Transplantation from 2010 through 2024 at a tertiary referral centre in Rabat, Morocco

Supplementary Code from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer

Supplementary material to "Investigating the cloud liquid water and droplet concentration relationship across cloud morphologies with machine learning"

Scalable and robust phylogenetic tree reconstruction from copy-number data with Sparse Rooted Neighbor Joining