Improved prognostication for lung cancer patients from computed tomography imaging using deep learning.

Authors

null

Felipe Torres

University of Toronto, Toronto, ON, Canada

Felipe Torres , Shazia Akbar , Felix Baldauf-Lenschen , Natasha B. Leighl

Sign-In to See More Abstracts, Journal Articles, Posters, Videos and Slides and to Bookmark Your Favorite Content.

Disclaimer

This material on this page is ©2024 American Society of Clinical Oncology, all rights reserved. Licensing available upon request. For more information, please contact licensing@asco.org

Poster Details

Meeting

2020 ASCO Virtual Scientific Program

Session Type

Poster Session

Session Title

Care Delivery and Regulatory Policy

Track

Care Delivery and Quality Care

Sub Track

Clinical Informatics/Advanced Algorithms/Machine Learning

Citation

J Clin Oncol 38: 2020 (suppl; abstr 2044)

DOI

10.1200/JCO.2020.38.15_suppl.2044

Abstract #

2044

Poster Bd #

36

Abstract Disclosures

Similar Posters

First Author: Felipe Soares Torres

Poster

2024 ASCO Annual Meeting

Cost-effectiveness of a circulating tumor fraction molecular biomarker for treatment response monitoring.

Cost-effectiveness of a circulating tumor fraction molecular biomarker for treatment response monitoring.

First Author: Zachary Rivers

Poster

2024 ASCO Annual Meeting

Novel method (MAXIM) uses deep learning model to impute missing stains in multiplex images (mIF).

Novel method (MAXIM) uses deep learning model to impute missing stains in multiplex images (mIF).

First Author: Muhammad Shaban

Poster

2024 ASCO Annual Meeting

Diagnostic test use and time to cancer diagnosis in Medicare recipients.

Diagnostic test use and time to cancer diagnosis in Medicare recipients.

First Author: Karen Chung