Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning
Journal Article
Overview
publication date
- May 1, 2021
has subject area
- Adult
- Aged
- Aged, 80 and over
- Computing Methodologies - Deep Learning
- Computing Methodologies - Deep Learning
- Diagnosis, Differential
- Diagnostic Techniques and Procedures - Photoacoustic Techniques
- Digestive System Diseases - Rectal Neoplasms
- Environment and Public Health - Prospective Studies
- Female
- Gastrointestinal Diseases - Rectal Neoplasms
- Health Care Evaluation Mechanisms - Prospective Studies
- Humans
- Intestinal Diseases - Rectal Neoplasms
- Investigative Techniques - Photoacoustic Techniques
- Investigative Techniques - Prospective Studies
- Male
- Mathematical Concepts - Deep Learning
- Mathematical Concepts - Deep Learning
- Middle Aged
- Neoplasms - Neoplasm, Residual
- Neoplasms by Site - Rectal Neoplasms
- Pathologic Processes - Neoplasm, Residual
- Rectal Diseases - Rectal Neoplasms
- Ultrasonography
Date in CU Experts
- January 19, 2026 8:46 AM
Full Author List
- Leng X; Uddin KMS; Chapman W; Luo H; Kou S; Amidi E; Yang G; Chatterjee D; Shetty A; Hunt S
author count
- 12
citation count
- 28
published in
- Radiology Journal
Other Profiles
International Standard Serial Number (ISSN)
- 0033-8419
Electronic International Standard Serial Number (EISSN)
- 1527-1315
Digital Object Identifier (DOI)
Additional Document Info
start page
- 349
end page
- 358
volume
- 299
issue
- 2