NSCLC tumor shrinkage prediction using quantitative image features

Highlights • Lung tumors shrink during radiotherapy, with much variation between patients. • Pre-treatment CT images can be used to predict tumor shrinkage after treatment. • Potential uses include identifying patients who will benefit from adaptive radiation therapy.

Saved in:
Bibliographic Details
Published in:Computerized medical imaging and graphics 2016-04, Vol.49, p.29-36
Main Authors: Hunter, Luke A, Chen, Yi Pei, Zhang, Lifei, Matney, Jason E, Choi, Haesun, Kry, Stephen F, Martel, Mary K, Stingo, Francesco, Liao, Zhongxing, Gomez, Daniel, Yang, Jinzhong, Court, Laurence E
Format: Article
Language:English
Subjects:
Algorithms
Carcinoma, Non-Small-Cell Lung - diagnostic imaging
Carcinoma, Non-Small-Cell Lung - radiotherapy
Feature extraction
Humans
Internal Medicine
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - radiotherapy
Mathematical models
Medical services
NSCLC
Other
Patients
Pattern Recognition, Automated - methods
prediction
Pretreatment
Prognosis
Quantitative image feature
Radiographic Image Interpretation, Computer-Assisted - methods
Radiotherapy, Image-Guided - methods
Reproducibility of Results
Sensitivity and Specificity
Shrinkage
Subtraction Technique
texture
Tomography, X-Ray Computed - methods
Treatment Outcome
Tumor Burden
tumor shrinkage
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Highlights • Lung tumors shrink during radiotherapy, with much variation between patients. • Pre-treatment CT images can be used to predict tumor shrinkage after treatment. • Potential uses include identifying patients who will benefit from adaptive radiation therapy.
ISSN:0895-6111
1879-0771
DOI:10.1016/j.compmedimag.2015.11.004