Published June 2016
| Version v1
Journal article
Radiomic phenotype features predict pathological response in non-small cell lung cancer
Creators
- 1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston (United States)
- 2. Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston (United States)
Description
Background and purposeRadiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).
Availability note (English)
Available from http://dx.doi.org/10.1016/j.radonc.2016.04.004Additional details
Identifiers
- DOI
- 10.1016/j.radonc.2016.04.004;
- PII
- S0167814016310386;
Publishing Information
- Journal Title
- Radiotherapy and Oncology
- Journal Volume
- 119
- Journal Issue
- 3
- Journal Page Range
- p. 480-486
- ISSN
- 0167-8140
- CODEN
- RAONDT
INIS
- Country of Publication
- Ireland
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 51004930
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- ALGORITHMS; BIOLOGICAL MARKERS; LUNGS; NEOPLASMS; PATIENTS; PHENOTYPE
- Descriptors DEC
- BODY; DISEASES; MATHEMATICAL LOGIC; ORGANS; RESPIRATORY SYSTEM
Optional Information
- Copyright
- Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.