The role of quantitative PET in predicting cancer treatment outcomes

By I. E. El Naqa. 2014

60 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. R. T. Ten Haken
2. Yi Luo
3. Huan-Hsin Tseng
4. Jen-Tzung Chien
5. Sunan Cui
6. Barry S. Rosenstein
7. Catharine M. West
8. J. Coates

In Radiology, Nuclear Medicine and Imaging

1. Yi Luo
2. R. T. Ten Haken
3. Huan-Hsin Tseng
4. Jen-Tzung Chien
5. Sunan Cui
6. Barry S. Rosenstein
7. Catharine M. West
8. J. Coates

Citations in the corpus

• Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer (2017, methodology): “The image features analysis was performed using customized routines in MATLAB and the features included metabolic tumor volume, intensity statistics, and texture-derived metrics [14, 15].”
• Radiogenomics and Radiotherapy Response Modeling (2017, methodology): “There are several methods to mitigate the effects of such problems, from basic data reduction or feature selection methods (Kantardzic 2011) to more advanced approaches based on incorporating prior knowledge, utilization of information theory or machine learning ensembles (El Naqa 2016).”