# Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/tumor-radiomic-heterogeneity-multiparametric-functional-imaging-to-characterize/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Stephen R. Bowen,William T. C. Yuh,Daniel S. Hippe,Wei Wu,Savannah C. Partridge,Saba Elias,Guang Jia,Zhibin Huang,George A. Sandison,Dennis P. Nelson,Michael V. Knopp,Simon S. Lo,Paul E. Kinahan,Nina A. Mayr |
| Citations | 117 |
| DOI | 10.1002/jmri.25874 |
| Fields | Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2765456966 |
| PMID | 29044908 |
| Type | article |
| Year | 2017 |

## Paper authors

- [Zhibin Huang](https://scholariq.org/researchers/zhibin-huang/)

## Paper primary topic

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

## Paper topics

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [MRI in cancer diagnosis](https://scholariq.org/topics/mri-in-cancer-diagnosis/)
- [Endometrial and Cervical Cancer Treatments](https://scholariq.org/topics/endometrial-and-cervical-cancer-treatments/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.