# A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-machine-learning-approach-to-radiogenomics-of-breast-cancer-a-study-of-922/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ashirbani Saha,Michael R. Harowicz,Lars J. Grimm,Connie E. Kim,Sujata V. Ghate,Ruth Walsh,Maciej A. Mazurowski |
| Citations | 305 |
| DOI | 10.1038/s41416-018-0185-8 |
| Fields | Biochemistry, Genetics and Molecular Biology,Medicine |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://www.nature.com/articles/s41416-018-0185-8.pdf |
| OpenAlex ID | https://openalex.org/W2884716214 |
| PMID | 30033447 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Lars J. Grimm](https://scholariq.org/researchers/lars-j-grimm/)

## Paper journal

- [British Journal of Cancer](https://scholariq.org/journals/british-journal-of-cancer/)

## 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/)
- [Breast Cancer Treatment Studies](https://scholariq.org/topics/breast-cancer-treatment-studies/)

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