Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

By E. Limkin, R. Sun, L. Dercle, Evangelia I. Zacharaki, C. Robert, S. Reuzé, A. Schernberg, Nikos Paragios, É. Deutsch, C. Ferté. Annals of Oncology, 2017

665 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/96187fedfa51e365e806b2ffb5d68966b68a12e3

How researchers see this paper

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

1. Di Dong
2. Shuo Wang
3. Zhenyu Liu
4. Jingwei Wei
5. Meiyun Wang
6. Cheng Fang
7. Xuezhi Zhou
8. Jie Tian

Citations in the corpus

• The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Clinical experts often think of the radiomic model as a black box that can provide good prediction results for particular clinical outcomes [131, 198], which tends to make radiomics a less accepted approach.”