MRI radiomic features to predict idh1 mutation status in gliomas: A machine learning approach using gradient tree boosting

Yu Sakai, Chen Yang, Shingo Kihira, Nadejda Tsankova, Fahad Khan, Adilia Hormigo, Albert Lai, Timothy Cloughesy, Kambiz Nael

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Fingerprint

Dive into the research topics of 'MRI radiomic features to predict idh1 mutation status in gliomas: A machine learning approach using gradient tree boosting'. Together they form a unique fingerprint.

Physics & Astronomy

Medicine & Life Sciences

Engineering & Materials Science

Chemical Compounds