Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2020 Feb 7;20:48. doi: 10.1186/s12883-020-1613-y

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s). 2020

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

PMC Copyright notice

Fig. 2 — The main procedure of the radiomic strategy for preoperative ODGs grading. Based on T1 CE and FLAIR data (a) and tumor volume of interest (VOI) manually drawn on resampled T1 CE and FLAIR images (b), a group of parametric images are derived and the corresponding parametric maps of the whole tumor region are extracted (c). Utilizing radiomic features analysis; a big collection of tumor parameter attributes was acquired for the following machine learning process (d). Feature selection methods were implemented and compared using random forest (RF) classifier with additional discussion on model parameters to construct the optimal ODG grading model (e)