Learning from scanners: Bias reduction and feature correction in radiomics.

ABSTRACT:

Purpose

Radiomics are quantitative features extracted from medical images. Many radiomic features depend not only on tumor properties, but also on non-tumor related factors such as scanner signal-to-noise ratio (SNR), reconstruction kernel and other image acquisition settings. This causes undesirable value variations in the features and reduces the performance of prediction models. In this paper, we investigate whether we can use phantom measurements to characterize and correct for the scanner SNR dependence.

Methods

We used a phantom with 17 regions of interest (ROI) to investigate the influence of different SNR values. CT scans were acquired with 9 different exposure settings. We developed an additive correction model to reduce scanner SNR influence.

Results

Sixty-t

SUBMITTER: Zhovannik I 

PROVIDER: S-EPMC6690665 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature