Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma.

ABSTRACT:

Background

Using high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model's robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks.

Materials and methods

A total of 1,419 head-and-neck cancer patients' computed tomography images, gross tumor volume segmentation, and clinically relevant outcomes (distant metastasis and local-regional recurrence) were collected from four publicly available datasets. The perturbation method was implemented to simulate images, and the radiomic feature robustness was quantified using intra-class correlation of coefficient (ICC). Three radiom

SUBMITTER: Teng X 

PROVIDER: S-EPMC9614273 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature