Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.

ABSTRACT: Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different p

SUBMITTER: Du Q 

PROVIDER: S-EPMC6504105 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature