“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To identify precise 3D radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 318 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). 3D radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius (R) and bin size (B). The lower 95% confidence limit (LCL) of the intraclass cor