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Purpose To identify precise three-dimensional 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 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article . © RSNA, 2024 See also the commentary by Sagreiya in this issue.

Listeria monocytogenes (LM) modifies the phagocytic compartment by targeting Rab5a function through an unknown mechanism. Inhibition of Rab5a exchange by LM can be considered the main virulence mechanism as it favours viability of the parasite within the phagosome as well as the exclusion of putative listericidal lysosomal proteases such as cathepsin-D. The significance of this survival mechanism is evidenced by the overexpression of Rab5a mutants in CHO cells that promoted GDP exchange on Rab5a and eliminated pathogenic LM. The following mutants showed listericidal effects: Rab5a:Q79L, a constitutively active mutant with accelerated GDP exchange and Rab5a GEF, Vps9, which overactivates the endogenous protein. Clearance of LM from these phagosomes was controlled by the hydrolytic action of cathepsin-D as suggested by the lysosomal protease inhibitor chloroquine, or the cathepsin-D inhibitor, pepstatin A, which caused a reversion of listericidal activity. Moreover, the effects of LM on Rab5a phagocytic function mimics those reported for the GDP locked dominant negative Rab5a mutant, S34N. Transfection of these mutants into CHO cells increased pathogen survival as they showed higher numbers of viable bacteria, complete inhibition of GDP exchange on Rab5a and impairment of the listericidal action probably exerted by cathepsin-D. We cotransfected functional Rab5a GEF into this dominant negative mutant and restored normal LM intraphagosomal viability, Rab5a exchange and listericidal action of cathepsin-D.