Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.

Purpose: To assess the prevalence and nature of incidental findings (IF) seen in low-dose computed tomographies (LDCT) from a lung cancer screening study for at-risk individuals. Materials and Methods: Radiology reports from LDCTs of 4073 participants of a lung cancer screening study were retrospectively reviewed for findings other than lung nodules, that is, IFs, which were regarded as actionable. The frequency, nature, and expected cost of these IFs, and their anticipated follow-up were estimated. Results: There were 880 IFs described in 782 study participants (19%); the median age of the participants was 62 years (range, 46–80 years). More IFs were found in men (55%) than in women. The majority of these findings were noncardiovascular (76%), for which imaging was suggested for 74%. There were 7 severe IFs (0.8%) that merited immediate attention. Seven known cancers were diagnosed from follow-ups of the IFs. The majority of IFs ( n = 486 [55%]) would require imaging follow-up if clinically indicated, with an estimated total a cost of CAN$45,500 to CAN$51,000 to provide initial diagnostic workup. Conclusion: IFs on lung cancer screening studies are not uncommon and frequently require imaging or other follow-up for definitive diagnoses and to assess their clinical relevance. The implication of IFs has to be considered when determining a cost-effective and ethical protocol for the utilisation of LDCT in a high-risk population.

The fusogenic orthoreoviruses express nonstructural fusion-associated small transmembrane (FAST) proteins that induce cell-cell fusion and syncytium formation. It has been speculated that the FAST proteins may serve as virulence factors by promoting virus dissemination and increased or altered cytopathology. To directly test this hypothesis, the gene encoding the p14 FAST protein of reptilian reovirus was inserted into the genome of a heterologous virus that does not naturally form syncytia, vesicular stomatitis virus (VSV). Expression of the p14 FAST protein by the VSV/FAST recombinant gave the virus a highly fusogenic phenotype in cell culture. The growth of this recombinant fusogenic VSV strain was unaltered in vitro but was significantly enhanced in vivo. The VSV/FAST recombinant consistently generated higher titers of virus in the brains of BALB/c mice after intranasal or intravenous infection compared to the parental VSV/green fluorescent protein (GFP) strain that expresses GFP in place of p14. The VSV/FAST recombinant also resulted in an increased incidence of hind-limb paralysis, it infected a larger volume of brain tissue, and it induced more extensive neuropathology, thus leading to a lower maximum tolerable dose than that for the VSV/GFP parental virus. In contrast, an interferon-inducing mutant of VSV expressing p14 was still attenuated, indicating that this interferon-inducing phenotype is dominant to the fusogenic properties conveyed by the FAST protein. Based on this evidence, we conclude that the reovirus p14 FAST protein can function as a bona fide virulence factor.