Radiogenomics and IR

Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom

BACKGROUND

Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development.

METHODS

Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability.

RESULTS

T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets.

CONCLUSIONS

A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application.

Beyond the Microbiome: Germ-ganism? An Integrative Idea for Microbial Existence, Organization, Growth, Pathogenicity, and Therapeutics

The advances made by microbiome research call for new vocabulary and expansion of our thinking in microbiology. For example, the life-forms presenting in both unicellular and multicellular formats invite us to rethink microbial existence, organization, growth, pathogenicity, and therapeutics in the 21st century. A view of such populations as parts of single organisms with a loose, distributed multicellular organization, introduced here as a germ-ganism, rather than communities, might open up interesting prospects for diagnostics and therapeutics innovation. This study tested and further contextualized the concept of germ-ganism using solid cultures of bacteria and fungi. Based on our findings and the literature reviewed herein, we propose that germ-ganism has synergy with a systems medicine approach by broadening host-environment interactions from cells and microorganisms to a scale of biological ecosystems. Germ-ganism also brings about the possibility of studying the multilevel impacts of novel therapeutic agents within and across networks of microbial ecosystems. The germ-ganism would lend itself, in the long term, to a veritable biocybernetics system, while in the mid-term, we anticipate it will contribute to new diagnostics and therapeutics. Biosecurity applications would be immensely affected by germ-ganism. Industrial applications of germ-ganism are of interest as a more sustainable alternative to costly solutions such as tampered strains/microorganisms. In conclusion, germ-ganism is informed by lessons from microbiome research and invites rethinking microbial existence, organization, and growth as an organism. Germ-ganism has vast ramifications for understanding pathogenicity, and clinical, biosecurity, and biotechnology applications in the current historical moment of the COVID-19 pandemic and beyond.

Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Background

To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features.

Methods

mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed.

Results

In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful.

Conclusions

Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes.

Radiogenomics and Radiomics in Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.