Intratumoral Delivery of MDNA55, an Interleukin-4 Receptor Targeted Immunotherapy, by MRI-Guided Convective Delivery for the Treatment of Recurrent Glioblastoma

https://digitalcommons.psjhealth.org/other_pubs/1047/thumbnail.jp

Current landscape of imaging and the potential role for artificial intelligence in the management of COVID-19

The clinical management of COVID-19 is challenging. Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of this disease. Chest x-ray radiography and computed tomography) are the standard imaging modalities used for the structural assessment of the disease status, while functional imaging (namely, positron emission tomography) has had limited application. Artificial intelligence can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. We review the current landscape of these imaging modalities and artificial intelligence approaches as applied in COVID-19 management

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma.

PURPOSE:To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI. METHODS:Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman's rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT. RESULTS:Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation. CONCLUSION:DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-na\uefve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status