Discrimination of an infected brain tumor from a cerebral abscess by combined MR perfusion and diffusion imaging

Background and purpose: CT perfusion (CTP) provides rapid and accessible imaging of ischemic stroke pathophysiology. Studies with limited brain coverage CTP have suggested that relative cerebral blood flow (relCBF) is the optimal CTP parameter to define irreversible infarction. We analyzed patients with whole brain CT perfusion and contemporaneous MR perfusion-diffusion imaging to confirm the optimal CTP parameter for infarct core and compare mismatch classification between MR and CT. Methods: Acute ischemic stroke patients <6hr after onset had whole brain CTP (320slice) closely followed by perfusion-diffusion MRI. Maps of CBF, CBV and time-to-peak of the deconvolved tissue residue function (Tmax) were generated by RAPID automated perfusion analysis software (Stanford University) using delay insensitive deconvolution. The optimal CTP map to identify infarct core was selected by maximizing the average Dice co-efficient across the same threshold range for all patients using co-registered diffusion lesion (manually outlined to its maximal visual extent) as reference region. Mismatch classification agreement between CT and MRI was then assessed using 2 definitions: mismatch ratio a) >1.2 or b) >1.8, absolute mismatch a) >10mL or b) >15mL, infarct core<70mL. Results: In 28 patients imaged <6hr from stroke onset (median age 69, median onset to CT 180min, median CT to MR 69min), relCBF provided the most accurate estimate for infarct core, significantly better than absolute or relative CBV (both p<0.001). Using relCBF to generate acute CTP infarct core volumes, the median magnitude of volume difference versus diffusion MR was 6.9mL, interquartile range 1.6-27.4mL. CTP mismatch between relCBF core and Tmax>6sec perfusion lesion was assessed in 25 patients (3/28 had no MR perfusion). CTP and MR perfusion-diffusion mismatch classification agreed in 23/25 (92%) patients (kappa 0.84) using either definition. Conclusions: This study using whole brain CTP confirms the greater accuracy of CBF over CBV for estimation of the infarct core. The >90% agreement in mismatch classification between CTP and MRI supports the concept that both modalities can identify similar patient populations for clinical trials of reperfusion therapies.

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Utilising functional imaging to predict survival in paediatric brain tumours.

Neuro-Oncology ◽

10.1093/neuonc/noz167.018 ◽

2019 ◽

Vol 21 (Supplement_4) ◽

pp. iv5-iv5

Author(s):

James Grist ◽

Stephanie Withey ◽

Lesley MacPherson ◽

Adam Oates ◽

Mr Stephen Powell ◽

...

Keyword(s):

Functional Imaging ◽

Brain Tumours ◽

Diffusion Imaging ◽

Supervised Machine Learning ◽

Imaging Features ◽

Tumour Grade ◽

Survival Status ◽

Significant Difference ◽

Paediatric Brain Tumours ◽

And Diffusion

Abstract Introduction Brain tumours are a common cause of death in the paediatric population. We have previously shown that MR imaging and spectroscopy can be used to non-invasively differentiate between tumour types. Here, we demonstrate that functional imaging can be highly predictive of survival and grade in a paediatric cohort. Methods Perfusion (PWI) and diffusion weighted imaging (DWI) were performed in a multi-site (Birmingham Children’s Hospital, Royal Victoria Infirmary, Alder Hey, Nottingham) cohort ([grade, 5-year survival alive:dead number] = [I,15:1],[II, 5:1],[III,2:3],[IV,8:11]). ROIs were drawn on T2 imaging and functional imaging features (mean, standard deviation, skewness, and kurtosis) were derived. Supervised machine learning was used to predict 5-year survival and tumour grade from features. ANOVA and post-hoc tests were used to assess differences in features between grade and 5-year survival status. Results 5-year survival was predicted with 89%, 85%, and 87% accuracy with all imaging, perfusion, or diffusion features, respectively. A significant difference in perfusion was found between surviving and diseased participants (1.71 ± 0.82 vs 2.62 ± 1 mL/100g/min, respectively, p < 0.05). A significant difference in ADC (mm2 s-1) between tumour grades was found (1 vs 4 (1533 ± 458 vs 857 ± 239), 4 vs 3 (857 ± 239 vs 1197 ± 137), 4 vs 2 (857 ± 239 vs 1440 ± 557), corrected p < 0.05). Conclusion We have shown that perfusion and diffusion imaging features can be used to non-invasively assess tumour grade and estimate 5-year survival status in a cohort of paediatric brain tumours.

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