Relationship between white matter hyperintensities volume and the circle of Willis configurations in patients with carotid artery pathology

Purpose We aimed to assess if there is a difference of distribution and volume of white matter hyperintensities (WMH) in the brain according to the Circle of Willis (CoW) configuration in patients with carotid artery pathology. Material and methods One-hundred consecutive patients (79 males, 21 females; mean age 70 years; age range 46–84 years) that underwent brain MRI before carotid endarterectomy (CEA) were included. FLAIR-WMH lesion volume was performed using a semi-automated segmentation technique and the status of the circle of Willis was assessed by two neuroradiologists in consensus. Results We found a prevalence of 55% of variants in the CoW configuration; 22 cases had one variants (40%); 25 cases had two variants (45.45%) and 8 cases showed 3 variants (14.55%). The configuration that was associated with the biggest WMH volume and number of lesions was the A1 + PcoA + PcoA. The PcoA variants were the most prevalent and there was no statistically significant difference in number of lesions and WMH for each vascular territory assessed and the same results were found for AcoA and A1 variants. Conclusion Results of our study suggest that the more common CoW variants are not associated with the presence of an increased WMH or number of lesions whereas uncommon configurations, in particular when 2 or more segment are missing increase the WMH volume and number of lesions. The WHM volume of the MCA territory seems to be more affected by the CoW configuration

Qualitative analysis of small (≤2 cm) regenerative nodules, dysplastic nodules and well-differentiated HCCs with gadoxetic acid MRI

$\textbf{BACKGROUND}$: The characterization of small lesions in cirrhotic patients is extremely difficult due to the overlap of imaging features among different entities in the step-way of the hepatocarcinogenesis. The aim of our study was to evaluate the role of gadoxetic-acid MRI in the differentiation of small (≤2 cm) well-differentiated hepatocellular carcinomas from regenerative and dysplastic nodules. $\textbf{METHODS}$: Seventy-three cirrhotic patients, with 118 focal liver lesions (≤2 cm) were prospectively recruited. MRI examination was performed with a 3T magnet and the study protocol included T1 - and T2-weighted pre-contrast sequences and T1 -weighted gadoxetic-acid enhanced post-contrast sequences obtained during the arterial, venous, late dynamic and hepatobiliary phases. All lesions were pathologically confirmed. Two radiologists blinded to clinical and pathological information evaluated two imaging datasets; another radiologist analysed the signal intensity characteristics of each lesion. Sensitivity, specificity and diagnostic accuracy were considered for statistical analysis. $\textbf{RESULTS}$: Good agreement was reported between the two readers (κ 0.70). Both readers reported a significantly improved sensitivity (57.7 and 66.2 vs 74.6 and 83.1) and diagnostic accuracy (0.717 and 0.778 vs 0.843 and 0.901) with the adjunction of the hepatobiliary phase 57.7 vs 74.6 and 66.2 vs 83.1 (p ≤ 0.04). $\textbf{CONCLUSIONS}$: Gadoxetic-acid MRI is a reliable tool for the characterization of HCC and lesions at high risk to further develop

Non-small-cell lung cancer resectability: diagnostic value of PET/MR.

Purpose To assess the diagnostic performance of PET/MR in patients with non-small-cell lung cancer. Methods Fifty consecutive consenting patients who underwent routine 18F-FDG PET/CT for potentially radically treatable lung cancer following a staging CT scan were recruited for PET/MR imaging on the same day. Two experienced readers, unaware of the results with the other modalities, interpreted the PET/MR images independently. Discordances were resolved in consensus. PET/MR TNM staging was compared to surgical staging from thoracotomy as the reference standard in 33 patients. In the remaining 17 nonsurgical patients, TNM was determined based on histology from biopsy, imaging results (CT and PET/CT) and follow-up. ROC curve analysis was used to assess accuracy, sensitivity and specificity of the PET/MR in assessing the surgical resectability of primary tumour. The kappa statistic was used to assess interobserver agreement in the PET/MR TNM staging. Two different readers, without knowledge of the PET/MR findings, subsequently separately reviewed the PET/CT images for TNM staging. The generalized kappa statistic was used to determine intermodality agreement between PET/CT and PET/MR for TNM staging. Results ROC curve analysis showed that PET/MR had a specificity of 92.3 % and a sensitivity of 97.3 % in the determination of resectability with an AUC of 0.95. Interobserver agreement in PET/MR reading ranged from substantial to perfect between the two readers (Cohen’s kappa 0.646 – 1) for T stage, N stage and M stage. Intermodality agreement between PET/CT and PET/MR ranged from substantial to almost perfect for T stage, N stage and M stage (Cohen’s kappa 0.627 – 0.823). Conclusion In lung cancer patients PET/MR appears to be a robust technique for preoperative staging

Multiparametric MRI for assessment of early response to neoadjuvant sunitinib in renal cell carcinoma

Purpose To detect early response to sunitinib treatment in metastatic clear cell renal cancer (mRCC) using multiparametric MRI. Method Participants with mRCC undergoing pre-surgical sunitinib therapy in the prospective NeoSun clinical trial (EudraCtNo: 2005-004502-82) were imaged before starting treatment, and after 12 days of sunitinib therapy using morphological MRI sequences, advanced diffusion-weighted imaging, measurements of R2* (related to hypoxia) and dynamic contrast-enhanced imaging. Following nephrectomy, participants continued treatment and were followed-up with contrast-enhanced CT. Changes in imaging parameters before and after sunitinib were assessed with the non-parametric Wilcoxon signed-rank test and the log-rank test was used to assess effects on survival. Results 12 participants fulfilled the inclusion criteria. After 12 days, the solid and necrotic tumor volumes decreased by 28% and 17%, respectively (p = 0.04). However, tumor-volume reduction did not correlate with progression-free or overall survival (PFS/OS). Sunitinib therapy resulted in a reduction in median solid tumor diffusivity D from 1298x10-6 to 1200x10-6mm2/ s (p = 0.03); a larger decrease was associated with a better RECIST response (p = 0.02) and longer PFS (p = 0.03) on the log-rank test. An increase in R2* from 19 to 28s-1 (p = 0.001) was observed, paralleled by a decrease in Ktrans from 0.415 to 0.305min-1 (p = 0.01) and a decrease in perfusion fraction from 0.34 to 0.19 (p<0.001). Conclusions Physiological imaging confirmed efficacy of the anti-angiogenic agent 12 days after initiating therapy and demonstrated response to treatment. The change in diffusivity shortly after starting pre-surgical sunitinib correlated to PFS in mRCC undergoing nephrectomy, however, no parameter predicted OS

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

open20siAcknowledgements: EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine, Frank Edward Elmore Fund and the Medical Research Council’s Doctoral Training Partnership [award reference: 1966157]. JMT is supported by a Wellcome Trust Clinical Research Career Development Fellowship [211100/Z/18/Z], the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the British Heart Foundation Cambridge Centre of Research Excellence. NRE was supported by a Research Training Fellowship from The Dunhill Medical Trust [RTF44/0114]. MMC was supported by fellowships from the Royal College of Surgeons of England, and the British Heart Foundation [BHF; FS/16/29/31957]. HP is undertaking a PhD with a BHF CRE studentship. FJG is an NIHR Senior Investigator. LR and ES were supported by The Mark Foundation for Cancer Research and Cancer Research UK (CRUK) Cambridge Centre [C9685/A25177]. MR is supported by AstraZeneca Oncology R&D. ES receives additional support provided by the NIHR Cambridge Biomedical Research Centre. FAG receives funding from CRUK. EAW receives support from the NIHR CRN. CBS acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, the Philip Leverhulme Prize, the EPSRC grants EP/S026045/1 and EP/T003553/1, the EPSRC Centre Nr. EP/N014588/1, the Wellcome Innovator Award RG98755, European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 NoMADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. JHFR is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, HEFCE, the Wellcome Trust and the EPSRC grant [EP/N014588/1] for the University of Cambridge Centre for Mathematical Imaging in Healthcare. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.noneLe E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F.Le E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F