European Radiology

Correction to: Diagnostic efficacy and safety of ultrasound-guided kidney transplant biopsy using cortex-only view: a retrospective single-center study The original version of this article, published on 17 December 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Jaeseung Shin was presented incorrectly. The corrected author list is given above.

Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.

Correction to: Is liver lesion characterisation by simplified IVIM DWI also feasible at 3.0 T? The original version of this article, published on 08 April 2019, unfortunately contained a mistake. The following correction has therefore been made in the original: The caption of Fig. 2 is wrong. The corrected version is given below.

Pericolic or paracolic? The right word in the right place for acute diverticulitis Key Point • The term "pericolic" is wrongly used to describe an abscess adjacent to the colon in patients with acute diverticulitis. We explain why the proper term is the word "paracolic."

Amide proton transfer–weighted MRI can detect tissue acidosis and monitor recovery in a transient middle cerebral artery occlusion model compared with a permanent occlusion model in rats Abstract Objectives To assess whether increases in amide proton transfer (APT)–weighted signal reflect the effects of tissue recovery from acidosis using transient rat middle cerebral artery occlusion (MCAO) models, compared to permanent occlusion models. Materials and methods Twenty-four rats with MCAO (17 transient and seven permanent occlusions) were prepared. APT-weighted signal (APTw), apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and MR spectroscopy were evaluated at three stages in each group (occlusion, reperfusion/1 h post-occlusion, and 3 h post-reperfusion/4 h post-occlusion). Deficit areas showing 30% reduction to the contralateral side were measured. Temporal changes were compared with repeated measures of analysis of variance. Relationship between APTw and lactate concentration was calculated. Results Both APTw and CBF values increased and APTw deficit area reduced at reperfusion (largest p = .002) in transient occlusion models, but this was not demonstrated in permanent occlusion. No significant temporal change was demonstrated with ADC at reperfusion. APTw deficit area was between ADC and CBF deficit areas in transient occlusion model. APTw correlated with lactate concentration at occlusion (r = − 0.49, p = .04) and reperfusion (r = − 0.32, p = .02). Conclusions APTw values increased after reperfusion and correlated with lactate content, which suggests that APT-weighted MRI could become a useful imaging technique to reflect tissue acidosis and its reversal. Key Points • APT-weighted signal increases in the tissue reperfusion, while remains stable in the permanent occlusion. • APTw deficit area was between ADC and CBF deficit areas in transient occlusion model, possibly demonstrating metabolic penumbra. • APTw correlated with lactate concentration during ischemia and reperfusion, indicating tissue acidosis.

Non-measurable infiltrative HCC: is post-contrast attenuation on CT a sign of tumor response? Abstract Objectives To evaluate the value of CT attenuation to assess the response to sorafenib in infiltrative/endovascular non-measurable advanced hepatocellular carcinoma (HCC). Methods From 2007 to 2014, patients with infiltrative HCC ± tumor-in-vein (TIV) were retrospectively included. Attenuation of tumors and TIV were measured at baseline and follow-up on arterial and portal venous phase CT by two independent radiologists. Attenuation changes (overall and as per Choi criteria) and Child-Pugh score were correlated to overall survival. Results Forty patients were included (38 men, 95%). Attenuation of both the tumors and TIV was significantly lower in follow-up CT than on baseline CT (p = 0.002 (arterial), and p = 0.001 (portal) for tumor, and p = 0.004 (arterial) and p < 0.001 (porta) for TIV). Median attenuation of TIV was significantly lower than that of the tumor in follow-up images (p = 0.010). Median OS for the entire cohort was 4 ± 1 months (95% CI: 2.1–5.9), with estimated OS rates at 6, 12, and 24 months of 43%, 29 and 12%, respectively. Baseline and follow-up CT attenuation in tumors and TVI were not correlated with survival. Survival was not significantly increased in patients with Choi criteria >15% CT HU decrease in the tumor and/or TIV during follow-up. Only Child-Pugh A (HR 4.9 (95%CI 2.3–10.7), p < 0.001) was identified as an independent factor of improved survival on multivariate analysis. Conclusion Despite significant changes under sorafenib, tumor attenuation of infiltrative/endovascular non-measurable HCC may be of limited value to assess survival in this subgroup of patients with very poor prognosis. Key Points • Attenuation of both tumors and tumor-in-vein decreases after sorafenib. • Attenuation decrease is more marked in the tumor-in-vein than in the tumor. • Attenuation decrease is not associated with longer overall survival.

Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features Abstract Objective To differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model. Methods Sixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into training and validation data sets (about 2:1). Quantitative radiomic features of the tumours were extracted from contrast-enhanced MR images. A subset of features was selected by feature stability and Boruta algorithm. The selected features were used to build a decision tree model. Predictive accuracy, sensitivity and specificity were used to assess model performance. The classification outcome of the model was combined with tumour location, age and gender features, and multivariable logistic regression analysis and permutation test using the entire data set were performed to further evaluate the decision tree model. Results A total of 271 radiomic features were successfully extracted for each tumour. Twelve features were selected as input variables to build the decision tree model. Two features S(1, -1) Entropy and S(2, -2) SumAverg were finally included in the model. The model showed an accuracy, sensitivity and specificity of 0.87, 0.90 and 0.83 for the training data set and 0.86, 0.80 and 0.91 for the validation data set. The classification outcome of the model related to the actual tumour types and did not rely on the other three features (p < 0.001). Conclusions A decision tree model with two features derived from the contrast-enhanced MR images performed well in differentiating PA from GBM. Key Points • MRI findings of PA and GBM are sometimes very similar. • Radiomics provides much more quantitative information about tumours. • Radiomic features can help to distinguish PA from GBM.

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis Abstract Objectives To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack. Methods We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics. Results The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05). Conclusions The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions. Key Points • The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking. • The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis. • As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective Abstract Objectives To develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene—the von Hippel-Lindau (VHL) gene mutation. Methods In this retrospective two-center study, two radiomics models were built using random forest from a training cohort (170 patients), where one model was built with all-relevant features and the other with minimum redundancy maximum relevance (mRMR) features. A model combining all-relevant features and clinical factors (sex, age) was also built. The radiogenomics association between selected features and VHL mutation was investigated by Wilcoxon rank-sum test. All models were tested on an independent validation cohort (85 patients) with ROC curves analysis. Results The model with eight all-relevant features from corticomedullary phase CT achieved an AUC of 0.949 and an accuracy of 92.9% in the validation cohort, which significantly outperformed the model with eight mRMR features (seven from nephrographic phase and one from corticomedullary phase) with an AUC of 0.851 and an accuracy of 81.2%. Combining age and sex did not benefit the performance. Five out of eight all-relevant features were significantly associated with VHL mutation, while all eight mRMR features were significantly associated with VHL mutation (false discovery rate-adjusted p < 0.05). Conclusions All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC. Most subtype-discriminative imaging features were found to be significantly associated with VHL mutation, which may underlie the molecular basis of the radiomics features. Key Points • All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC with high accuracy. • Most RCC-subtype-discriminative CT features were associated with the key RCC-driven gene—the VHL gene mutation. • Radiomics model can be more accurate and interpretable when the imaging features could reflect underlying molecular basis of RCC.

Initial exploration of coronary stent image subtraction using dual-layer spectral CT Abstract Objectives This study aimed to investigate the feasibility of coronary stent image subtraction using spectral tools derived from dual-layer spectral computed tomography (CT). Methods Forty-three patients (65 stents) who underwent coronary CT angiography using dual-layer spectral CT were included. Conventional, 50-keV (kilo electron-volt), 100-keV, and virtual non-contrast (VNC) images were reconstructed from the same cardiac phase. Stents were subtracted on VNC images from conventional (convsub), 100-keV (100-keVsub), and 50-keV (50-keVsub) images. The in-stent lumen diameters were measured on subtraction, conventional, and 100-keV images. Subjective evaluation of reader confidence and subtractive quality was evaluated. Friedman tests were performed to compare in-stent lumen diameters and subjective evaluation among different images. Correlation between stent diameter and subjective evaluation was expressed as Spearman's rank correlation coefficient (rs). The diagnostic accuracy was assessed according to invasive coronary angiography (ICA) performed in 11 patients (20 stents). Results In-stent lumen diameters were significantly larger on subtraction images than those on conventional and 100-keV images (p < 0.05). Higher reader confidence was found on 100-keV, convsub, 100-keVsub, and 50-keVsub images compared with conventional images (p < 0.05). Subtractive quality of 100-keVsub images was better than that of convsub images (p = 0.037). A moderate-to-strong correlation between stent diameter and subjective evaluation was found (rs = 0.527~0.790, p < 0.05). Higher specificity, positive predictive value, and negative predictive value of subtraction images were shown by ICA results. Conclusions Subtraction images derived from dual-layer spectral CT enhanced in-stent lumen visibility and could potentially improve diagnostic performance for evaluating coronary stents. Key Points • Dual-layer spectral CT enabled good subtractive quality of coronary stents without misregistration artifacts. • Subtraction images could improve in-stent lumen visibility. • Reader confidence and diagnostic performance were enhanced with subtraction images.

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
Crete.Greece.72100
2841026182

6948891480

alsfakia@gmail.com