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Dual energy CT (DECT) is a new technique that allows differentiation of materials and tissues based on CT density values derived from two synchronous CT acquisitions at different tube potentials. With the introduction of a new dual source CT system, this technique can now be used routinely in abdominal imaging. Potential clinical applications include evaluation of renal masses, liver lesions, urinary calculi, small bowel, pancreas, and adrenal glands. In CT angiography of abdominal aortic aneurysms, dual energy CT techniques can be used to remove bones from the datasets, and virtual unenhanced images allow differentiation of contrast agent from calcifying thrombus in patients with endovascular stents. This review describes potential applications, practical guidelines, and limitations of dual energy CT in the abdomen.

Objectives To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning. Methods In this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++. Results The training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue. Conclusions In this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader. Key Points • Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans. • Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts. • Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6–9 mm size.

Bone is a major site of haematogenous tumour cell spread in renal cell carcinoma (RCC), and most patients with RCC will develop painful and functionally disabling bone metastases at advanced disease stages. The prognosis of these patients is generally poor and the treatment is, therefore, aimed at palliation. However, RCC-associated bone metastases can be curable in select patients. Current data support a multimodal management strategy that includes wide resection of lesions, radiotherapy, systemic therapy, and other local treatment options, which can improve quality of life and survival. Nevertheless, the optimal approach for metastatic bone disease in RCC has not yet been defined and practical recommendations are rare. To improve the management and outcomes of patients with RCC and bone metastases, the International Kidney Cancer Coalition and the interdisciplinary working group on renal tumours of the German Cancer Society convened a meeting of experts with a global perspective to perform an unstructured review and elaborate on current treatment strategies on the basis of published data and expertise. The panel formulated recommendations for the diagnosis and treatment of patients with RCC and metastasis to the bone. Furthermore, the experts summarized current challenges and unmet patient needs that should be addressed in the future.

Objectives Previous meta-analyses on CT-colonography included both average and high risk individuals, which may overestimate the diagnostic value in screening. A meta-analysis was performed to obtain the value of CT-colonography for screening. Methods A search was performed using PubMed, Embase and Cochrane. Article selection and critical appraisal was done by two reviewers. Inclusion criteria: prospective, randomized trials or cohort studies comparing CT-colonography with colonoscopy (≥50 participants), ≥95% average risk participants ≥50 years. Study characteristics and 2 × 2 contingency Tables were recorded. Sensitivity and specificity estimates were calculated per patient and per polyp (≥6 mm, ≥10 mm), using univariate and bivariate analyses. Results Five of 1,021 studies identified were included, including 4,086 participants (<1% high risk). I 2 -values showed substantial heterogeneity, especially for 6–9 mm polyps and adenomas: 68.1% vs. 78.6% (sensitivity per patient). Estimated sensitivities for patients with polyps or adenomas ≥ 6 mm were 75.9% and 82.9%, corresponding specificities 94.6% and 91.4%. Estimated sensitivities for patients with polyps or adenomas ≥ 10 mm were 83.3% and 87.9%, corresponding specificities 98.7% and 97.6%. Estimated sensitivities per polyp for advanced adenomas ≥ 6 mm and ≥ 10 mm were 83.9% and 83.8%. Conclusion Compared to colonoscopy, CT-colonography has a high sensitivity for adenomas ≥ 10 mm. For (advanced) adenomas ≥ 6 mm sensitivity is somewhat lower.

PurposeThe prediction of an infiltration of the mesorectal fascia (MRF) and malignant lymph nodes is essential for treatment planning and prognosis of patients with rectal cancer. The aim of this study was to assess the additional diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the detection of a malignant involvement of the MRF and of mesorectal lymph nodes in patients with locally advanced rectal cancer.MethodsIn this prospective study, 22 patients with locally advanced rectal cancer were examined with 1.5-T MRI between September 2012 and April 2015. Histopathological assessment of tumor size, tumor infiltration to the MRF, and malignant involvement of locoregional lymph nodes served as standard of reference. Sensitivity and specificity of detecting MRF infiltration and malignant nodes (nodal cut-off size [NCO] ≥ 5 and ≥ 10 mm, respectively) was determined by conventional MRI (cMRI; precontrast and postcontrast T1-weighted, T2-weighted, and diffusion-weighted images) and by additional semi-quantitative DCE-MRI maps (cMRI+DCE-MRI).ResultsCompared to cMRI, additional semi-quantitative DCE-MRI maps significantly increased sensitivity (86 vs. 71% [NCO ≥ 5 mm]/29% [NCO ≥ 10 mm]) and specificity (90 vs. 70% [NCO ≥ 5 mm]) of detecting malignant lymph nodes (p < 0.05). Moreover, DCE-MRI significantly augmented specificity (91 vs. 82%) of discovering a MRF infiltration (p < 0.05), while there was no change in sensitivity (83%; p > 0.05).ConclusionDCE-MRI considerably increases both sensitivity and specificity for the detection of small mesorectal lymph node metastases (≥ 5 mm but < 10 mm) and sufficiently improves specificity of a suspected MRF infiltration in patients with locally advanced rectal cancer.

Antiangiogenic therapies interacting with tumor-specific pathways have been established for targeted therapy of renal cell carcinoma (RCC). However, evaluation of tumor response based on morphologic tumor diameter measurements has limitations, as tumor shrinkage may lag behind pathophysiological response. Functional imaging techniques such as dynamic contrast-enhanced (DCE) ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI), unenhanced diffusion-weighted MRI (DW-MRI), and also metabolic imaging with positron emission tomography (PET) have the ability to assess physiological parameters and to predict and monitor therapy response. Assessment of changes in vascularity, cellularity, oxygenation, and glucose uptake with functional imaging during targeted therapy may correlate with progression-free survival and can predict tumor response or progression. In this review, we explore the potential of functional imaging techniques for assessing the effects of targeted therapy of RCC and as well review the reproducibility and limitations.

Background We analysed in vitro the appearance of commonly used ureteral stents with dual-energy computed tomography (DECT) and we used these characteristics to optimize the differentiation between stents and adjacent stone. Methods We analysed in vitro a selection of 36 different stents from 7 manufacturers. They were placed in a self-build phantom model and measured using the SOMATOM® Force Dual Source CT-Scanner (Siemens, Forchheim, Germany). Each sample was scanned at various tube potentials of 80 and 150 peak kilovoltage (kVp), 90 and 150 kVp and 100 and 150 kVp. The syngo Post-Processing Suite software program (Siemens, Forchheim, Germany) was used for differentiation based on a 3–material decomposition algorithm (UA, calcium, urine) according to our standard stone protocol. Results Stents composed of polyurethane appeared blue and silicon-based stents were red on the image. The determined appearances were constant for various peak kilovoltage (kVp) values. The coloured stent-stone-contrast displayed on DECT improves monitoring, especially of small calculi adjacent to indwelling ureteral stents. Conclusion Both urinary calculi and ureteral stents can be accurately differentiated by a distinct appearance on DECT. For the management of urolithiasis patients can be monitored more easily and accurately using DECT if the stent shows a different colour than the adjacent stone.

Our purpose was to assess the effect of computer-aided detection (CAD) on lesion detection as a second reader in computed tomographic colonography, and to compare the influence of CAD on the performance of readers with different levels of expertise. Fifty-two CT colonography patient data-sets (37 patients: 55 endoscopically confirmed polyps > or =0.5 cm, seven cancers; 15 patients: no abnormalities) were retrospectively reviewed by four radiologists (two expert, two nonexpert). After primary data evaluation, a second reading augmented with findings of CAD (polyp-enhanced view, Siemens) was performed. Sensitivities and reading time were calculated for each reader without CAD and supported by CAD findings. The sensitivity of expert readers was 91% each, and of nonexpert readers, 76% and 75%, respectively, for polyp detection. CAD increased the sensitivity of expert readers to 96% (P = 0.25) and 93% (P = 1), and that of nonexpert readers to 91% (P = 0.008) and 95% (P = 0.001), respectively. All four readers diagnosed 100% of cancers, but CAD alone only 43%. CAD increased reading time by 2.1 min (mean). CAD as a second reader significantly improves sensitivity for polyp detection in a high disease prevalence population for nonexpert readers. CAD causes a modest increase in reading time. CAD is of limited value in the detection of cancer.

ObjectivesVarious imaging methods have been evaluated regarding non-invasive differentiation of renal cell carcinoma (RCC) subtypes. Dual-energy computed tomography (DECT) allows iodine concentration (IC) analysis as a correlate of tissue perfusion. Microvascular density (MVD) in histopathology specimens is evaluated to determine intratumoral vascularization. The objective of this study was to assess the potential of IC and MVD regarding the differentiation between papillary and clear cell RCC and between well- and dedifferentiated tumors. Further, we aimed to investigate a possible correlation between these parameters.MethodsDECT imaging series of 53 patients with clear cell RCC (ccRCC) and 15 with papillary RCC (pRCC) were analyzed regarding IC. Histology samples were stained using CD31/CD34 monoclonal antibodies; MVD was evaluated digitally. Statistical analysis included performance of Mann-Whitney U test, ROC analysis, and Spearman rank correlation.ResultsAnalysis of IC demonstrated significant differences between ccRCC and pRCC (p < 0.001). A cutoff value of ≤ 3.1 mg/ml at IC analysis allowed identification of pRCC with an accuracy of 86.8%. Within the ccRCC subgroup, G1/G2 tumors could significantly be differentiated from G3/G4 carcinomas (p = 0.045). A significant positive correlation between IC and MVD could be determined for the entire RCC cohort and the ccRCC subgroup. Limitations include the small percentage of pRCCs.ConclusionsIC analysis is a useful method to differentiate pRCC from ccRCC. The significant positive correlation between IC and MVD indicates valid representation of tumor perfusion by DECT.Key Points• Analysis of iodine concentration using DECT imaging could reliably distinguish papillary from clear cell subtypes of renal cell cancer (RCC).• A cutoff value of 3.1 mg/ml allowed a distinction between papillary and clear cell RCCs with an accuracy of 86.8%.• The positive correlation with microvascular density in tumor specimens indicates correct display of perfusion by iodine concentration analysis.

Objectives To assess the power of multi-detector row computerized tomography (MDCT) in daily routine as a basic staging procedure for the decision on local treatment of patients with bladder cancer. Patients and methods We retrospectively analysed 276 patients who had undergone radical cystectomy between 2004 and 2008 and correlated the MDCT findings with pathological findings, number of removed lymph nodes and type of urinary diversion. Results Accuracy of MDCT in predicting pathological tumour stage was 49% (kappa coefficient, 0.23; P  < 0.001). Overstaging occurred in 23.4%, and understaging occurred in 24.7%. Accuracy in predicting lymph node metastases was 54% (kappa coefficient, 0.04; P  = 0.297). Overstaging and understaging occurred in 8.3 and 29.4%, respectively. Significantly more ileal conduits were performed in patients with high postoperative pathological tumour stages ( P  = 0.04) and positive lymph nodes ( P  = 0.013). In contrast, there was no correlation between preoperative CT tumour/nodal stage and the number of removed lymph nodes ( P  = 0.44 and P  = 0.732, respectively), and between preoperative tumour stage and type of urinary diversion ( P  = 0.126). Conclusions MDCT as a preoperative staging procedure has a low accuracy in predicting the correct tumour and nodal stage, and therefore, it has little impact on decision-making for local treatment of muscle-invasive bladder cancer during radical cystectomy.