Dual-energy CT in acute cholecystitis- features predicting culture-positive bile and outcome.

PURPOSE: Low mono-energetic CT has been shown to improve visualization of acute abdominal inflammatory processes. We aimed to determine its utility in patients with acute cholecystitis and potential added value in clinical decision making. METHODS: Sixty-seven consecutive patients with radiological signs of cholecystitis on contrast-enhanced dual-layer CT imaging were retrospectively identified over a four-year period (2/17-8/21). A ranked Likert scale was created for imaging findings present in acute cholecystitis, including gallbladder mucosal integrity and enhancement and pericholecystic liver parenchymal enhancement. These rankings were correlated with laboratory data, followed by sensitivity, specificity, and odds-ratios calculations. RESULTS: Mucosal integrity and pericholecystic liver enhancement were better seen on low-energetic images by unanimous consensus. Presence of pericholecystic liver enhancement and poorer mucosal wall integrity correlated with positive bile cultures (sensitivity: 93.8 % and 96.9 %, specificity: 37.5 and 50.0 %; odds-ratio: 9.0[1.1-68.1 95 %CI] and 31.0 [2.7-350.7 95 %CI], p = 0.017 and p ≤ 0.001) in patients undergoing cholecystostomy (n = 40/67). Moreover, binary regression modeling showed that the strongest predictor variable for bile culture positivity was the score for pericholecystic liver enhancement (Exp(B) = 0.6, P = 0.022). By contrast, other laboratory markers and other imaging findings (such as GB wall thickness) showed lower sensitivities (76-82 %), specificities (16-21 %) and odds ratios (0.2-4.4) for the prediction of infected bile. CONCLUSIONS: Pericholecystic liver enhancement and gallbladder wall integrity are better visualized on low-DECT images. These findings also potentially predict bile culture positivity in patients with cholecystitis, which may influence clinical management including the need for intervention.

Evaluation of the Pulmonary Arteries on CTPA With Dual Energy CT: Objective Analysis and Subjective Preferences in a Multireader Study.

PURPOSE: To perform qualitative and quantitative evaluation of low-monoenergetic images (50 KeV) compared with conventional images (120 kVp) in pulmonary embolism (PE) studies and to determine the extent and clinical relevance of these differences as well as radiologists' preferences. MATERIALS AND METHODS: One hundred fifty CT examinations for PE detection conducted on a single-source dual-energy CT were retrospectively evaluated. Attenuation, contrast-to-noise-ratio, and signal-to-noise-ratio were obtained in a total of 8 individual pulmonary arteries on each exam-including both central (450/1200=37.5%) and peripheral (750/1200=62.5%) locations. Results were compared between the conventional and low-monoenergetic images. For quality assessment, 41 images containing PE were presented side-by-side as pairs of slices in both conventional and monoenergetic modes and evaluated for ease in embolus detection by 9 radiologists: cardiothoracic specialists (3), noncardiothoracic specialists (3), and residents (3). Paired samples t tests, a-parametric Wilcoxon test, McNemar test, and kappa statistics were performed. RESULTS: Monoenergetic images had an overall statistically significant increased average ratio of 2.09 to 2.26 (P<0.05) for each measured vessel attenuation, with an increase in signal-to-noise ratio (23.82±9.29 vs. 11.39±3.2) and contrast-to-noise ratio (17.17±6.7 vs 7.27±2.52) (P<0.05). Moreover, 10/150 (6%) of central pulmonary artery measurements considered suboptimal on conventional mode were considered diagnostic on the monoenergetic images (181±14.6 vs. 387.7±72.4 HU respectively, P<0.05). In the subjective evaluation, noncardiothoracic radiologists showed a preference towards low-monoenergetic images, whereas cardiothoracic radiologists did not (74.4% vs. 57.7%, respectively, P<0.05). CONCLUSIONS: The SNR and CNR increase on monoenergetic images may have clinical significance particularly in the setting of sub-optimal PE studies. Noncardiothoracic radiologists and residents prefer low monoenergetic images.

Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up.

PURPOSE: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans. METHODS: The inputs are the current and prior CT scans and their organ and lesion segmentations. Classification of lesion changes is formalized as bipartite graph matching where lesion pairings are computed by adaptive overlap-based lesion matching. Six types of lesion changes are computed by connected components analysis. The method was evaluated on 208 pairs of lung and liver CT scans from 57 patients with 4600 lesions, 1713 lesion matchings and 2887 lesion changes. Ground-truth lesion segmentations, lesion matchings and lesion changes were created by an expert radiologist. RESULTS: Our method yields a lesion matching rate accuracy of 99.7% (394/395) and 95.0% (1252/1318) for the lung and liver datasets. Precision and recall are > 0.99 and 0.94 and 0.95 (respectively) for the detection of lesion changes. The analysis of lesion changes helped the radiologist detect 48 missed lesions and 8 spurious lesions in the input ground-truth lesion datasets. CONCLUSION: The classification of lesion classification provides the clinician with a readily accessible and intuitive identification and classification of the lesion changes and their patterns in support of clinical decision making. Comprehensive automatic computer-aided lesion matching and analysis of lesion changes may improve quantitative follow-up and evaluation of disease status, assessment of treatment efficacy and response to therapy.

MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI.

PURPOSE: Radiological detection and follow-up of pancreatic cysts in multisequence MRI studies are required to assess the likelihood of their malignancy and to determine their treatment. The evaluation requires expertise and has not been automated. This paper presents MC3DU-Net, a novel multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI studies consisting of coronal MRCP and axial TSE MRI sequences. METHODS: MC3DU-Net leverages the information in both sequences by computing a pancreas Region of Interest (ROI) segmentation in the TSE MRI scan, transferring it to MRCP scan, and then detecting and segmenting the cysts in the ROI of the MRCP scan. Both the voxel-level ROI of the pancreas and the segmentation of the cysts are performed with 3D U-Nets trained with Hard Negative Patch Mining, a new technique for class imbalance correction and for the reduction in false positives. RESULTS: MC3DU-Net was evaluated on a dataset of 158 MRI patient studies with a training/validation/testing split of 118/17/23. Ground truth segmentations of a total of 840 cysts were manually obtained by expert clinicians. MC3DU-Net achieves a mean recall of 0.80 ± 0.19, a mean precision of 0.75 ± 0.26, a mean Dice score of 0.80 ± 0.19 and a mean ASSD of 0.60 ± 0.53 for pancreatic cysts of diameter > 5 mm, which is the clinically relevant endpoint. CONCLUSION: MC3DU-Net is the first fully automatic method for detection and segmentation of pancreatic cysts in MRI. Automatic detection and segmentation of pancreatic cysts in MRI can be performed accurately and reliably. It may provide a method for precise disease evaluation and may serve as a second expert reader.

Optimal CT windowing on low-monoenergetic images using a simplex algorithm-based approach for abdominal inflammatory processes.

BACKGROUND: OBJECTIVES: To determine optimal window settings for conspicuity of abdominal inflammatory processes on 50 keV low-monoenergetic images derived from dual-energy spectral CT (DECT). METHODS: A retrospective study of 30 patients with clinically proven pancreatitis (15/30) or pyelonephritis (15/30) with inflammatory lesions visible on DECT scans were selected to serve as reference populations. 50 keV low-monoenergetic images in the portal venous phase were iteratively evaluated by 6 abdominal radiologists in twenty-one different windows (7-350HU center; 120-580HU width), selected using a simplex optimization algorithm. Each reader graded the conspicuity of the parenchymal hypodense lesions and image background quality. Three-dimensional contour maps expressing the relationship between overall reader grade and window center and width were constructed and used to find the ideal window for inflammatory pancreatic and renal processes and the image background quality. Finally, 15 appendicitis cases were reviewed on optimal pancreas and kidney windows and the manufacturer recommended conventional abdominal window settings for conventional imaging. RESULTS: Convergence to optimal windowing was achieved based upon a total of 3,780 reads (21 window settings × 6 readers × 15 cases for pancreas and kidney). Highest conspicuity grade (>4.5 ± 0.0) for pancreas inflammatory lesions was seen at 116HU/430HU, whereas hypodense pyelonephritis had highest conspicuity at 290HU/570HU. This rendered an ideal "compromise" window (>4 ± 0.2) of 150HU/450HU which differed substantially from conventional manufacturer recommended settings of 50HU/380HU (2.1 ± 1.0, p = 0.00001). Appendix mucosal enhancement was best visualized at manufacturer settings. CONCLUSIONS: Optimal visualization of inflammatory processes in abdominal organs on 50 keV low-monoenergetic images may require tailored refinement of window settings.

Real-time influence of intracellular acidification and Na+ /H+ exchanger inhibition on in-cell pyruvate metabolism in the perfused mouse heart: A 31 P-NMR and hyperpolarized 13 C-NMR study.

Disruption of acid-base balance is linked to various diseases and conditions. In the heart, intracellular acidification is associated with heart failure, maladaptive cardiac hypertrophy, and myocardial ischemia. Previously, we have reported that the ratio of the in-cell lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) activities is correlated with cardiac pH. To further characterize the basis for this correlation, these in-cell activities were investigated under induced intracellular acidification without and with Na+ /H+ exchanger (NHE1) inhibition by zoniporide. Male mouse hearts (n = 30) were isolated and perfused retrogradely. Intracellular acidification was performed in two ways: (1) with the NH4 Cl prepulse methodology; and (2) by combining the NH4 Cl prepulse with zoniporide. 31 P NMR spectroscopy was used to determine the intracellular cardiac pH and to quantify the adenosine triphosphate and phosphocreatine content. Hyperpolarized [1-13 C]pyruvate was obtained using dissolution dynamic nuclear polarization. 13 C NMR spectroscopy was used to monitor hyperpolarized [1-13 C]pyruvate metabolism and determine enzyme activities in real time at a temporal resolution of a few seconds using the product-selective saturating excitation approach. The intracellular acidification induced by the NH4 Cl prepulse led to reduced LDH and PDH activities (-16% and -39%, respectively). This finding is in line with previous evidence of reduced myocardial contraction and therefore reduced metabolic activity upon intracellular acidification. Concomitantly, the LDH/PDH activity ratio increased with the reduction in pH, as previously reported. Combining the NH4 Cl prepulse with zoniporide led to a greater reduction in LDH activity (-29%) and to increased PDH activity (+40%). These changes resulted in a surprising decrease in the LDH/PDH ratio, as opposed to previous predictions. Zoniporide alone (without intracellular acidification) did not change these enzyme activities. A possible explanation for the enzymatic changes observed during the combination of the NH4 Cl prepulse and NHE1 inhibition may be related to mitochondrial NHE1 inhibition, which likely negates the mitochondrial matrix acidification. This effect, combined with the increased acidity in the cytosol, would result in an enhanced H+ gradient across the mitochondrial membrane and a temporarily higher pyruvate transport into the mitochondria, thereby increasing the PDH activity at the expense of the cytosolic LDH activity. These findings demonstrate the complexity of in-cell cardiac metabolism and its dependence on intracellular acidification. This study demonstrates the capabilities and limitations of hyperpolarized [1-13 C]pyruvate in the characterization of intracellular acidification as regards cardiac pathologies.

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

OBJECTIVES: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis. METHODS: A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.1, std = 11.9, range 1-49) were analyzed. Two radiologists performed readings of all pairs; conventional with RECIST 1.1 and with computer-aided assessment. For computer-aided reading, we used a novel simultaneous multi-channel 3D R2U-Net classifier trained and validated on other scans. The reference was established by having an expert radiologist validate the computed lesion detection and segmentation. The results were then verified and modified as needed by another independent radiologist. The primary outcome measure was the disease status assessment with the conventional and the computer-aided readings with respect to the reference. RESULTS: For conventional and computer-aided reading, there was a difference in disease status classification in 40 out of 86 (46.51%) and 10 out of 86 (27.9%) CT studies with respect to the reference, respectively. Computer-aided reading improved conventional reading in 30 CT studies by 34.5% for two readers (23.2% and 46.51%) with respect to the reference standard. The main reason for the difference between the two readings was lesion volume differences (p = 0.01). CONCLUSIONS: AI-based computer-aided analysis of liver metastases may improve the accuracy of the evaluation of neoplastic liver disease status. CLINICAL RELEVANCE STATEMENT: AI may aid radiologists to improve the accuracy of evaluating changes over time in metastasis of the liver. KEY POINTS: • Classification of liver metastasis changes improved significantly in one-third of the cases with an automatically generated comprehensive lesion and lesion changes report. • Simultaneous deep learning changes detection and volumetric assessment may improve the evaluation of liver metastases temporal changes potentially improving disease management.

The different clonal origins of metachronous and synchronous metastases.

BACKGROUND: Metastases are the leading cause of mortality in cancer patients. Linear and parallel are the two prominent models of metastatic progression. Metastases can be detected synchronously along with the primary tumor or metachronously, following treatment of localized disease. The aim of the study was to determine whether synchronous metastases (SM) and metachronous metastases (MM) differ only in lead-time or stem from different biological processes. MATERIALS AND METHODS: We retrospectively studied the chest CTs of 791 patients inflicted by eleven malignancy types that were treated in our institution in the years 2010-2020. Patient's population included 396 with SM and 395 with MM. The diameter of 15,427 lung metastases was measured. Clonal origin was deduced from the linear/parallel ratio (LPR)-a computerized analysis of metastases diameters. LPR of 1 suggests pure linear dissemination and - 1 pure parallel. RESULTS: Patients with MM were significantly older (average of 62.9 vs 60.7 years, p = 0.02), and higher percentage of them were males (58.7% vs 51.1%, p = 0.03). Median overall survival of patients with MM and SM was remarkably similar (23 months and 26 months respectively, p = 0.774) when calculated from the time of metastases diagnosis. Parallel dissemination (LPR ≤ 0) was found in 35.4% of patients with MM compared to only 19.8% of the patients with SM (p < 0.00001). CONCLUSION: Patients with SM and MM differ in demography and in clonal origin. Different therapeutic approaches may be considered in these two conditions.

Investigating Cardiac Metabolism in the Isolated Perfused Mouse Heart with Hyperpolarized [1-13C]Pyruvate and 13C/31P NMR Spectroscopy.

Metabolism is the basis of important processes in cellular life. Characterizing how metabolic networks function in living tissues provides crucial information for understanding the mechanism of diseases and designing treatments. In this work, we describe procedures and methodologies for studying in-cell metabolic activity in a retrogradely perfused mouse heart in real-time. The heart was isolated in situ, in conjunction with cardiac arrest to minimize the myocardial ischemia and was perfused inside a nuclear magnetic resonance (NMR) spectrometer. While in the spectrometer and under continuous perfusion, hyperpolarized [1-13C]pyruvate was administered to the heart, and the subsequent hyperpolarized [1-13C]lactate and [13C]bicarbonate production rates served to determine, in real-time, the rates of lactate dehydrogenase and pyruvate dehydrogenase production. This metabolic activity of hyperpolarized [1-13C]pyruvate was quantified with NMR spectroscopy in a model free-manner using the product selective saturating-excitations acquisition approach. 31P spectroscopy was applied in between the hyperpolarized acquisitions to monitor the cardiac energetics and pH. This system is uniquely useful for studying metabolic activity in the healthy and diseased mouse heart.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

OBJECTIVES: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. METHODS: The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. RESULTS: The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. CONCLUSIONS: The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. CLINICAL RELEVANCE: Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. KEY POINTS: • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.

Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest-abdominal-pelvis CT as a case study.

OBJECTIVES: We assessed the appropriateness of chest-abdominal-pelvis (CAP) CT scan use in the Emergency Department (ED), based on expert physicians and the ESR iGuide, a clinical decision support system (CDSS). METHODS: A retrospective cross-study was conducted. We included 100 cases of CAP-CT scans ordered at the ED. Four experts rated the appropriateness of the cases on a 7-point scale, before and after using the decision support tool. RESULTS: Before using the ESR iGuide the overall mean rating of the experts was 5.2 ± 1.066, and it increased slightly after using the system (5.85 ± 0.911 (p < 0.01)). Using a threshold of 5 (on a 7-level scale), the experts considered only 63% of the tests appropriate before using the ESR iGuide. The number increased to 89% after consultation with the system. The degree of overall agreement among the experts was 0.388 before ESR iGuide consultation and 0.572 after consultation. According to the ESR iGuide, for 85% of the cases, CAP CT was not a recommended option (score 0). Abdominal-Pelvis CT was "usually appropriate" for 65 out of the 85 (76%) cases (score 7-9). 9% of the cases did not require CT as first exam modality. CONCLUSIONS: According to both the experts and the ESR iGuide, inappropriate testing was prevalent, in terms of both frequency of the scans and also inappropriately chosen body regions. These findings raise the need for unified workflows that might be achieved using a CDSS. Further studies are needed to investigate the CDSS contribution to the informed decision-making and increased uniformity among different expert physicians when ordering the appropriate test.

Is it time for redefining oligometastatic disease? Analysis of lung metastases CT in ten tumor types.

BACKGROUND: Oligometastatic disease (OD) is usually defined arbitrarily as a condition in which there are ≤ 5 metastases. Given limited disease, it is expected that patients with OD should have better prognosis compared to other metastatic patients and that they can potentially benefit from metastasis-directed therapy (MDT). In this study, we attempted to redefine OD based upon objective evidence that fulfill these assumptions. METHODS: Chest CTSs of 773 patients with 15,947 lung metastases originating from ten malignancy types were evaluated. The number and largest diameter of each metastasis was recorded. Metastatic cluster was defined as a cluster of two or more metastases with diameter difference ≤ 1 mm. The prognostic power of seven statistical models on overall survival (OS) was analyzed. FINDINGS: Both the number of metastases and metastatic clusters had a highly significant impact on OS (p < 0.0001, p = 0.003 respectively). Patients with a single metastasis or a single cluster of metastases (regardless of metastases number), equaling 16.2% of all patients, had significantly better prognosis compared to other patients (p = 0.0002). If metastases diameter variability is ignored, as in the standard definition of OD, then patients with 2-5 and 6-10 metastases would have a similar prognosis. INTERPRETATION: Patients with a single cluster of metastases, theoretically originating from a single clone, have significantly better prognosis compared to patients with more than one cluster. Using this definition can potentially improve the results of MDT. The upper limit of metastases number should be determined by the technical capabilities of the MDT used.

Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net.

The identification and quantification of liver lesions changes in longitudinal contrast enhanced CT (CECT) scans is required to evaluate disease status and to determine treatment efficacy in support of clinical decision-making. This paper describes a fully automatic end-to-end pipeline for liver lesion changes analysis in consecutive (prior and current) abdominal CECT scans of oncology patients. The three key novelties are: (1) SimU-Net, a simultaneous multi-channel 3D R2U-Net model trained on pairs of registered scans of each patient that identifies the liver lesions and their changes based on the lesion and healthy tissue appearance differences; (2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; (3) a method for longitudinal analysis of one or more of consecutive scans of a patient based on SimU-Net that handles major liver deformations and incorporates lesion segmentations from previous analysis. To validate our methods, five experimental studies were conducted on a unique dataset of 3491 liver lesions in 735 pairs from 218 clinical abdominal CECT scans of 71 patients with metastatic disease manually delineated by an expert radiologist. The pipeline with the SimU-Net model, trained and validated on 385 pairs and tested on 249 pairs, yields a mean lesion detection recall of 0.86±0.14, a precision of 0.74±0.23 and a lesion segmentation Dice of 0.82±0.14 for lesions > 5 mm. This outperforms a reference standalone 3D R2-UNet mdel that analyzes each scan individually by ∼50% in precision with similar recall and Dice score on the same training and test datasets. For lesions matching, the precision is 0.86±0.18 and the recall is 0.90±0.15. For lesion classification, the specificity is 0.97±0.07, the precision is 0.85±0.31, and the recall is 0.86±0.23. Our new methods provide accurate and comprehensive results that may help reduce radiologists' time and effort and improve radiological oncology evaluation.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept.

Purpose: Quantitative evaluation of renal obstruction is crucial for preventing renal atrophy. This study presents a novel method for diagnosing renal obstruction by automatically extracting objective indicators from routine multi-phase CT Urography (CTU). Material and methods: The study included multi-phase CTU examinations of 6 hydronephrotic kidneys and 24 non-hydronephrotic kidneys (23,164 slices). The developed algorithm segmented the renal parenchyma and the renal pelvis of each kidney in each CTU slice. Following a 3D reconstruction of the parenchyma and renal pelvis, the algorithm evaluated the amount of the contrast media in both components in each phase. Finally, the algorithm evaluated two indicators for assessing renal obstruction: the change in the total amount of contrast media in both components during the CTU phases, and the drainage time, "T1/2", from the renal parenchyma. Results: The algorithm segmented the parenchyma and renal pelvis with an average dice coefficient of 0.97 and 0.92 respectively. In all the hydronephrotic kidneys the total amount of contrast media did not decrease during the CTU examination and the T1/2 value was longer than 20 min. Both indicators yielded a statistically significant difference (p < 0.001) between hydronephrotic and normal kidneys, and combining both indicators yielded 100% accuracy. Conclusions: The novel algorithm enables accurate 3D segmentation of the renal parenchyma and pelvis and estimates the amount of contrast media in multi-phase CTU examinations. This serves as a proof-of-concept for the ability to extract from routine CTU indicators that alert to the presence of renal obstruction and estimate its severity.

Justification of CT practices across Europe: results of a survey of national competent authorities and radiology societies.

OBJECTIVES: Published literature on justification of computed tomography (CT) examinations in Europe is sparse but demonstrates consistent sub-optimal application. As part of the EU initiated CT justification project, this work set out to capture CT justification practices across Europe. METHODS: An electronic questionnaire consisting of mostly closed multiple-choice questions was distributed to national competent authorities and to presidents of European radiology societies in EU member states as well as Iceland, Norway, Switzerland, and the UK (n = 31). RESULTS: Fifty-one results were received from 30 European countries. Just 47% (n = 24) stated that advance justification of individual CT examinations is performed by a medical practitioner. Radiologists alone mostly (n = 27, 53%) perform daily justification of CT referrals although this is a shared responsibility in many countries. Imaging referral guidelines are widely available although just 13% (n = 6) consider them in daily use. Four countries (Cyprus, Ireland, Sweden, UK) reported having them embedded within clinical decision support systems. Justification of new practices with CT is mostly regulated (77%) although three countries (Belgium, Iceland and Portugal) reported not having any national system in place for generic justification. Health screening with CT was reported by seven countries as part of approved screening programmes and by eight countries outside. When performed, CT justification audits were reported to improve CT justification rates. CONCLUSIONS: CT justification practices vary across Europe with less than 50% using advance justification and a minority having clinical decision support systems in place. CT for health screening purposes is not currently widely used in Europe.

Feasibility and Accuracy of a Novel Hands-Free Robotic System for Percutaneous Needle Insertion and Steering.

PURPOSE: To assess the performance and accuracy of CT-guided needle insertion for clinical biopsies using a novel, hands-free robotic system that balances accuracy with the duration of the procedure and radiation dose. MATERIALS AND METHODS: A prospective, multi-center study was conducted on 60 clinically indicated biopsies of abdominal lesions at two centers (Center 1, n=26; Center 2, n=34). CT datasets were obtained for planning and controlled placement of 17g and 18g needles using a patient-mounted, CT-guided robotic system with 5 degrees of freedom. Planning included target selection, skin entry point, and predetermined checkpoints where additional imaging was performed to permit stepwise correction of the needle trajectory. Success rate, needle tip-to-target distance, number of checkpoints used, number of trajectory corrections, procedure duration, and effective radiation dose were recorded and compared between centers. RESULTS: In 55 of 60 procedures (91.7%), the robot positioned the trocar needle successfully on target. In the remaining 5 patients, the procedure was manually performed by the operator due to technical failure (n=3) or patient-related factors (n=2). The average lesion size was 2.8 ± 1.7cm with a lesion depth from the skin of 8.7 ± 2.6cm, and there was no difference between centers. The overall accuracy (needle tip-to-target distance) was 1.71 ± 1.49 (range 0.05-7.20mm), with an accuracy of 2.06 ± 1.45 mm at Center 1 and 1.45 ± 1.52 mm at Center 2 (p=0.1358). Center 1 used significantly more checkpoints (4.96 ± 1.08) and performed target adjustments in 20 of 24 (83%) cases compared to Center 2 (2.77 ± 0.6 checkpoints and target adjustments in 13 of 31 cases, 42%) (p=0.0024). Accordingly, the steering duration from skin entry to the target varied between Centers 1 and 2; 13.1min ± 4.25min vs. 5.7min ± 2.7min, respectively (p <0.001). The average DLP for the entire procedure was 1147 ± 820 mGycm, with a slightly lower average at Center 2 (1031 ± 724 mGycm) compared to Center 1 (1297 ± 925 mGycm) (p=0.236). CONCLUSION: Accurate needle-targeting within an error of 2mm can be achieved in patients using a CT-guided robotic system. The variation in the number of checkpoints did not affect system accuracy but was related to shorter steering times and may contribute to a lower radiation dose. Accurate needle insertion using a hands-free CT-guided robotic system may facilitate difficult needle placement and enhance the performance of less-experienced interventionalists.

Patterns of metastases progression- The linear parallel ratio.

BACKGROUND: Linear and parallel are the two leading models of metastatic progression. In this study we propose a simple way to differentiate between them. While the linear model predicts accumulation of genetic and epigenetic alterations within the primary tumor by founder cells before spreading as waves of metastases, the parallel model suggests preclinical distribution of less advanced disseminated tumor cells with independent selection and expansion at the ectopic sites. Due to identical clonal origin and time of dispatching, linear metastases are expected to have comparable diameters in any specific organ while parallel metastases are expected to appear in variable sizes. METHODS AND FINDINGS: Retrospective revision of chest CT of oncological patients with lung metastases was performed. Metastasis number and largest diameters were recorded. The sum number of metastases with a similar diameter (c) and those without (i) was counted and the linear/parallel ratio (LPR) was calculated for each patient using the formula (∑c-∑i)/(∑c+∑i). A LPR ratio of 1 implies pure linear progression pattern and -1 pure parallel. 12,887 metastases were measured in 503 patients with nine malignancy types. The median LPR of the entire group was 0.71 (IQR 0.14-0.93). In carcinomas of the pancreas, prostate, and thyroid the median LPR was 1. Median LPRs were 0.91, 0.65, 0.60, 0.58, 0.50 and 0.43 in renal cell carcinomas, melanomas, colorectal, breast, bladder, and sarcomas, respectively. CONCLUSIONS: Metastatic spread of thyroid, pancreas, and prostate tumors is almost exclusively by a linear route. The spread of kidney, melanoma, colorectal, breast, bladder and sarcoma is both linear and parallel with increasing dominance of the parallel route in this order. These findings can explain and predict the clinical and genomic features of these tumors and can potentially be used for evaluation of metastatic origin in the individual patient.