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Background: Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods: A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings: Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively. [ABSTRACT FROM AUTHOR]

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. [ABSTRACT FROM AUTHOR]

Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network’s capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. Typically, the problem can be approached by developing more discriminative diagnosis methods. In this paper, computer-aided diagnosis was used to facilitate the prediction of characteristics of solitary pulmonary nodules in CT of lungs to diagnose early-stage lung cancer. Methods: The synthetic minority over-sampling technique (SMOTE) was used to account for raw data in order to balance the original training data set. Curvelet-transformation textural features, together with 3 patient demographic characteristics, and 9 morphological features were used to establish a support vector machine (SVM) prediction model. Longitudinal data as the test data set was used to evaluate the classification performance of predicting early-stage lung cancer. Results: Using the SMOTE as a pre-processing procedure, the original training data was balanced with a ratio of malignant to benign cases of 1∶1. Accuracy based on cross-evaluation for the original unbalanced data and balanced data was 80% and 97%, respectively. Based on Curvelet-transformation textural features and other features, the SVM prediction model had good classification performance for early-stage lung cancer, with an area under the curve of the SVMs of 0.949 (P<0.001). Textural feature (standard deviation) showed benign cases had a higher change in the follow-up period than malignant cases. Conclusions: With textural features extracted from a Curvelet transformation and other parameters, a sensitive support vector machine prediction model can increase the rate of diagnosis for early-stage lung cancer. This scheme can be used as an auxiliary tool to differentiate between benign and malignant early-stage lung cancers in CT images. [ABSTRACT FROM AUTHOR]

Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained. [ABSTRACT FROM AUTHOR]

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy. [ABSTRACT FROM AUTHOR]

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results. [ABSTRACT FROM AUTHOR]

Objective: We performed a meta-analysis to evaluate the diagnostic value of positron emission tomography/computed tomography (PET/CT) in the detection of para-aortic lymph node metastasis in cervical cancer. Methods: We searched the PubMed, Embase, Web of Science, Cochrane Library, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang and VIP databases in all languages from their inception to September 2018. Stat15.0 software was used to obtain pooled estimates of sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) as well as a summary receiver operating characteristic (SROC) curves. Deek‘s funnel plot was used to assess publication bias. QUADAS-2 was used to evaluate the quality of the studies. The protocol for this meta-analysis is registered in PROSPERO (CRD42019115330). Results: We obtained 14 studies, and the pooled estimates for sensitivity and specificity of PET/CT were 0.71 (95% confidence interval (CI) = 0.54–0.83) and 0.97 (95% CI = 0.93–0.98), respectively. Pooled PLR and NLR were 21.53 and 0.30, respectively. The diagnostic odds ratio (DOR) was70.59, and the area under the curve (AUC) was 0.95. Conclusion: PET/CT is an effective and important imaging method for the diagnosis of para-aortic lymph node metastasis in early cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods: The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results: The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion: The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets. [ABSTRACT FROM AUTHOR]

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. [ABSTRACT FROM AUTHOR]

Background: Surgical indications for rectal neuroendocrine tumors with potential lymph node metastasis remain controversial. Although accurate preoperative diagnosis of nodal status may be helpful for treatment strategy, scant data about clinical values of lymph node size have been reported. The aim of this retrospective study was to investigate the relationship between lymph node size and lymph node metastasis. Methods: Participants comprised 102 patients who underwent rectal resection with total mesenteric excision or tumor-specific mesenteric excision and in some cases additional lateral pelvic lymph node dissection for rectal neuroendocrine tumor between June 2005 and September 2016. All lymph nodes from specimens were checked and measured. Results: Pathological lymph node metastasis was confirmed in 37 patients (36%), including 6 patients (5.8%) with lateral pelvic lymph node metastasis. A total of 1169 lymph nodes in the mesorectum were retrieved from all specimens, with 78 lymph nodes (6.7%) showing metastasis. Mean length (long-axis diameter) of metastatic lymph nodes in the mesorectum was 4.31 mm, significantly larger than that of non-metastatic lymph nodes (2.39 mm, P<0.01). The optimal cut-off of major axis length for predicting mesorectal lymph node metastasis was 3 mm. We could predict lymph node metastasis in only 7 patients (21%) from preoperative multidetector-row computed tomography. Conclusions: Metastatic lymph nodes were small, so predicting lymph node metastasis from preoperative computed tomography is difficult. Alternative modalities with a scan width less than 3 mm may be needed to predict lymph node metastasis of rectal NET with low cost and labour requirements. [ABSTRACT FROM AUTHOR]

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods. [ABSTRACT FROM AUTHOR]

Background: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.Methods and Findings: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks.Conclusions: Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data. [ABSTRACT FROM AUTHOR]

Background: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Methods: Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. Results: For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. Conclusions: Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the diagnostic performance of choline positron emission tomography/computed tomography (PET/CT) for the detection of bone metastasis in patients with prostate cancer. Methods: MEDLINE, EMBASE and the Cochrane Library were searched up to 20 February 2018 for studies that used 11C-choline or 18F-choline PET/CT for the detection of bone metastasis in patients with prostate cancer and “histopathology and/or clinical follow-up” as the reference standard. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Pooled diagnostic accuracy with the 95% confidence interval (CI) was calculated using a bivariate random effects model. We also constructed hierarchical summary receiver operating characteristic curves and performed meta-regression analyses. Results: Fourteen studies with reasonable methodological quality were included in the analysis. On a per-patient basis, the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 0.89 (95% CI 0.80–0.94), 0.98 (95% CI 0.95–0.99), 40.4 (95% CI 19.7–82.6), 0.12 (95% CI 0.07–0.20), and 344 (95% CI 148–803), respectively. On a per-lesion basis, the pooled sensitivity, specificity, PLR, NLR, and DOR were 0.91 (95% CI 0.85–0.94), 0.97 (95% CI 0.95–0.98), 34.1 (95% CI 20.0–58.1), 0.10 (95% CI 0.06–0.16), and 358 (95% CI 165–778), respectively. In the meta-regression analysis, the clinical setting (staging vs. restaging) was the only source of study heterogeneity on a per-patient basis. Conclusions: Choline PET/CT shows excellent diagnostic performance for the detection of bone metastasis. However, a negative choline PET/CT result cannot ensure the lack of bone metastasis. [ABSTRACT FROM AUTHOR]

Introduction: Aim was to develop a full automatic clustering approach of the time-activity curves (TAC) from dynamic 18F-FET PET and evaluate its association with IDH1 mutation status and survival in patients with gliomas. Methods: Thirty-seven patients (mean age: 45±13 y) with newly diagnosed gliomas and dynamic 18F-FET PET before any histopathologic investigation or treatment were retrospectively included. Each dynamic 18F-FET PET was realigned to the first image and spatially normalized in the Montreal Neurological Institute template. A tumor mask was semi-automatically generated from Z-score maps. Each brain tumor voxel was clustered in one of the 3 following centroids using dynamic time warping and k-means clustering (centroid #1: slowly increasing slope; centroid #2: rapidly increasing followed by slowly decreasing slope; and centroid #3: rapidly increasing followed by rapidly decreasing slope). The percentage of each dynamic 18F-FET TAC within tumors and other conventional 18F-FET PET parameters (maximum and mean tumor-to-brain ratios [TBRmax and TBRmean], time-to-peak [TTP] and slope) was compared between wild-type and IDH1 mutant tumors. Their prognostic value was assessed in terms of progression free-survival (PFS) and overall survival (OS) by Kaplan-Meier estimates. Results: Twenty patients were IDH1 wild-type and 17 IDH1 mutant. Higher percentage of centroid #1 and centroid #3 within tumors were positively (P = 0.016) and negatively (P = 0.01) correlated with IDH1 mutated status. Also, TBRmax, TBRmean, TTP, and slope discriminated significantly between tumors with and without IDH1 mutation (P range 0.01 to 0.04). Progression occurred in 22 patients (59%) at a median of 13.1 months (7.6–37.6 months) and 13 patients (35%) died from tumor progression. Patients with a percentage of centroid #1 > 90% had a longer survival compared with those with a percentage of centroid #1 < 90% (P = 0.003 for PFS and P = 0.028 for OS). This remained significant after stratification on IDH1 mutation status (P = 0.029 for PFS and P = 0.034 for OS). Compared to other conventional 18F-FET PET parameters, TTP and slope were associated with PFS and OS (P range 0.009 to 0.04). Conclusions: Based on dynamic 18F-FET PET acquisition, we developed a full automatic clustering approach of TAC which appears to be a valuable noninvasive diagnostic and prognostic marker in patients with gliomas. [ABSTRACT FROM AUTHOR]

Purpose: Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. Material and methods: Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model. Results: Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939. Conclusions: Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed. [ABSTRACT FROM AUTHOR]

Background: MRI is the optimal method for sensitive detection of tumour tissue and pre-operative staging in oral cancer. When jawbone resections are necessary, the current standard of care for oral tumour surgery in our hospital is 3D virtual planning from CT data. 3D printed jawbone cutting guides are designed from the CT data. The tumour margins are difficult to visualise on CT, whereas they are clearly visible on MRI scans. The aim of this study was to change the conventional CT-based workflow by developing a method for 3D MRI-based lower jaw models. The MRI-based visualisation of the tumour aids in planning bone resection margins. Materials and findings: A workflow for MRI-based 3D surgical planning with bone cutting guides was developed using a four-step approach. Key MRI parameters were defined (phase 1), followed by an application of selected Black Bone MRI sequences on healthy volunteers (phase 2). Three Black Bone MRI sequences were chosen for phase 3: standard, fat saturated, and an out of phase sequence. These protocols were validated by applying them on patients (n = 10) and comparison to corresponding CT data. The mean deviation values between the MRI- and the CT-based models were 0.63, 0.59 and 0.80 mm for the three evaluated Black Bone MRI sequences. Phase 4 entailed examination of the clinical value during surgery, using excellently fitting printed bone cutting guides designed from MRI-based lower jaw models, in two patients with oral cancer. The mean deviation of the resection planes was 2.3 mm, 3.8 mm for the fibula segments, and the mean axis deviation was the fibula segments of 1.9°. Conclusions: This study offers a method for 3D virtual resection planning and surgery using cutting guides based solely on MRI imaging. Therefore, no additional CT data are required for 3D virtual planning in oral cancer surgery. [ABSTRACT FROM AUTHOR]

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences. [ABSTRACT FROM AUTHOR]

Background: As Medicare expands the use of computed tomography (CT) for diagnosing lung cancer, there is increased opportunity to diagnose lung cancer in asymptomatic patients. This descriptive study characterizes the disease-specific diagnostic and treatment services that patients with a positive diagnosis following CT received, stratified by presentation at CT. Methods: Patients who were diagnosed with lung cancer following CT in 2013, had no history of lung cancer, survived at least 1 year, were aged 55–80 years, and had Medicare Advantage insurance were included. Patients were grouped based upon presentation at CT: morbidities unrelated to lung cancer, classic lung cancer symptoms, and cancer syndromes. Patients with none of these factors were categorized into a no diagnoses/symptoms group. The type and intensity of services used in the year following the CT was reported for each group. Results: 1,261 patients were included. Early treatment services were most common in the group with morbidities unrelated to lung cancer (13.7%) and least common in the cancer syndromes group (6.6%). Advanced treatment services were used by 47.3% of the cancer syndromes group versus 23.5% of the no diagnoses/symptoms group. Conclusions: The intensity of disease-specific diagnostic and treatment services varied by presentation at CT. Patients with no symptoms or morbidities at the time of CT less frequently received advanced interventions. Learning about the utilization patterns of others with a similar presentation at CT may help patients with positive lung cancer diagnoses engage in shared decision making and in norming their experiences against those of other similarly-situated patients. [ABSTRACT FROM AUTHOR]

Development and comparison of spine-shaped phantoms generated by two different 3D-printing technologies, digital light processing (DLP) and Polyjet has been purposed to utilize in patient-specific quality assurance (QA) of stereotactic body radiation treatment. The developed 3D-printed spine QA phantom consisted of an acrylic body phantom and a 3D-printed spine shaped object. DLP and Polyjet 3D printers using a high-density acrylic polymer were employed to produce spine-shaped phantoms based on CT images. Image fusion was performed to evaluate the reproducibility of our phantom, and the Hounsfield units (HUs) were measured based on each CT image. Two different intensity-modulated radiotherapy plans based on both CT phantom image sets from the two printed spine-shaped phantoms with acrylic body phantoms were designed to deliver 16 Gy dose to the planning target volume (PTV) and were compared for target coverage and normal organ-sparing. Image fusion demonstrated good reproducibility of the developed phantom. The HU values of the DLP- and Polyjet-printed spine vertebrae differed by 54.3 on average. The PTV Dmax dose for the DLP-generated phantom was about 1.488 Gy higher than that for the Polyjet-generated phantom. The organs at risk received a lower dose for the 3D printed spine-shaped phantom image using the DLP technique than for the phantom image using the Polyjet technique. Despite using the same material for printing the spine-shaped phantom, these phantoms generated by different 3D printing techniques, DLP and Polyjet, showed different HU values and these differently appearing HU values according to the printing technique could be an extra consideration for developing the 3D printed spine-shaped phantom depending on the patient’s age and the density of the spinal bone. Therefore, the 3D printing technique and materials should be carefully chosen by taking into account the condition of the patient in order to accurately produce 3D printed patient-specific QA phantom. [ABSTRACT FROM AUTHOR]

Purpose: To retrospectively evaluate diagnostic performance of dual-energy subtraction radiography (DESR) for interpretation of chest radiographs compared to conventional radiography (CR) using computed tomography (CT) as standard of reference. Material and methods: A total of 199 patients (75 female, median age 67) were included in this institutional review board (IRB)-approved clinical trial. All patients were scanned in posteroanterior and lateral direction with dual-shot DE-technique. Chest CT was performed within ±72 hours. The system provides three types of images: bone weighted-image, soft tissue weighted-image, herein termed as DESR-images, and a standard image, termed CR-image (marked as CR-image). Images were evaluated by two radiologists for presence of inserted life support lines, pneumothorax, pleural effusion, infectious consolidation, interstitial lung changes, tumor, skeletal alterations, soft tissue alterations, aortic or tracheal calcification and pleural thickening. Inter-observer agreement between readers and diagnostic performance were calculated. McNemar’s test was used to test for significant differences. Results: Mean inter-observer agreement throughout the investigated parameters was higher in DESR images compared to CR-images (kDESR = 0.935 vs. kCR = 0.858). DESR images provided significantly increased sensitivity compared to CR-images for the detection of infectious consolidations (42% vs. 62%), tumor (46% vs. 57%), interstitial lung changes (69% vs. 87%) and aortic or tracheal calcification (25 vs. 73%) (p<0.05). There were no significant differences in sensitivity for the detection of inserted life support lines, pneumothorax, pleural effusion, skeletal alterations, soft tissue alterations or pleural thickening (p>0.05). Conclusion: DESR increases significantly the sensibility without affecting the specificity evaluating chest radiographs, with emphasis on the detection of interstitial lung diseases. [ABSTRACT FROM AUTHOR]

Background: The National Lung Screening Trial (NLST) results indicate that computed tomography (CT) lung cancer screening for current and former smokers with three annual screens can be cost-effective in a trial setting. However, the cost-effectiveness in a population-based setting with >3 screening rounds is uncertain. Therefore, the objective of this study was to estimate the cost-effectiveness of lung cancer screening in a population-based setting in Ontario, Canada, and evaluate the effects of screening eligibility criteria.Methods and Findings: This study used microsimulation modeling informed by various data sources, including the Ontario Health Insurance Plan (OHIP), Ontario Cancer Registry, smoking behavior surveys, and the NLST. Persons, born between 1940 and 1969, were examined from a third-party health care payer perspective across a lifetime horizon. Starting in 2015, 576 CT screening scenarios were examined, varying by age to start and end screening, smoking eligibility criteria, and screening interval. Among the examined outcome measures were lung cancer deaths averted, life-years gained, percentage ever screened, costs (in 2015 Canadian dollars), and overdiagnosis. The results of the base-case analysis indicated that annual screening was more cost-effective than biennial screening. Scenarios with eligibility criteria that required as few as 20 pack-years were dominated by scenarios that required higher numbers of accumulated pack-years. In general, scenarios that applied stringent smoking eligibility criteria (i.e., requiring higher levels of accumulated smoking exposure) were more cost-effective than scenarios with less stringent smoking eligibility criteria, with modest differences in life-years gained. Annual screening between ages 55-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago yielded an incremental cost-effectiveness ratio of $41,136 Canadian dollars ($33,825 in May 1, 2015, United States dollars) per life-year gained (compared to annual screening between ages 60-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago), which was considered optimal at a cost-effectiveness threshold of $50,000 Canadian dollars ($41,114 May 1, 2015, US dollars). If 50% lower or higher attributable costs were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $38,240 ($31,444 May 1, 2015, US dollars) or $48,525 ($39,901 May 1, 2015, US dollars), respectively. If 50% lower or higher costs for CT examinations were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $28,630 ($23,542 May 1, 2015, US dollars) or $73,507 ($60,443 May 1, 2015, US dollars), respectively. This scenario would screen 9.56% (499,261 individuals) of the total population (ever- and never-smokers) at least once, which would require 4,788,523 CT examinations, and reduce lung cancer mortality in the total population by 9.05% (preventing 13,108 lung cancer deaths), while 12.53% of screen-detected cancers would be overdiagnosed (4,282 overdiagnosed cases). Sensitivity analyses indicated that the overall results were most sensitive to variations in CT examination costs. Quality of life was not incorporated in the analyses, and assumptions for follow-up procedures were based on data from the NLST, which may not be generalizable to a population-based setting.Conclusions: Lung cancer screening with stringent smoking eligibility criteria can be cost-effective in a population-based setting. [ABSTRACT FROM AUTHOR]

Purpose: To find differential CT features of colorectal schwannomas from gastrointestinal stromal tumors (GISTs). Materials and Methods: CT features of 13 pathologically proven colorectal schwannomas and 21 GISTs were retrospectively reviewed. The following CT items were analyzed: size, longitudinal and transverse location, shape, margin, homogeneity, necrosis, surface ulceration, calcification, degree of attenuation, the presence of enlarged lymph node (LN), and metastasis. Among the features, significant variables were evaluated using univariate statistical tests. The optimal cut-off point of tumor size was obtained by ROC analysis. Binary logistic regression analysis was used to find the most independent CT variables. Results: Small size, non-rectum location, smooth margin, homogeneous high attenuation without necrosis, and the presence of enlarged LNs were found to be significant variables to differentiate schwannomas from GISTs (P<0.05). The optimized cut-off point for tumor size in distinguishing GISTs from schwannomas was 3.9 cm (AUC = 0.808, sensitivity = 66.7%, specificity = 92.3%, P<0.0001). Binary regression analysis revealed that only non-rectum location remained independent predictor for schwannomas differentiated from GISTs (odds ratio = 31.667, P = 0.001). Conclusion: Colorectal schwannomas usually located in non-rectum and appear as small subepithelial nodules showing homogeneous high attenuation and smooth margin. Schwannomas exclusively accompany with enlarged LNs. [ABSTRACT FROM AUTHOR]

Background & Aims: Liver computed tomography and dynamic magnetic resonance imaging play an important role in the early detection of hepatocellular carcinoma. However, the American Association for the Study of Liver Diseases (AASLD) recommend the use of applied imaging studies for HCC diagnosis only in cirrhotic patients. This study aimed to comprehensively compare liver CT and dynamic MRI for HCC diagnosis before surgical resection over years in clinical practice, and also to compare the diagnostic differences between liver CT and dynamic MRI in HCCs with varying degrees of fibrosis. Methods: 841 patients with liver tumor who had liver CT or dynamic MRI examinations followed by surgical resection were included in the study. We defined typical HCC imaging characteristics as early enhancement in the artery phase and early washout in the venous phase. The tumor size was recorded based on pathological examination after surgery. The pathologic fibrosis score was verified by the METAVIR scoring classification. Results: Among the 841 patients, 756 underwent liver CT and 204 underwent dynamic liver MRI before surgery. The etiologies of chronic liver disease included hepatitis B virus, hepatitis C virus, hepatitis B and C virus, and non-hepatitis B or C virus. The sensitivity and accuracy of liver CT or MRI for HCC diagnosis was approximately 80%~90%. Liver CT had a diagnostic accuracy for HCC similar to that of dynamic MRI, and liver fibrosis stage did not influence their diagnostic efficacies. Conclusions: The application of 4-phase dynamic CT and MRI exhibit similar diagnostic accuracy for hepatocellular carcinoma, in tumors of sizes 1 to 2 cm and >2 cm. Liver fibrosis status did not affect the diagnostic accuracy of liver CT or MRI for HCC. The AASLD and EASL restrictions of dynamic imaging studies for HCC diagnosis to cirrhotic patients alone are unnecessary. [ABSTRACT FROM AUTHOR]

Purpose: Minimal axial diameter (MIAD) in magnetic resonance imaging (MRI) was recognized as the most useful parameter in diagnosing lateral retropharyngeal lymph (LRPL) nodes in nasopharyngeal carcinoma (NPC). This study aims to explore the additional nodal parameters in MRI and positron emission tomography–computed tomography for increasing the prediction accuracy. Materials and Methods: A total of 663 LRPL nodes were retrospectively collected from 335 patients with NPC. The LRPL nodes ascertained on follow-up MRI were considered positive for metastases. First, the optimal cutoff value of each parameter was derived for each parameter. In addition, neural network (NN) nodal evaluation was tested for all combinations of three parameters, namely MIAD, maximal axial diameter (MAAD), and maximal coronal diameter (MACD). The optimal approach was determined through brute force attack, and the results of two methods were compared using a bootstrap sampling method. Second, the mean standard uptake value (NSUVmean) was added as the fourth parameter and tested in the same manner for 410 nodes in 219 patients. Results: In first and second analysis, the accuracy rate (percentage) for the MIAD was 89.0% (590/663) and 89.0% (365/410), with the optimal cutoff values being 6.1 mm and 6.0 mm, respectively. With the combination of all three and four parameters, the accuracy rate of the NN was 89% (288/332) and 88.8% (182/205), respectively. In prediction, the optimal combinations of the three and four parameters resulted in correct identification of three (accuracy: 593/663, 89.4%) and six additional nodes (371/410, 90.5%), representing 4% (3/73) and 13.3% (6/45) decreases in incorrect prediction, respectively. Conclusion: NPC LRPL nodes with an MIAD ≥ 6.1 mm are positive. Among nodes with an MIAD < 6.1 mm, if the NSUVmean ≥ 2.6 or MACD ≥ 25 mm and MAAD ≥ 8 mm, the nodes are positive; otherwise, they are negative. [ABSTRACT FROM AUTHOR]

CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distance between corresponding landmark of 6.4 mm vs 10.2 mm). The registration of the preoperative data with the needle image in 16 cases yielded a DICE of 90.1% and a mean surface distance of 5.2 mm. The remaining three cases with DICE smaller than 80% were classified as unsuccessful registration. The results show that this is promising tool for liver image registration in interventional radiology. [ABSTRACT FROM AUTHOR]

This study aimed to identify the computed tomography characteristics of treatment-naïve patients with lung adenocarcinoma and known driver mutations in EGFR, KRAS, or ALK. Patients with advanced lung adenocarcinoma (stage IIIB–IV) and known mutations in EGFR, KRAS, or ALK were assessed. The radiological findings for the main tumor and intra-thoracic status were retrospectively analyzed in each group, and the groups’ characteristics were compared. We identified 265 treatment-naïve patients with non-small-cell carcinoma, who had EGFR mutations (n = 159), KRAS mutations (n = 55), or ALK rearrangements (n = 51). Among the three groups, we evaluated only patients with stage IIIB–IV lung adenocarcinoma who had EGFR mutations (n = 126), KRAS mutations (n = 35), or ALK rearrangements (n = 47). We found that ground-glass opacity at the main tumor was significantly more common among EGFR-positive patients, compared to ALK-positive patients (p = 0.009). Lymphadenopathy was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.003). Extranodal invasion was significantly more common among ALK-positive patients, compared to EGFR-positive patients and KRAS-positive patients (p = 0.001 and p = 0.049, respectively). Lymphangitis was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.049). Pleural effusion was significantly less common among KRAS-positive patients, compared to EGFR-positive patients and ALK-positive patients (p = 0.046 and p = 0.026, respectively). Lung metastases were significantly more common among EGFR-positive patients, compared to KRAS-positive patients and ALK-positive patients (p = 0.007 and p = 0.04, respectively). In conclusion, EGFR mutations were associated with ground-glass opacity, KRAS-positive tumors were generally solid and less likely to metastasize to the lung and pleura, and ALK-positive tumors tended to present with lymphadenopathy, extranodal invasion, and lymphangitis. These mutation-specific imaging characteristics may be related to the biological differences between these cancers. [ABSTRACT FROM AUTHOR]

Background: Image texture analysis is a noninvasive technique for quantifying intratumoral heterogeneity, with derived texture features reported to be closely related to the treatment outcome of tumors. Gastric cancer is one of the most common tumors and the third leading cause of cancer-related deaths worldwide. Although trastuzumab is associated with a survival gain among patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer, optimal patient selection is challenging. The purpose of this study was to determine whether CT texture features of HER2-positive gastric cancer were related to the survival rate after trastuzumab treatment. Methods and Findings: Patients diagnosed with HER2-positive advanced gastric cancer from February 2007 to August 2014 were retrospectively selected. Using in-house built software, histogram features (kurtosis and skewness) and gray-level co-occurrence matrices (GLCM) features (angular second moment [ASM], contrast, entropy, variance, and correlation) were derived from the CT images of HER2-positive advanced gastric cancer in 26 patients. All the patients were followed up for more than 6 months, with no confirmed deaths. The patients were dichotomized into a good and poor survival group based on cutoff points of overall survival of 12 months. A receiver-operating characteristics (ROC) analysis was performed to test the ability of each texture parameter to identify the good survival group. Kaplan–Meier curves for patients above and below each threshold were constructed. Using a threshold of >265.8480 for contrast, >488.3150 for variance, and ≤0.1319×10−3. for correlation, all of the area under the ROC curves showed fair accuracy (>0.7). Kaplan–Meier analysis showed statistically significant survival difference between two groups according to optimal cutoff values of contrast, variance, correlation and ASM. However, as this study had a small number of patients, a further study with a larger population will be needed to validate the results. Conclusions: Heterogeneous texture features on CT images were associated with better survival in patients with HER2-positive advanced gastric cancer who received trastuzumab-based treatment. Therefore, texture analysis shows potential to be a clinically useful imaging biomarker providing additional prognostic information for patient selection. [ABSTRACT FROM AUTHOR]

Background: The appropriate management of the large number of lung nodules detected during the course of routine medical care presents a challenge. We aimed to evaluate the usual clinical practice in solitary pulmonary nodule (SPN) management and associated radiation exposure. Methods: We examined 893 radiology reports of consecutive patients undergoing chest computed tomography (CT) and radiography at two public hospitals in Spain. Information on diagnostic procedures from SPN detection and lung cancer diagnosis was collected prospectively for 18 months. Results: More than 20% of patients with SPN detected on either chest radiograph (19.8%) or CT (26.1%) underwent no additional interventions and none developed lung cancer (100% negative predictive value). 346 (72.0%) patients with SPN detected on chest radiograph and 254 (61.5%) patients with SPN detected on CT had additional diagnostic tests and were not diagnosed with lung cancer. In patients undergoing follow-up imaging for SPNs detected on CT median number of additional imaging tests was 3.5 and the mean cumulative effective dose was 24.4 mSv; for those detected on chest radiograph the median number of additional imaging tests was 2.8 and the mean cumulative effective dose was 10.3 mSv. Conclusions: Patients who did not have additional interventions were not diagnosed of lung cancer. There was an excessive amount of interventions in a high percentage of patients presenting SPN, which was associated with an excess of radiation exposure. [ABSTRACT FROM AUTHOR]

Introduction: For accurate dose calculations, it is necessary to provide a correct relationship between the CT numbers and electron density in radiotherapy treatment planning systems (TPSs). The purpose of this study was to investigate the energy dependence of measured CT numbers on substituted materials used for CT number calibration of radiotherapy TPSs and the resulting errors in the treatment planning calculation doses. Materials and Methods: In this study, we designed a cylindrical water phantom with different materials used as tissue equivalent materials for the simulation of tissues and obtaining the related CT numbers. For evaluating the effect of CT number variations of substituted materials due to energy changing of scanner (kVp) on the dose calculation of TPS, the slices of the scanned phantom at three kVp's were imported into the desired TPSs (MIRS and CorePLAN). Dose calculations were performed on two TPSs. Results: The mean absolute percentage differences between the CT numbers of CT scanner and two treatment planning systems for all the samples were 3.22%±2.57% for CorePLAN and 2.88%±2.11% for MIRS. It was also found that the maximum absolute percentage difference between all of the calculated doses from each photon beam of linac (6 and 15 MV) at three kVp's was less than 1.2%. Discussion: The present study revealed that, for the materials with effective low atomic number, the mean CT number increased with increasing energy, which was opposite for the materials with an effective high atomic number. We concluded that the tissue substitute materials had a different behavior in the energy ranges from 80 to 130 kVp. So, it is necessary to consider the energy dependence of the substitute materials used for the measurement or calibration of CT number for radiotherapy treatment planning systems. [ABSTRACT FROM AUTHOR]

Objectives: To prospectively compare the diagnostic performance and the visualization of the upper urinary tract (UUT) using a comprehensive 3.0T- magnetic resonance urography (MRU) protocol versus triple-phase computed tomography urography (CTU). Methods: During the study period (January-2014 through December-2015), all consecutive patients in our tertiary university hospital scheduled by a urologist for CTU to exclude UUT malignancy were invited to participate. Diagnostic performance and visualization scores of 3.0T-MRU were compared to CTU using Wilcoxon matched-pairs test. Results: Twenty patients (39 UUT excreting units) were evaluated. 3.0T-MRU and CTU achieved equal diagnostic performances. The benign etiology of seven UUT obstructions was clarified equally with both methods. Another two urinary tract malignant tumors and one benign extraurinary tumor were detected and confirmed. Diagnostic visualization was slightly better in the intrarenal cavity areas with CTU but worsened towards distal ureter. MRU showed consistently slightly better visualization of the ureter. In the comparison, full 100% visualizations were detected in all areas in 93.6% (with 3.0T-MRU) and 87.2% (with CTU) and >75% visualization in 100% (3.0T-MRU) and 93.6% (CTU). Mean CTU effective radiation dose was 9.2 mSv. Conclusions: Comprehensive 3.0T-MRU is an accurate imaging modality achieving comparable performance with CTU; since it does not entail exposure to radiation, it has the potential to become the primary investigation technique in selected patients. Trial Registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to improve the image quality of cone-beam computed tomography (CBCT) mounted on the gantry of a linear accelerator used in radiation therapy based on the image information provided by planning multi-detector CT (MDCT). Methods: MDCT-based shading correction for CBCT and virtual monochromatic CT (VMCT) synthesized using the dual-energy method were performed. In VMCT, the high-energy data were obtained from CBCT, while the low-energy data were obtained from MDCT. An electron density phantom was used to investigate the efficacy of shading correction and VMCT on improving the target detectability, Hounsfield unit (HU) accuracy and variation, which were quantified by calculating the contrast-to-noise ratio (CNR), the percent difference (%Diff) and the standard deviation of the CT numbers for tissue equivalent background material, respectively. Treatment plan studies for a chest phantom were conducted to investigate the effects of image quality improvement on dose planning. Results: For the electron density phantom, the mean value of CNR was 17.84, 26.78 and 34.31 in CBCT, shading-corrected CBCT and VMCT, respectively. The mean value of %Diff was 152.67%, 11.93% and 7.66% in CBCT, shading-corrected CBCT and VMCT, respectively. The standard deviation within a uniform background of CBCT, shading-corrected CBCT and VMCT was 85, 23 and 15 HU, respectively. With regards to the chest phantom, the monitor unit (MU) difference between the treatment plan calculated using MDCT and those based on CBCT, shading corrected CBCT and VMCT was 6.32%, 1.05% and 0.94%, respectively. Conclusions: Enhancement of image quality in on-board CBCT can contribute to daily patient setup and adaptive dose delivery, thus enabling higher confidence in patient treatment accuracy in radiation therapy. Based on our results, VMCT has the highest image quality, followed by the shading corrected CBCT and the original CBCT. The research results presented in this study should be able to provide a route to reach a high level of image quality for CBCT imaging in radiation oncology. [ABSTRACT FROM AUTHOR]

To retrospectively determine the association between breast lesion morphology and malignancy and to determine the optimal value of lesion enhancement (HU, Hounsfield units) to improve the diagnostic accuracy of breast cancer in patients with incidental breast lesions (IBLs). A total of 97 patients with 102 IBLs detected from July 2009 to December 2012 were enrolled in this study. Two radiologists analyzed CT images for the presence of malignancy based on the morphology of the lesions alone and in combination with an enhancement value (HU) analysis. There were 36 malignant and 66 benign IBLs. When the morphology and enhancement values were combined, the sensitivity, specificity, and accuracy were 92%, 97%, and 95%, respectively, for reader 1 and 89%, 94%, and 92%, respectively, for reader 2. The addition of HU values led to correct changes in the diagnosis; specifically, the accuracy of the diagnosis of reader 1 and reader 2 improved by 6.9% and 11.8%, respectively. The addition of the enhancement value (HU) to the CT morphology improved the diagnostic accuracy in the differentiation of malignant from benign IBLs by using the region of interest (ROI) to measure the HU within the most suspicious part of the lesion. [ABSTRACT FROM AUTHOR]

Background: Conflicting findings were observed in recent studies assessing the association between patients’ area-level socio-economic status and the received number of computed tomography (CT) examinations in children. The aim was to investigate the association between area-level socio-economic status and variation in CT examination practice for pediatric patients in Germany. Methods: Data from Radiology Information Systems for children aged 0 to < 15 years without cancer who had at least one CT examination between 2001 and 2010 were extracted in 20 hospitals across Germany. The small-area German Index of Multiple Deprivation (GIMD) was used to assess regional deprivation. The GIMD scores were classified into least, medium and most deprived areas and linked with the patient’s last known postal code. A multinomial logistic regression model was used to assess the association between patients’ CT numbers and regional deprivation adjusting for age, sex, and location of residence (urban/rural). Results: A total of 37,810 pediatric patients received 59,571 CT scans during the study period. 27,287 (72%) children received only one CT, while n = 885 (2.3%) received six or more. Increasing numbers of CT examinations in non-cancer patients were significantly associated with higher regional deprivation, which increased, although CI overlap, for higher CT categories: ‘2–3 CT’ odds ratio (OR) = 1.45, 95%CI: 1.40–1.50; ‘4–5 CT’ OR = 1.48, 95%CI: 1.38–1.59; ‘6+CT’ OR = 1.54, 95%CI: 1.41–1.69. In addition, male sex, higher age categories, and specific body regions were positively associated with increased numbers of CT examinations. Conclusion: We observed a positive association between regional deprivation and CT numbers in non-cancer pediatric patients. Limitations of the ecological approach and the lack of differentiation of CT details have to be acknowledged. More information on CT indications is necessary for a full assessment of this finding. In addition, further work on ways to assess socio-economic status more accurately may be required. [ABSTRACT FROM AUTHOR]

Background: Tumor hypoxia is associated with treatment resistance to cancer therapies. Hypoxia can be investigated by immunohistopathologic methods but such procedure is invasive. A non-invasive method to interrogate tumor hypoxia is an attractive option as such method can provide information before, during, and after treatment for personalized therapies. Our study evaluated the correlations between computed tomography (CT) perfusion parameters and immunohistopathologic measurement of tumor hypoxia. Methods: Wistar rats, 18 controls and 19 treated with stereotactic radiosurgery (SRS), implanted with the C6 glioma tumor were imaged using CT perfusion on average every five days to monitor tumor growth. A final CT perfusion scan and the brain were obtained on average 14 days (8–22 days) after tumor implantation. Tumor hypoxia was detected immunohistopathologically with pimonidazole. The tumor, necrotic, and pimonidazole-positive areas on histology samples were measured. Percent necrotic area and percent hypoxic areas were calculated. Tumor volume (TV), blood flow (BF), blood volume (BV), and permeability-surface area product (PS) were obtained from the CT perfusion studies. Correlations between CT perfusion parameters and histological parameters were assessed by Spearman’s ρ correlation. A Bonferroni-corrected P value < 0.05 was considered significant. Results: BF and BV showed significant correlations with percent hypoxic area ρ = -0.88, P < 0.001 and ρ = -0.81, P < 0.001, respectively, for control animals and ρ = -0.7, P < 0.001 and ρ = -0.6, P = 0.003, respectively, for all animals, while TV and BV were correlated (ρ = -0.64, P = 0.01 and ρ = -0.43, P = 0.043, respectively) with percent necrotic area. PS was not correlated with either percent necrotic or percent hypoxic areas. Conclusions: Percent hypoxic area provided significant correlations with BF and BV, suggesting that CT perfusion parameters are potential non-invasive imaging biomarkers of tumor hypoxia. [ABSTRACT FROM AUTHOR]

Introduction: Patient management following elective cranial surgery often includes routine postoperative computed tomography (CT). We analyzed whether a regime of early extubation and close neurological monitoring without routine CT is safe, and compared the rate of postoperative emergency neurosurgical intervention with published data. Methods: Four hundred ninety-two patients were prospectively analyzed; 360 had supra- and 132 had infratentorial lesions. Extubation within one hour after skin closure was aimed for in all cases. CT was performed within 48 hours only in cases of unexpected neurological findings. Results: Four-hundred sixty-nine of the 492 patients (95.3%) were extubated within one hour, 20 (4.1%) within 3 hours, and three (0.6%) within 3 to 10 hours. Emergency CT within 48 hours was performed for 43/492 (8.7%) cases. Rate of recraniotomy within 48 hours for patients with postoperative hemorrhage was 0.8% (n = 4), and 0.8% (n = 4) required placement of an external ventricular drain (EVD). Of 469 patients extubated within one hour, 3 required recraniotomy and 2 required EVD placements. Of 23 patients with delayed extubation, 1 recraniotomy and 2 EVDs were required. Failure to extubate within one hour was associated with a significantly higher risk of surgical intervention within 48 hours (rate 13.0%, p = 0.004, odds ratio 13.9, 95% confidence interval [3.11–62.37]). Discussion: Early extubation combined with close neurological monitoring is safe and omits the need for routine postoperative CT. Patients not extubated within one hour do need early CT, since they had a significantly increased risk of requiring emergency neurosurgical intervention. Trial Registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Objective: We evaluated postoperative changes in nasal cavity volume and their effects on nasal function and symptoms after endoscopic endonasal transsphenoidal approach for antero-central skull base surgery. Study Design: Retrospective chart review at a tertiary referral center. Methods: We studied 92 patients who underwent binostril, four-hand, endoscopic endonasal transsphenoidal approach surgery using the bilateral modified nasoseptal rescue flap technique. Pre- and postoperative paranasal computed tomography and the Mimics® program were used to assess nasal cavity volume changes at three sections. We also performed several pre- and postoperative tests, including the Connecticut Chemosensory Clinical Research Center test, Cross-Cultural Smell Identification Test, Nasal Obstruction Symptoms Evaluation, and Sino-Nasal Outcome Test-20. In addition, a visual analog scale was used to record subjective symptoms. We compared these data with the pre- and postoperative nasal cavity volumes. Results: Three-dimensional, objective increases in nasal passage volumes were evident between the inferior and middle turbinates (p<0.001) and between the superior turbinate and choana (p = 0.006) postoperatively. However, these did not correlate with subjectively assessed symptoms (NOSE, SNOT-20 and VAS; all nasal cavity areas; p≥0.05) or olfactory dysfunction (CCCRC and CCSIT test; all nasal cavity areas; p≥0.05). Conclusion: Skull base tumor surgery via an endoscopic endonasal transsphenoidal approach altered the patients’ nasal anatomy, but the changes in nasal cavity volumes did not affect nasal function or symptoms. These results will help surgeons to appropriately expose the surgical field during an endoscopic endonasal transsphenoidal approach. [ABSTRACT FROM AUTHOR]

With advances in therapeutic instruments and techniques, three-dimensional dose delivery has been widely used in radiotherapy. The verification of dose distribution in a small field becomes critical because of the obvious dose gradient within the field. The study investigates the dose distributions of various field sizes by using NIPAM polymer gel dosimeter. The dosimeter consists of 5% gelatin, 5% monomers, 3% cross linkers, and 5 mM THPC. After irradiation, a 24 to 96 hour delay was applied, and the gel dosimeters were read by a cone beam optical computed tomography (optical CT) scanner. The dose distributions measured by the NIPAM gel dosimeter were compared to the outputs of the treatment planning system using gamma evaluation. For the criteria of 3%/3 mm, the pass rates for 5 × 5, 3 × 3, 2 × 2, 1 × 1, and 0.5 × 0.5 cm2 were as high as 91.7%, 90.7%, 88.2%, 74.8%, and 37.3%, respectively. For the criteria of 5%/5 mm, the gamma pass rates of the 5 × 5, 3 × 3, and 2 × 2 cm2 fields were over 99%. The NIPAM gel dosimeter provides high chemical stability. With cone-beam optical CT readouts, the NIPAM polymer gel dosimeter has potential for clinical dose verification of small-field irradiation. [ABSTRACT FROM AUTHOR]

Purpose: The five point Deauville (D) scale is widely used to assess interim PET metabolic response to chemotherapy in Hodgkin lymphoma (HL) patients. An International Validation Study reported good concordance among reviewers in ABVD treated advanced stage HL patients for the binary discrimination between score D1,2,3 and score D4,5. Inter-reader reliability of the whole scale is not well characterised. Methods: Five international expert readers scored 100 interim PET/CT scans from paediatric HL patients. Scans were acquired in 51 European hospitals after two courses of OEPA chemotherapy (according to the EuroNet-PHL-C1 study). Images were interpreted in direct comparison with staging PET/CTs. Results: The probability that two random readers concord on the five point D score of a random case is only 42% (global kappa = 0.24). Aggregating to a three point scale D1,2 vs. D3 vs. D4,5 improves concordance to 60% (kappa = 0.34). Concordance if one of two readers assigns a given score is 70% for score D1,2 only 36% for score D3 and 64% for D4,5. Concordance for the binary decisions D1,2 vs. D3,4,5 is 67% and 86% for D1,2,3 vs D4,5 (kappa = 0.36 resp. 0.56). If one reader assigns D1,2,3 concordance probability is 92%, but only 64% if D4,5 is called. Discrepancies occur mainly in mediastinum, neck and skeleton. Conclusion: Inter-reader reliability of the five point D-scale is poor in this interobserver analysis of paediatric patients who underwent OEPA. Inter-reader variability is maximal in cases assigned to D2 or D3. The binary distinction D1,2,3 versus D4,5 is the most reliable criterion for clinical decision making. [ABSTRACT FROM AUTHOR]

Purpose: We compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters. Materials and Methods: A total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation. Results: SUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold. Conclusions: Our results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods. [ABSTRACT FROM AUTHOR]

Background: Perihematomal edema contributes to secondary brain injury in the course of intracerebral hemorrhage. The effect of decompressive surgery on perihematomal edema after intracerebral hemorrhage is unknown. This study analyzed the course of PHE in patients who were or were not treated with decompressive craniectomy. Methods: More than 100 computed tomography images from our published cohort of 25 patients were evaluated retrospectively at two university hospitals in Switzerland. Computed tomography scans covered the time from admission until day 100. Eleven patients were treated by decompressive craniectomy and 14 were treated conservatively. Absolute edema and hematoma volumes were assessed using 3-dimensional volumetric measurements. Relative edema volumes were calculated based on maximal hematoma volume. Results: Absolute perihematomal edema increased from 42.9 ml to 125.6 ml (192.8%) after 21 days in the decompressive craniectomy group, versus 50.4 ml to 67.2 ml (33.3%) in the control group (Δ at day 21 = 58.4 ml, p = 0.031). Peak edema developed on days 25 and 35 in patients with decompressive craniectomy and controls respectively, and it took about 60 days for the edema to decline to baseline in both groups. Eight patients (73%) in the decompressive craniectomy group and 6 patients (43%) in the control group had a good outcome (modified Rankin Scale score 0 to 4) at 6 months (P = 0.23). Conclusions: Decompressive craniectomy is associated with a significant increase in perihematomal edema compared to patients who have been treated conservatively. Perihematomal edema itself lasts about 60 days if it is not treated, but decompressive craniectomy ameliorates the mass effect exerted by the intracerebral hemorrhage plus the perihematomal edema, as reflected by the reduced midline shift. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the role of functional visceral fat activity assessed by preoperative F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in colorectal cancer (CRC) for predicting regional lymph node (LN) or distant metastasis. Method: We evaluated 131 patients with newly diagnosed CRC. They all underwent pre-operative 18F-FDG PET/CT and surgery. Functional fat activity was measured by maximum standardized uptake value (SUVmax) using 18F-FDG PET/CT. Functional visceral fat activity was measured by SUVmax of visceral fat/SUVmax of subcutaneous fat (V/S) ratio. Mann-Whitney U test, χ2 test, Fisher’s exact test, receiver-operating characteristic (ROC) analysis, Spearrman’s correlation coefficient, and uni- and multivariate logistic regression statistical analyses were done. Results: Patients with higher V/S ratio displayed a significantly higher rate of regional LN (p = 0.004) and distant metastasis (p<0.001). In addition, V/S ratio was the only factor that was significantly associated with distant metastasis. An optimal cut-off V/S ratio of 1.88 was proposed for predicting distant metastasis with a sensitivity of 84.6% and specificity of 78.8% (area under the curve: 0.86; p<0.0001) Conclusion: Functional visceral fat activity is significantly associated with distant metastasis in CRC patients. Furthermore, V/S ratio can be useful as a complementary factor in predicting distant metastasis. [ABSTRACT FROM AUTHOR]

Purpose: To assess the measurement variability of subsolid nodules (SSNs) in follow-up situations and to compare the degree of variability between measurement metrics. Methods: Two same-day repeat-CT scans of 69 patients (24 men and 45 women) with 69 SSNs were randomly assigned as initial or follow-up scans and were read by the same (situation 1) or different readers (situation 2). SSN size and solid portion size were measured in both situations. Measurement variability was calculated and coefficients of variation were used for comparisons. Results: Measurement variability for the longest and average diameter of SSNs was ±1.3 mm (±13.0%) and ±1.3 mm (±14.4%) in situation 1, and ±2.2 mm (±21.0%) and ±2.1 mm (±21.3%) in situation 2, respectively. For solid portion, measurement variability on lung and mediastinal windows was ±1.2 mm (±27.1%) and ±0.8 mm (±24.0%) in situation 1, and ±3.7 mm (±61.0%) and ±1.5 mm (±47.3%) in situation 2, respectively. There were no significant differences in the degree of variability between the longest and average diameters and between the lung and mediastinal window settings (p>0.05). However, measurement variability significantly increased when the follow-up and initial CT readers were different (p<0.001). Conclusions: A cutoff of ±2.2 mm can be reliably used to determine true nodule growth on follow-up CT. Solid portion measurements were not reliable in evaluating SSNs’ change when readers of initial and follow-up CT were different. [ABSTRACT FROM AUTHOR]

Objectives: To assess the diagnostic value of dual energy spectral CT imaging for colorectal cancer grading using the quantitative iodine density measurements in both arterial phase (AP) and venous phase (VP). Methods: 81 colorectal cancer patients were divided into two groups based on their pathological findings: a low grade group including well (n = 13) and moderately differentiated cancer (n = 24), and a high grade group including poorly differentiated (n = 42) and signet ring cell cancer (n = 2). Iodine density (ID) in the lesions was derived from the iodine-based material decomposition (MD) image and normalized to that in the psoas muscle to obtain normalized iodine density (NID). The difference in ID and NID between AP and VP was calculated. Results: The ID and NID values of the low grade cancer group were, 14.65±3.38mg/mL and 1.70±0.33 in AP, and 21.90±3.11mg/mL and 2.05± 0.32 in VP, respectively. The ID and NID values for the high grade cancer group were 20.63±3.72mg/mL and 2.95±0.72 in AP, and 26.27±3.10mg/mL and 3.51±1.12 in VP, respectively. There was significant difference for ID and NID between the low grade and high grade cancer groups in both AP and VP (all p<0.001). ROC analysis indicated that NID of 1.92 in AP provided 70.3% sensitivity and 97.7% specificity in differentiating low grade cancer from high grade cancer. Conclusions: The quantitative measurement of iodine density in AP and VP can provide useful information to differentiate low grade colorectal cancer from high grade colorectal cancer with NID in AP providing the greatest diagnostic value. [ABSTRACT FROM AUTHOR]

Multipuncture radiofrequency ablation is expected to produce a large ablated area and reduce intrahepatic recurrence of hepatocellular carcinoma; however, it requires considerable skill. This study evaluated the utility of a new simulator system for multipuncture radiofrequency ablation. To understand positioning of multipuncture electrodes on three-dimensional images, we developed a new technology by expanding real-time virtual ultrasonography. We performed 21 experimental punctures in phantoms. Electrode insertion directions and positions were confirmed on computed tomography, and accuracy and utility of the simulator system were evaluated by measuring angles and intersections for each electrode. Moreover, to appropriately assess placement of the three electrodes, puncture procedures with or without the simulator were performed by experts and non-experts. Technical success was defined as maximum angle and distance ratio, as calculated by maximum and minimum distances between electrodes. In punctures using 2 electrodes, correlations between angles on each imaging modality were strong (ultrasound vs. simulator: r = 0.991, p<0.001, simulator vs. computed tomography: r = 0.991, p<0.001, ultrasound vs. computed tomography: r = 0.999, p<0.001). Correlations between distances in each imaging modality were also strong (ultrasound vs. simulator: r = 0.993, p<0.001; simulator vs. computed tomography: r = 0.994, p<0.001; ultrasound vs. computed tomography: r = 0.994, p<0.001). In cases with 3 electrodes, distances between each electrode correlated strongly (yellow-labeled vs. red-labeled: r = 0.980, p<0.001; red-labeled vs. blue-labeled: r = 0.953, p<0.001; yellow-labeled vs. blue-labeled: r = 0.953, p<0.001). Both angle and distance ratio (expert with simulator vs. without simulator; p = 0.03, p = 0.02) were significantly smaller in procedures performed by experts using the simulator system. The new simulator system appears to accurately guide electrode positioning. This simulator system could allow multipuncture radiofrequency ablation to be performed more effectively and comfortably. [ABSTRACT FROM AUTHOR]

Background: Skeletal muscle depletion (sarcopenia) is closely associated with limited physical ability and high mortality. This study evaluated the prognostic significance of skeletal muscle status before and after chemotherapy in patients with unresectable colorectal cancer (CRC). Methods: We conducted a retrospective analysis of 215 consecutive patients with unresectable CRC who underwent systemic chemotherapy. Skeletal muscle cross-sectional area was measured by computed tomography. We evaluated the prognostic value of skeletal muscle mass before chemotherapy and the rate of skeletal muscle change in cross-sectional area after chemotherapy. Results: One-hundred-eighty-two patients met our inclusion criteria. There were no significant differences in progression-free survival (PFS) or overall survival (OS) associated with skeletal muscle mass before chemotherapy. However, 22 patients with skeletal muscle loss (>5%) after chemotherapy showed significantly shorter PFS and OS compared with those without skeletal muscle loss (PFS, log-rank p = 0.029; OS, log-rank p = 0.009). Multivariate Cox regression analysis revealed that skeletal muscle loss after chemotherapy (hazard ratio, 2.079; 95% confidence interval, 1.194–3.619; p = 0.010) was independently associated with OS. Conclusions: Skeletal muscle loss after chemotherapy was an independent, negative prognostic factor in unresectable CRC. [ABSTRACT FROM AUTHOR]

Purpose: We evaluated the effects of respiratory gating on treatment accuracy in lung cancer patients undergoing lung stereotactic body radiotherapy by using electronic portal imaging device (EPID) images. Materials and Methods: Our study population consisted of 30 lung cancer patients treated with stereotactic body radiotherapy (48 Gy/4 fractions/4 to 9 days). Of these, 14 were treated with- (group A) and 16 without gating (group B); typically the patients whose tumors showed three-dimensional respiratory motion ≧5 mm were selected for gating. Tumor respiratory motion was estimated using four-dimensional computed tomography images acquired during treatment simulation. Tumor position variability during all treatment sessions was assessed by measuring the standard deviation (SD) and range of tumor displacement on EPID images. The two groups were compared for tumor respiratory motion and position variability using the Mann-Whitney U test. Results: The median three-dimensional tumor motion during simulation was greater in group A than group B (9 mm, range 3–30 mm vs. 2 mm, range 0–4 mm; p<0.001). In groups A and B the median SD of the tumor position was 1.1 mm and 0.9 mm in the craniocaudal- (p = 0.24) and 0.7 mm and 0.6 mm in the mediolateral direction (p = 0.89), respectively. The median range of the tumor position was 4.0 mm and 3.0 mm in the craniocaudal- (p = 0.21) and 2.0 mm and 1.5 mm in the mediolateral direction (p = 0.20), respectively. Conclusions: Although patients treated with respiratory gating exhibited greater respiratory tumor motion during treatment simulation, tumor position variability in the EPID images was low and comparable to patients treated without gating. This demonstrates the benefit of respiratory gating. [ABSTRACT FROM AUTHOR]

Background: Early detection of lung cancer is crucial as the prognosis depends on the disease stage. Chest radiographs has been the principal diagnostic tool for general practitioners (GPs), but implies a potential risk of false negative results, while computed tomography (CT) has a higher sensitivity. The aim of this study was to describe the implementation of direct access to low-dose CT (LDCT) from general practice. Methods: We conducted a cohort study nested in a randomised study. A total of 119 general practices with 266 GPs were randomised into two groups. Intervention GPs were offered direct access to chest LDCT combined with a Continuing Medical Education (CME) meeting on lung cancer diagnosis. Results: During a 19-month period, 648 patients were referred to LDCT (0.18/1000 adults on GP list/month). Half of the patients needed further diagnostic work-up, and 15 (2.3%, 95% CI: 1.3–3.8%) of the patients had lung cancer; 60% (95% CI: 32.3–83.7%) in a localised stage. The GP referral rate was 61% higher for CME participants compared to non-participants. Conclusion: Of all patients referred to LDCT, 2.3% were diagnosed with lung cancer with a favourable stage distribution. Half of the referred patients needed additional diagnostic work-up. There was an association between participation in CME and use of CT scan. The proportion of cancers diagnosed through the usual fast-track evaluation was 2.2 times higher in the group of CME-participating GPs. The question remains if primary care case-finding with LDCT is a better option for patients having signs and symptoms indicating lung cancer than a screening program. Whether open access to LDCT may provide earlier diagnosis of lung cancer is yet unknown and a randomised trial is required to assess any effect on outcome. Trial Registration: Clinicaltrials.gov [ABSTRACT FROM AUTHOR]

Objectives: To compare 64-slice contrast-enhanced computed tomography (CT) with 3-Tesla magnetic resonance imaging (MRI) using Gd-EOB-DTPA for the diagnosis of hepatocellular carcinoma (HCC) and evaluate the utility of diffusion-weighted imaging (DWI) in this setting. Methods: 3-phase-liver-CT was performed in fifty patients (42 male, 8 female) with suspected or proven HCC. The patients were subjected to a 3-Tesla-MRI-examination with Gd-EOB-DTPA and diffusion weighted imaging (DWI) at b-values of 0, 50 and 400 s/mm2. The apparent diffusion coefficient (ADC)-value was determined for each lesion detected in DWI. The histopathological report after resection or biopsy of a lesion served as the gold standard, and a surrogate of follow-up or complementary imaging techniques in combination with clinical and paraclinical parameters was used in unresected lesions. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values were evaluated for each technique. Results: MRI detected slightly more lesions that were considered suspicious for HCC per patient compared to CT (2.7 versus 2.3, respectively). ADC-measurements in HCC showed notably heterogeneous values with a median of 1.2±0.5×10−3 mm2/s (range from 0.07±0.1 to 3.0±0.1×10−3 mm2/s). MRI showed similar diagnostic accuracy, sensitivity, and positive and negative predictive values compared to CT (AUC 0.837, sensitivity 92%, PPV 80% and NPV 90% for MRI vs. AUC 0.798, sensitivity 85%, PPV 79% and NPV 82% for CT; not significant). Specificity was 75% for both techniques. Conclusions: Our study did not show a statistically significant difference in detection in detection of HCC between MRI and CT. Gd-EOB-DTPA-enhanced MRI tended to detect more lesions per patient compared to contrast-enhanced CT; therefore, we would recommend this modality as the first-choice imaging method for the detection of HCC and therapeutic decisions. However, contrast-enhanced CT was not inferior in our study, so that it can be a useful image modality for follow-up examinations. [ABSTRACT FROM AUTHOR]