Standard CT Images Research Articles

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?

To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686–0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646–0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656–0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.

Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.

To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures. Standard CT scans of 58 patients with insufficiency fractures of the spine, performed between 2006 and 2013, were analyzed retrospectively. Every included patient had at least two CT scans. Intact vertebrae in a first scan that either fractured ("unstable") or remained intact ("stable") in the consecutive scan were manually segmented on mid-sagittal reformations. TA features for all vertebrae were extracted using open-source software (MaZda). In a paired control study, all vertebrae of the study cohort "cases" and matched controls were classified using ROC analysis of Hounsfield unit (HU) measurements and supervised ML techniques. In a within-subject vertebra comparison, vertebrae of the cases were classified into "unstable" and "stable" using identical techniques. One hundred twenty vertebrae were included. Classification of cases/controls using ROC analysis of HU measurements showed an AUC of 0.83 (95% confidence interval [CI], 0.77-0.88), and ML-based classification showed an AUC of 0.97 (CI, 0.97-0.98). Classification of unstable/stable vertebrae using ROC analysis showed an AUC of 0.52 (CI, 0.42-0.63), and ML-based classification showed an AUC of 0.64 (CI, 0.61-0.67). TA combined with ML allows to identifying patients who will suffer from vertebral insufficiency fractures in standard CT scans with high accuracy. However, identification of single vertebra at risk remains challenging. • Bone texture analysis combined with machine learning allows to identify patients at risk for vertebral body insufficiency fractures on standard CT scans with high accuracy. • Compared to mere Hounsfield unit measurements on CT scans, application of bone texture analysis combined with machine learning improve fracture risk prediction. • This analysis has the potential to identify vertebrae at risk for insufficiency fracture and may thus increase diagnostic value of standard CT scans.

Virtual Noncalcium Dual-Energy CT: Detection of Lumbar Disk Herniation in Comparison with Standard Gray-scale CT.

Purpose To assess the diagnostic performance of dual-energy CT with reconstruction of virtual noncalcium (VNCa) images for the detection of lumbar disk herniation compared with standard CT image reconstruction. Materials and Methods For this retrospective study, 41 patients (243 intervertebral disks; overall mean age, 68 years; 24 women [mean age, 68 years] and 17 men [mean age, 68 years]) underwent clinically indicated third-generation, dual-source, dual-energy CT and 3.0-T MRI within 2 weeks between March 2017 and January 2018. Six radiologists, blinded to clinical and MRI information, independently evaluated conventional gray-scale dual-energy CT series for the presence and degree of lumbar disk herniation and spinal nerve root impingement. After 8 weeks, readers reevaluated examinations by using color-coded VNCa reconstructions. MRI evaluated by two separate experienced readers, blinded to clinical and dual-energy CT information, served as the standard of reference. Sensitivity and specificity were the primary metrics of diagnostic performance. Results A total of 112 herniated lumbar disks were depicted at MRI. VNCa showed higher overall sensitivity (612 of 672 [91%] vs 534 of 672 [80%]) and specificity (723 of 786 [92%] vs 665 of 786 [85%]) for detecting lumbar disk herniation compared with standard CT (all comparisons, P < .001). Interreader agreement was excellent for VNCa and substantial for standard CT (κ = 0.82 vs 0.67; P < .001). VNCa achieved superior diagnostic confidence, image quality, and noise scores compared with standard CT (all comparisons, P < .001). Conclusion Color-coded dual-energy CT virtual noncalcium reconstructions show substantially higher diagnostic performance and confidence for depicting lumbar disk herniation compared with standard CT. © RSNA, 2018.

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

Background: Directional deep brain stimulation (DBS) allows steering the stimulation in an axial direction which offers greater flexibility in programming. However, accurate anatomical visualization of the lead orientation is required for interpreting the observed stimulation effects and to guide programming. Objectives: In this study we aimed to develop and test an accurate and robust algorithm for determining the orientation of segmented electrodes based on standard postoperative CT imaging used in DBS. Methods: Orientation angles of directional leads (Cartesia^TM; Boston Scientific, Marlborough, MA, USA) were determined using CT imaging. Therefore, a sequential algorithm was developed that quantitatively compares the similarity of the observed CT artifacts with calculated artifact patterns based on the lead’s orientation marker and a geometric model of the segmented electrodes. Measurements of seven ground truth phantoms and three leads with 60 different configurations of lead implantation and orientation angles were analyzed for validation. Results: The accuracy of the determined electrode orientation angles was –0.6 ± 1.5° (range: –5.4 to 4.2°). This accuracy proved to be sufficiently high to resolve even subtle differences between individual leads. Conclusions: The presented algorithm is user independent and provides highly accurate results for the orientation of the segmented electrodes for all angular constellations that typically occur in clinical cases.

Identification and characterization of myocardial metastases in neuroendocrine tumor patients using 68Ga-DOTATATE PET-CT

BackgroundFocal 68Ga-DOTATATE PET lesions within the myocardium of neuroendocrine tumor (NET) patients are observed in clinical practice. We determined the frequency and characteristics of lesions that are consistent with cardiac metastasis and assessed the lesion detection rate of conventional imaging.Methods629 patients who underwent 68Ga-DOTATATE PET-CT at a supraregional comprehensive cancer center on NET were included from a consecutive registry. Inclusion criteria were: (1) focal 68Ga-DOTATATE tracer uptake within the myocardium in more than two sequential PET exams, and (2) contrast-enhanced CT. To determine the diagnostic accuracy of conventional CT imaging, a case-control cohort with a ratio of 1:3 was used. PET and CT were independently analyzed by two blinded readers. Cohen’s κ was assessed for interreader agreement. Descriptive statistics were applied for frequencies and characteristics and group comparisons were analyzed using the Fisher’s exact test.ResultsThe prevalence of myocardial metastases related to the registry was 2.4% (15 of 629 NET patients fulfilling the inclusion criteria), for a total of 21 myocardial 68Ga-DOTATATE foci detected. Myocardial lesions were most frequently located in the left ventricle (43%) and the septum (43%). No patient demonstrated a pericardial effusion. Patients with myocardial metastases did not differ in demographics, tumor grading, disease stage or circulating tumor markers compared to the overall registry (all p > 0.05). Higher Ki67-Indices were observed (p = 0.049) for patients with myocardial metastases. Interreader agreement for PET assessment was excellent (Cohen’s κ = 1.0). CT reading showed a sensitivity of 19% (95% confidence interval: 6–43%) at a specificity of 100% (95% confidence interval: 90–100%).Conclusions68Ga-DOTATATE PET enables detection of myocardial metastatic lesions in NET patients. In contrast, standard morphologic CT imaging provides very limited sensitivity.

Evaluation of Radiation Dose and Image Quality using High-Pitch 70-kV Chest CT in Immunosuppressed Patients.

The aim of the study was to evaluate high-pitch 70-kV CT examinations of the thorax in immunosuppressed patients regarding radiation dose and image quality in comparison with 120-kV acquisition. The image data from 40 patients (14 women and 26 men; mean age: 40.9 ± 15.4 years) who received high-pitch 70-kV CT chest examinations were retrospectively included in this study. A control group (n = 40), matched by age, gender, BMI, and clinical inclusion criteria, had undergone standard 120-kV chest CT imaging. All CT scans were performed on a third-generation dual-source CT unit. For an evaluation of the radiation dose, the CT dose index (CTDIvol), dose-length product (DLP), effective dose (ED), and size-specific dose estimates (SSDE) were analyzed in each group. The objective image quality was evaluated using signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Three blinded and independent radiologists evaluated subjective image quality and diagnostic confidence using 5-point Likert scales. The mean dose parameters were significantly lower for high-pitch 70-kV CT examinations (CTDIvol, 2.9 ± 0.9 mGy; DLP, 99.9 ± 31.0 mGyxcm; ED, 1.5 ± 0.6 mSv; SSDE, 3.8 ± 1.2 mGy) compared to standard 120-kV CT imaging (CTDIvol, 8.8 ± 3.7mGy; DLP, 296.6 ± 119.3 mGyxcm; ED, 4.4 ± 2.1 mSv; SSDE, 11.6 ± 4.4 mGy) (P≤ 0.001). The objective image parameters (SNR: 7.8 ± 2.1 vs. 8.4 ± 1.8; CNR: 7.7 ± 2.4 vs. 8.3 ± 2.8) (P≥ 0.065) and the cumulative subjective image quality (4.5 ± 0.4 vs. 4.7 ± 0.3) (p = 0.052) showed no significant differences between the two protocols. High-pitch 70-kV thoracic CT examinations in immunosuppressed patients resulted in a significantly reduced radiation exposure compared to standard 120-kV CT acquisition without a decrease in image quality. · Third-generation dual-source CT units enable high-pitch 70-kV CT examinations of the chest.. · High-pitch 70-kV CT examinations show a significantly reduced radiation dose compared to standard 120-kV CT examinations.. · High-pitch 70-kV CT examinations of the chest show comparable objective and subjective image quality.. · Subjectively deteriorated image noise and sharpness of 70-kV CT did not impact diagnostic confidence.. · Yel I, Martin SS, Wichmann JL et al. Evaluation of Radiation Dose and Image Quality using High-Pitch 70-kV Chest CT in Immunosuppressed Patients . Fortschr Röntgenstr 2019; 191: 122 - 129.

Single-energy metal artifact reduction technique for reducing metallic coil artifacts on post-interventional cerebral CT and CT angiography.

To evaluate the effects of the single-energy metal artifact reduction (SEMAR) algorithm on image quality of cerebral CT and CT angiography (CTA) for patients who underwent intracranial aneurysm coiling. Twenty patients underwent cerebral CT and CTA using a 320-detector row CT after intracranial aneurysm coiling. Images with and without application of the SEMAR algorithm (SEMAR CT and standard CT images, respectively) were reconstructed for each patient. The images were qualitatively assessed by two independent radiologists in a blinded manner for the depiction of anatomical structures around the coil, delineation of the arteries around the coil, and the depiction of the status of coiled aneurysms. Artifact strength was quantitatively assessed by measuring the standard deviation of attenuation values around the coil. The strength of artifacts measured in SEMAR CT images was significantly lower than that in standard CT images (25.7 ± 10.2 H.U. vs. 80.4 ± 67.2 H.U., p < 0.01, Student's paired t test). SEMAR CT images were significantly improved compared with standard CT images in the depiction of anatomical structures around the coil (p < 0.01, the sign test), delineation of the arteries around the coil (p < 0.01), and the depiction of the status of coiled aneurysms (p < 0.01). The SEMAR algorithm significantly reduces metal artifacts from intracranial aneurysm coiling and improves visualization of anatomical structures and arteries around the coil, and depiction of the status of coiled aneurysms on post-interventional cerebral CT.

Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer

Background: Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy.Material and methods: Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed.Results: In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61–0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47–0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54–0.71) and 0.62 (95%CI 0.49–0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor.Conclusions: A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.

Indexed Left Atrial Adipose Tissue Area Is Associated With Severity of Atrial Fibrillation and Atrial Fibrillation Recurrence Among Patients Undergoing Catheter Ablation.

Background: Epicardial adipose tissue (EAT) has been associated with adverse left atrial (LA) remodeling and atrial fibrillation (AF) outcomes, possibly because of paracrine signaling.Objectives: We examined factors associated with a novel measure of EAT i.e., indexed LAEAT (iLAEAT) and its prognostic significance after catheter ablation (CA) of atrial fibrillation (AF).Methods: We performed a retrospective analysis of 274 participants with AF referred for CA. LAEAT area was measured from a single pre-ablation CT image and indexed to body surface area (BSA) to calculate iLAEAT. Clinical, echocardiographic data and 1-year AF recurrence rates after CA were compared across tertiles of iLAEAT. We performed logistic regression analysis adjusting for factors associated with AF to examine relations between iLAEAT and AF recurrence.Results: Mean age of participants was 61 ± 10 years, 136 (49%) were women, mean BMI was 32 ± 9 kg/m2 and 85 (31%) had persistent AF. Mean iLAEAT was 0.82 ± 0.53 cm2/m2. Over 12-months, 109 (40%) had AF recurrence. Participants in the highest iLAEAT tertile were older, had higher CHA2DS2VASC scores, more likely to be male, have greater LA volume, and were more likely to have persistent (vs. paroxysmal) type AF than participants in the lowest iLAEAT tertile (p for all < 0.05). In regression analyses, iLAEAT was associated with higher odds of AF recurrence (OR = 2.93; 95% CI 1.34–6.43).Conclusions: iLAEAT can quantify LA adipose tissue burden using standard CT images. It is strongly associated with AF risk factors and outcomes, supporting the hypothesis that EAT plays a role in the pathophysiology of AF.

Detection of distant metastasis and prognostic prediction of recurrent salivary gland carcinomas using 18F‐FDG PET/CT

To compare the diagnostic accuracy of 18 F-FDG PET/CT and conventional contrast-enhanced CT for the re-staging of recurrent salivary gland carcinoma (SGC). This study included 58 consecutive patients who underwent recurrent SGCs after definitive treatment. The recurrences were evaluated by 18 F-FDG PET/CT and contrast-enhanced CT of the neck and chest. McNemar's test was used to compare the diagnostic accuracy of 18 F-FDG PET/CT with standard neck and chest CT imaging, and a Cox proportional hazards model was used to assess the prognostic value of PET/CT. Of 58 patients with recurrent SGCs, 17 (29%) had a local recurrence, 17 (29%) had a regional recurrence, and 38 (66%) had a distant metastasis, with these classifications showing overlap. The sensitivity and accuracy of 18 F-FDG PET/CT for the detection of distant metastases were significantly higher than those of CT (p<0.05), whereas, for detection of loco-regional recurrences, they did not differ (p>0.1). The 18 F-FDG PET/CT-positive findings at distant sites were predictors of poor progression-free and overall survival outcome (all p<0.05). 18 F-FDG PET/CT is a more effective method than CT for detecting distant site recurrences of SGC. This may lead to prognostic prediction for recurrent SGCs.

Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study.

Objectives:Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC.Methods:Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan–Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions.Results:The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets. There was a consistent and significant split between survival curves with HPV status determined by p16 [p = 0.007; hazard ratio (HR): 0.46], Mall (p = 0.036; HR: 0.55) and Mno art (p = 0.027; HR: 0.49).Conclusion:This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status.Advances in knowledge:Radiomics has the potential to identify clinically relevant molecular phenotypes.

Arms Down Cone Beam CT Hepatic Angiography Performance Assessment: Vascular Imaging Quality and Imaging Artifacts

The practice of positioning patients' arms above the head during catheter-injected hepatic arterial phase cone beam CT (A-CBCT) imaging has been inherited from standard CT imaging due to image quality concerns, but interrupts workflow and extends procedure time. We sought to assess A-CBCT image quality and artifacts with arms extended above the head versus down by the side. We performed an IRB approved retrospective evaluation of reformatted and 3D-volume rendered images from 91 consecutive A-CBCTs (43 arms up, 48 arms down) acquired during hepatic tumor arterial embolization procedures. Two interventional radiologists reviewed all A-CBCT imaging and assigned vessel visualization scores (VVS) from 1 to 5, ranging from non-diagnostic to optimal visualization. Streak artifacts across axial images were rated from 1 to 3 based on resulting image quality (none to significant). Presence of respiratory or cardiac motion during acquisition, body mass index and radiation dose area product (DAP) were also recorded and analyzed. Univariate and multivariate analyses were used to assess the impact of arm position on VVS and imaging artifacts. VVS were not significantly associated with arm position during A-CBCT imaging. One reader reported more streak artifacts across axial images in the arms down group (p=0.005). DAP was not statistically different between the groups (23.9Gycm2 [6.1-73.4] arms up, 26.1Gycm2 [4.2-102.6] arms down, p=0.54). A-CBCT angiography performed with the arms above the head is not superior for clinically relevant hepatic vascular visualization compared to imaging performed with the arms by the patient's side.

Dual-energy bone removal computed tomography (BRCT): preliminary report of efficacy of acute intracranial hemorrhage detection.

One of the major applications of dual-energy computed tomography (DECT) is automated bone removal (BR). We hypothesized that the visualization of acute intracranial hemorrhage could be improved on BRCT by removing bone as it has the highest density tissue in the head. This preliminary study evaluated the efficacy of a DE BR algorithm for the head CT of trauma patients. Sixteen patients with acute intracranial hemorrhage within 1day after head trauma were enrolled in this study. All CT examinations were performed on a dual-source dual-energy CT scanner. BRCT images were generated using the Bone Removal Application. Simulated standard CT and BRCT images were visually reviewed in terms of detectability (presence or absence) of acute hemorrhagic lesions. DECT depicted 28 epidural/subdural hemorrhages, 17 contusional hemorrhages, and 7 subarachnoid hemorrhages. In detecting epidural/subdural hemorrhage, BRCT [28/28 (100%)] was significantly superior to simulated standard CT [17/28 (61%)] (p=.001). In detecting contusional hemorrhage, BRCT [17/17 (100%)] was also significantly superior to simulated standard CT [11/17 (65%)] (p=.0092). BRCT was superior to simulated standard CT in detecting acute intracranial hemorrhage. BRCT could improve the detection of small intracranial hemorrhages, particularly those adjacent to bone, by removing bone that can interfere with the visualization of small acute hemorrhage. In an emergency such as head trauma, BRCT can be used as support imaging in combination with simulated standard CT and bone scale CT, although BRCT cannot replace a simulated standard CT.

Four-month course of adjuvant dabrafenib in patients with surgically resected stage IIIC melanoma characterized by a BRAFV600E/K mutation.

BackgroundWe tested the hypothesis that a 4-month course of adjuvant dabrafenib in stage IIIC BRAF-mutated melanoma would improve 2 year RFS from 24% to 51%, and that tumor-derived cell free DNA (cfDNA) in plasma would correlate with and predict recurrence.MethodsPatients with stage IIIC BRAF V600E/K mutated melanoma who were free of disease after surgical resection received 4 months of adjuvant dabrafenib. Patients were evaluated with imaging at baseline, at the end of cycles 2, 4, 6, then every 3 months until disease relapse or 2 years, whichever came first. Serial blood samples were collected for evaluation of cfDNA at the same time.Results21/23 patients enrolled were evaluable; 2 patients withdrew consent during the first week of treatment. The 2 year RFS was 28.6% (95% CI 12-48%). The estimated overall survival at 2 years was 78% (95% CI 51-91%). cfDNA detection had a 53% sensitivity in relapsing patients but cfDNA detection did not provide lead-time advantage over CT scanning.ConclusionA 4-month course of adjuvant dabrafenib did not result in a detectable improvement in 2-year RFS. cfDNA was less sensitive than standard CT imaging and did not provide a lead-time advantage in detecting relapse.

Imaging: Perivascular fat - an unheralded informant of coronary inflammation.

A novel image-analysis method using standard CT imaging demonstrates that changes in the CT signal of coronary perivascular adipose tissue are associated with the degree of inflammation in adjacent coronary plaques. This exciting development might become a useful adjuvant clinical tool, although further validation and prospective outcome studies are required.

Utility of PET/CT in primary identification and management of cancers of unknown primary.

e18219 Background: The role of PET/CT in staging and management of cancers of unknown primary (CUP) is poorly defined. Our study sought to evaluate PET/CT versus standard CT in determining the primary site, lymph node status and solid organ metastases, and assess whether results of PET scans altered management of patients with CUP. Methods: We conducted a retrospective chart review of CUP patients to measure concordance of PET/CT and CT scans in patients diagnosed with CUP at two Western Canadian academic centres; the Cross Cancer Institute and BC Cancer Agencies from 2008 to 2015. Results: We identified 244 patients who had standard CT and PET/CT during their work-up. Of the 70 patients whose primary identification status was recorded, standard CT imaging detected 16.1% of primaries compared to 12.1% on PET/CT. 63.3% of primaries were concordantly identified on both modalities. PET/CT identified 2 primaries not seen on CT whereas standard CT identified 9 primaries not seen on PET/CT. Concordance rates between PET/CTs and standard CTs were 77.9% and 93.9% regarding lymph node status and extent of metastases, respectively. PET/CT identified a greater extent lymph node involvement in patients; 93.8% versus 79.0%. (CCOR = 2.92, 95% CI = 0.87-9.72) and identified a greater extent of solid organ involvement; 96.5% versus 92.4%. (CCOR = 45.25, 95% CI = 7.79-262.78). Data from the CCI of patients who had discordant imaging in either domain or identification of primary showed no cases in which PET/CT alone provided information that changed patient management. Conclusions: Our results suggest PET/CT scans do not provide additional diagnostic information with respect to identifying the primary site but may be superior in clarifying extent of metastases and nodal status though this did not alter therapy. Future studies investigating the impact of PET/CT scans on management and cost effectiveness in CUP are critical.

EP-1608: Deriving HPV status from standard CT imaging: a radiomic approach with independent validation

EP-1608: Deriving HPV status from standard CT imaging: a radiomic approach with independent validation

PD46-12 VIRTUAL SIMULATION IMPROVES A NOVICE′S ABILITY TO LOCALIZE RENAL TUMORS IN 3D PHYSICAL MODELS – A MULTI-INSTITUTIONAL PROSPECTIVE RANDOMIZED CONTROLLED STUDY

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment III1 Apr 2017PD46-12 VIRTUAL SIMULATION IMPROVES A NOVICE′S ABILITY TO LOCALIZE RENAL TUMORS IN 3D PHYSICAL MODELS – A MULTI-INSTITUTIONAL PROSPECTIVE RANDOMIZED CONTROLLED STUDY Arun Rai, Jason Scovell, Adithya Balasubramanian, Ang Xu, Ryan Siller, Taylor P. Kohn, Young Moon, Naveen Yadav, and Richard Link Arun RaiArun Rai More articles by this author , Jason ScovellJason Scovell More articles by this author , Adithya BalasubramanianAdithya Balasubramanian More articles by this author , Ang XuAng Xu More articles by this author , Ryan SillerRyan Siller More articles by this author , Taylor P. KohnTaylor P. Kohn More articles by this author , Young MoonYoung Moon More articles by this author , Naveen YadavNaveen Yadav More articles by this author , and Richard LinkRichard Link More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2384AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Efficient robotic partial nephrectomy requires a precise understanding of tumor location and relationship to vital structures. Translating standard imaging into a reliable 3D mental model is challenging, especially for inexperienced surgeons. We sought to determine if renal tumor visualization and manipulation within a robotic virtual simulator improves the ability of novices to accurately identify tumor location in 3D space. METHODS We recruited medical students from Baylor College of Medicine and UT McGovern Medical School. Using a custom-built algorithm, two volumetric reconstructions from CT imaging were generated and imported into the dV-Trainer. For each tumor, 9 different model variations were 3D printed (1 real, 8 with modified tumor locations). Subjects were randomized 1:1 into two groups, dV-Trainer and non dV-Trainer, and were given 5 minutes to review CT images. Subjects in the dV-Trainer group were allowed to manipulate the virtual model for an additional 5 minutes. They were then asked to identify the model corresponding to the real tumor in each case and to assign a nephrometry score. The primary outcome was distance of the tumor selected from the correct location. RESULTS 100 subjects participated and all were included for analysis. There was no difference in subject age (mean: 23.6 ± 2.2) or training year between groups. Subjects in the dV-Trainer group more accurately visualized tumor location (Normalized distance: Model 1: sim 0.17 ± 0.23 vs. no-sim 0.31 ± 0.31, p=0.012; Model 2: sim 0.12 ± 0.28 vs. no-sim 0.34 ± 0.39, p=.001). These findings were not affected by age or year of training. Surprisingly, subjects in the dV-Trainer group had more difficulty assigning the correct nephrometry score than those in the non-dV-Trainer group. CONCLUSIONS In this prospective randomized trial, exposure to a patient-specific virtual model improves the novice ability to accurately visualize tumor location when compared to interpreting standard planar CT images alone. This workflow, including our novel reconstruction algorithm, provides a streamlined method for generating patient-specific kidney anatomic simulations which may be valuable for teaching surgical trainees and visualizing complex tumor cases before surgery. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e895 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Arun Rai More articles by this author Jason Scovell More articles by this author Adithya Balasubramanian More articles by this author Ang Xu More articles by this author Ryan Siller More articles by this author Taylor P. Kohn More articles by this author Young Moon More articles by this author Naveen Yadav More articles by this author Richard Link More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

A Support Construction for CT Image Based on K-Means Clustering

Computer Tomography in medical imaging provides human internal body pictures in the digital form. The more quality images it provides, the better information we get. Normally, medical imaging can be constructed by projection data from several perspectives. In this paper, our research challenges and describes a numerical method for refining the image of a Region of Interest (ROI) by constructing support within a standard CT image. It is obvious that the quality of tomographic slice is affected by artifacts. CT using filter and K-means clustering provides a way to reconstruct an ROI with minimal artifacts and improve the degree of the spatial resolution. Experimental results are presented for improving the reconstructed images, showing that the approach enhances the overall resolution and contrast of ROI images. Our method provides a number of advantages: robustness with noise in projection data and support construction without the need to acquire any additional setup.