Search

Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.

IntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.ResultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​

Objective. To explore the clinical utility of an intervertebral motion metric by determining the proportion of patients for whom it changed their surgical treatment plan from decompression only to decompression with fusion or vice versa. Summary of Background Data. Lumbar spinal stenosis from degenerative spondylolisthesis is commonly treated with decompression only or decompression with additional instrumented fusion. An objective diagnostic tool capable of establishing abnormal motion between lumbar vertebrae to guide decision-making between surgical procedures is needed. To this end, a metric based on the vertebral sagittal plane translationper-degree-of-rotation calculated from flexion-extension radiographs was developed. Materials and Methods. First, spine surgeons documented their intended surgical plan. Subsequently, the participants' flexionextension radiographs were taken. From these, the translationper-degree-of-rotation was calculated and reported as a sagittal plane shear index (SPSI). The SPSI metric of the spinal level intended to be treated was used to decide if the intended surgical plan needed to be changed or not. Results. SPSI was determined for 75 participants. Of these, 51 (68%) had an intended surgical plan of decompression only and 24 (32%) had decompression with fusion. In 63% of participants, the SPSI was in support of their intended surgical plan. For 29% of participants, the surgeon changed the surgical plan after the SPSI metric became available to them. A suggested change in the surgical plan was overruled by 8% of participants. The final surgical plan was decompression only for 59 (79%) participants and decompression with fusion for 16 (21%) participants. Conclusion. The 29% change in intended surgical plans suggested that SPSI was considered by spine surgeons as an adjunct metric in deciding whether to perform decompression only or to add instrumented fusion. This change exceeded the a priori defined 15% considered necessary to show the potential clinical utility of SPSI. [ABSTRACT FROM AUTHOR]

Objectives: Interpretation of dental radiographs is a difficult process, particularly for inexperienced students. This study introduced concept mapping for dental students to help in the radiographic interpretation of common jaw lesions. We aimed to analyze the efficacy of the concept map (CM) in radiographic interpretation, with a discussion of the diagnostic reasoning dilemma. Methods: This study included 39 dental students. After a 1‐h class for CM guidance and based on three group discussions and one‐on‐one feedback, the students completed and submitted CMs for three jaw diseases (ameloblastoma, odontogenic keratocyst, and simple bone cyst). All participants underwent a pretest and posttest of knowledge and diagnosis; all students but one completed an open‐ended questionnaire regarding the use of CMs. Results: Concept mapping effectively improved diagnostic accuracy. The participants' posttest scores were better than their pretest scores in both knowledge and diagnostic tests. Most of the students attempted radiographic interpretation through analytic reasoning. The time required for the students to draw a CM varied from student to student from 3–5 h to 1–3 days. Conclusion: This study shows that CMs can improve the radiographic diagnostic ability of dental students by providing a framework for analytic reasoning. Continuous research is warranted to improve the effectiveness of CM in oral radiographic interpretation in the dental student's class. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite multiple standardized interpretation criteria (SICs) for imaging response assessment in patients with head and neck squamous cell carcinoma (HNSCC), these systems remain relatively underutilized. This underutilization may in part relate to the fact that the relevant information has been published in disparate journals over multiple decades. This paper aims to review the SICs available for use in interpreting post-treatment FDG PET/CT in patients with HNSCC. We evaluate each SIC in the context of eight desired traits. Selecting a SIC that best matches the needs of one's practice is expected to facilitate multidisciplinary buy-in and maximize the likelihood of successful implementation. Introduction: There is growing interest in the development and application of standardized imaging criteria (SIC), to minimize variability and improve the reproducibility of image interpretation in head and neck squamous cell carcinoma (HNSCC). Methods: "Squamous cell carcinoma" AND "standardized interpretation criteria" OR "radiographic response assessment" were searched using PubMed and Google Scholar for articles published between 2009 and 2024, returning 56 publications. After abstract review, 18 were selected for further evaluation, and 6 different SICs (i.e., PERCIST, Porceddu, Hopkins, NI-RADS, modified Deauville, and Cuneo) were included in this review. Each SIC is evaluated in the context of 8 desired traits of a standardized reporting system. Results: Two SICs have societal endorsements (i.e., PERCIST, NI-RADS); four can be used in the evaluation of locoregional and systemic disease (i.e., PERCIST, Hopkins, NI-RADS, Cuneo), and four have specific categories for equivocal imaging results (i.e., Porceddu, NI-RADS, modified Deauville, and Cuneo). All demonstrated areas for future improvement in the context of the 8 desired traits. Conclusion: Multiple SICs have been developed for and demonstrated value in HNSCC post-treatment imaging; however, these systems remain underutilized. Selecting an SIC with features that best match the needs of one's practice is expected to maximize the likelihood of successful implementation. [ABSTRACT FROM AUTHOR]

Abstract The main purpose of vital pulp therapy (VPT) is to preserve the integrity and function of the pulp. A wide variety of materials and techniques have been proposed to improve treatment outcomes, and among them, the utilization of lasers has gained significant attention. The application of lasers in different stages of VPT has witnessed remarkable growth in recent years, surpassing previous approaches. This study aimed to review the applications of lasers in different steps of VPT and evaluate associated clinical and radiographic outcomes. An electronic search using Scopus, MEDLINE, Web of Science and Google Scholar databases from 2000 to 2023 was carried out by two independent researchers. The focus was on human studies that examined the clinical and/or radiographic effects of different laser types in VPT. A total of 4243 studies were included in this narrative review article. Based on the compiled data, it can be concluded that although current literature suggests laser may be proposed as an adjunct modality for some procedural steps in VPT, more research with standardized methodologies and criteria is needed to obtain more reliable and conclusive results.

Abstract The Medical Radiation Practice Board of Australia (MRPBA) minimum competency framework requires all Australian radiographers to identify significant pathology in radiological images and take appropriate action to alert these urgent findings and ensure patient safety. Despite professional bodies endorsing the provision of preliminary image evaluations (PIE) in written format, radiographer image interpretation often remains inconsistent, informal, or undocumented. The purpose of this narrative review was to assess the literature to determine if PIE in the form of written radiographer comments is of value to the Australian healthcare system. A structured search was completed using four health research databases: CINAHL, Medline, Scopus and Web of Science. Studies have suggested that there is a contextual need for commenting due to increased imaging service pressures, radiologist shortages and subsequent reporting delays. Radiographers appear well placed and willing to provide accurate initial input with evidence that this would be valued and appreciated within the multidisciplinary team. Radiographer commenting has also been shown to reduce diagnostic and communicative errors with the potential to improve patient management. Finally, it was shown that participation in image interpretation practices can enhance recruitment, retention and job satisfaction among radiographers. Therefore, the current literature supports implementation of radiographer commenting within the Australian healthcare system.

The main purpose of vital pulp therapy (VPT) is to preserve the integrity and function of the pulp. A wide variety of materials and techniques have been proposed to improve treatment outcomes, and among them, the utilization of lasers has gained significant attention. The application of lasers in different stages of VPT has witnessed remarkable growth in recent years, surpassing previous approaches. This study aimed to review the applications of lasers in different steps of VPT and evaluate associated clinical and radiographic outcomes. An electronic search using Scopus, MEDLINE, Web of Science and Google Scholar databases from 2000 to 2023 was carried out by two independent researchers. The focus was on human studies that examined the clinical and/or radiographic effects of different laser types in VPT. A total of 4243 studies were included in this narrative review article. Based on the compiled data, it can be concluded that although current literature suggests laser may be proposed as an adjunct modality for some procedural steps in VPT, more research with standardized methodologies and criteria is needed to obtain more reliable and conclusive results. [ABSTRACT FROM AUTHOR]

The Medical Radiation Practice Board of Australia (MRPBA) minimum competency framework requires all Australian radiographers to identify significant pathology in radiological images and take appropriate action to alert these urgent findings and ensure patient safety. Despite professional bodies endorsing the provision of preliminary image evaluations (PIE) in written format, radiographer image interpretation often remains inconsistent, informal, or undocumented. The purpose of this narrative review was to assess the literature to determine if PIE in the form of written radiographer comments is of value to the Australian healthcare system. A structured search was completed using four health research databases: CINAHL, Medline, Scopus and Web of Science. Studies have suggested that there is a contextual need for commenting due to increased imaging service pressures, radiologist shortages and subsequent reporting delays. Radiographers appear well placed and willing to provide accurate initial input with evidence that this would be valued and appreciated within the multidisciplinary team. Radiographer commenting has also been shown to reduce diagnostic and communicative errors with the potential to improve patient management. Finally, it was shown that participation in image interpretation practices can enhance recruitment, retention and job satisfaction among radiographers. Therefore, the current literature supports implementation of radiographer commenting within the Australian healthcare system. [ABSTRACT FROM AUTHOR]

BackgroundAmorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and actionable (high-risk and malignant) amorphous calcifications using a combination of local textures, global spatial relationships, and interpretable handcrafted expert features.MethodOur approach was trained and validated on a set of 168 2D full-field digital mammography exams (248 images) from 168 patients. Within these 248 images, we identified 276 image regions with segmented amorphous calcifications and a biopsy-confirmed diagnosis. A set of local (radiomic and region measurements) and global features (distribution and expert-defined) were extracted from each image. Local features were grouped using an unsupervised k-means clustering algorithm. All global features were concatenated with clustered local features and used to train a LightGBM classifier to distinguish benign from actionable cases.ResultsOn the held-out test set of 60 images, our approach achieved a sensitivity of 100%, specificity of 35%, and a positive predictive value of 38% when the decision threshold was set to 0.4. Given that all of the images in our test set resulted in a recommendation of a biopsy, the use of our algorithm would have identified 15 images (25%) that were benign, potentially reducing the number of breast biopsies.ConclusionsQuantitative analysis of full-field digital mammograms can extract subtle shape, texture, and distribution features that may help to distinguish between benign and actionable amorphous calcifications.

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT. [ABSTRACT FROM AUTHOR]

Objective: Cone beam computed tomography (CBCT) is an imaging modality that has recently gained increasing popularity for dental imaging. This study aimed to investigate the usage of CBCT imaging among Iranian Association of Endodontists members using an online survey. Methods: Iranian endodontic practitioners were recruited to participate in the study. A web-based questionnaire was designed and sent to 328 endodontists. The questionnaire was available for a one-month long period during November 2019. The questionnaire included basic demographic details of the participants and questions related to CBCT application in endodontic treatment procedures. The validity and reliability of the questionnaire were assessed by expert endodontists. The chi-square test was used for data analysis, and a p-value less than 0.05 was considered statistically significant.Results: A total of 101 participants completed the survey, giving an overall completed response rate of 30.8%. Ninety-four percent of participants (n=95) used CBCT imaging in their practice. There were significant differences in some variables between endodontists who frequently prescribed CBCT as compared to those who rarely prescribed it (P

BackgroundWe sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals.MethodsThis was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (

BACKGROUND: Petrous temporal bone cone-beam computed tomography scans help aid diagnosis and accurate identification of key operative landmarks in temporal bone and mastoid surgery. Our primary objective was to determine the accuracy of using a deep learning convolutional neural network algorithm to augment identification of structures on petrous temporal bone cone-beam computed tomography. Our secondary objective was to compare the accuracy of convolutional neural network structure identification when trained by a senior versus junior clinician. METHODS: A total of 129 petrous temporal bone cone-beam computed tomography scans were obtained from an Australian public tertiary hospital. Key intraoperative landmarks were labeled in 68 scans using bounding boxes on axial and coronal slices at the level of the malleoincudal joint by an otolaryngology registrar and board-certified otolaryngologist. Automated structure identification was performed on axial and coronal slices of the remaining 61 scans using a convolutional neural network (Microsoft Custom Vision) trained using the labeled dataset. Convolutional neural network structure identification accuracy was manually verified by an otolaryngologist, and accuracy when trained by the registrar and otolaryngologist labeled datasets respectively was compared. RESULTS: The convolutional neural network was able to perform automated structure identification in petrous temporal bone cone-beam computed tomography scans with a high degree of accuracy in both axial (0.958) and coronal (0.924) slices (P < .001). Convolutional neural network accuracy was proportionate to the seniority of the training clinician in structures with features more difficult to distinguish on single slices such as the cochlea, vestibule, and carotid canal. CONCLUSION: Convolutional neural networks can perform automated structure identification in petrous temporal bone cone-beam computed tomography scans with a high degree of accuracy, with the performance being proportionate to the seniority of the training clinician. Training of the convolutional neural network by the most senior clinician is desirable to maximize the accuracy of the results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Medical Imaging & Radiation Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Cone beam computed tomography (CBCT) is an imaging modality that has recently gained increasing popularity for dental imaging. This study aimed to investigate the usage of CBCT imaging among Iranian Association of Endodontists members using an online survey. Methods: Iranian endodontic practitioners were recruited to participate in the study. A web-based questionnaire was designed and sent to 328 endodontists. The questionnaire was available for a one-month long period during November 2019. The questionnaire included basic demographic details of the participants and questions related to CBCT application in endodontic treatment procedures. The validity and reliability of the questionnaire were assessed by expert endodontists. The chi-square test was used for data analysis, and a p-value less than 0.05 was considered statistically significant. Results: A total of 101 participants completed the survey, giving an overall completed response rate of 30.8%. Ninetyfour percent of participants (n=95) used CBCT imaging in their practice. There were significant differences in some variables between endodontists who frequently prescribed CBCT as compared to those who rarely prescribed it (P<0.05). CBCT was prescribed more frequently by endodontists who received training in CBCT usage, those performing periapical surgeries, and those using magnification in their practice. Conclusions: The survey indicated that CBCT technology is widely used among Iranian endodontists particularly if they have already received the required training. The most common indications for CBCT were detecting vertical root fracture, teeth with complex anatomy and additional canals, and root resorption. [ABSTRACT FROM AUTHOR]

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy γ-ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximum-likelihood attenuation and activity (MLAA) algorithm has been used for GCT reconstruction but suffers from noise. Kernel MLAA exploits an X-ray CT image prior through the kernel framework to guide GCT reconstruction and has demonstrated substantial improvements in noise suppression. However, similar to other kernel methods for image reconstruction, the existing kernel MLAA uses image intensity-based features to construct the kernel representation, which is not always robust and may lead to suboptimal reconstruction with artefacts. In this paper, we propose a modified kernel method by using an autoencoder convolutional neural network (CNN) to extract an intrinsic feature set from the X-ray CT image prior. A computer simulation study was conducted to compare the autoencoder CNN-derived feature representation with raw image patches for evaluation of kernel MLAA for GCT image reconstruction and dual-energy multi-material decomposition. The results show that the autoencoder kernel MLAA method can achieve a significant image quality improvement for GCT and material decomposition as compared to the existing kernel MLAA algorithm. A weakness of the proposed method is its potential over-smoothness in a bone region, indicating the importance of further optimization in future work. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

PurposeTo demonstrate a stronger correlation and agreement of yttrium-90 (90Y) positron emission tomography (PET)/computed tomography (CT) measurements with explant liver tumor dosing compared with the standard model (SM) for radioembolization.Materials and methodsHepatic VX2 tumors were implanted into New Zealand white rabbits, with growth confirmed by 7 T magnetic resonance imaging. Seventeen VX2 rabbits provided 33 analyzed tumors. Treatment volumes were calculated from manually drawn volumes of interest (VOI) with three-dimensional surface renderings. Radioembolization was performed with glass 90Y microspheres. PET/CT imaging was completed with scatter and attenuation correction. Three-dimensional ellipsoid VOI were drawn to encompass tumors on fused images. Tumors and livers were then explanted for inductively coupled plasma (ICP)-optical emission spectroscopy (OES) analysis of microsphere content. 90Y PET/CT and SM measurements were compared with reference standard ICP-OES measurements of tumor dosing with Pearson correlation and Bland-Altman analyses for agreement testing with and without adjustment for tumor necrosis.ResultsThe median infused activity was 33.3 MBq (range, 5.9-152.9). Tumor dose was significantly correlated with 90Y PET/CT measurements (r = 0.903, P < .001) and SM estimates (r = 0.607, P < .001). Bland-Altman analyses showed that the SM tended to underestimate the tumor dosing by a mean of -8.5 Gy (CI, -26.3-9.3), and the degree of underestimation increased to a mean of -18.3 Gy (CI, -38.5-1.9) after the adjustment for tumor necrosis.Conclusions90Y PET/CT estimates were strongly correlated and had better agreement with reference measurements of tumor dosing than SM estimates.

Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.

INTRODUCTION: We previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm. MATERIALS AND METHODS: In this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The positive class was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases. RESULTS: Area under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively. CONCLUSION: Prospectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.

Background: This study assessed the diagnostic accuracy of cone-beam computed tomography (CBCT) with a modified grayscale range for the detection of buccal cortical plate defects adjacent to dental implants. Materials and Methods: In this in vitro experimental study, titanium implants were inserted in 168 fresh bovine bone blocks with 1-1.5 mm of buccal cortical plate thickness. The blocks were randomly divided into four groups (n = 42). No defect was created in the control blocks. In the three experimental groups, cortical plate defects were randomly created in the cervical, middle, or apical third by a round bur with a 2-mm diameter (n = 42). All blocks underwent CBCT with and without change in the grayscale range. Two observers evaluated all images regarding the presence/absence of defects. Kappa test is used for the agreement of the observers. The diagnostic accuracy of the two modalities was compared by calculating the area under the receiver operating characteristic curve (AUC) (P ≤ 0.05). The sensitivity and specificity values were also compared. Results: The AUC was not significantly different between the two modalities with and without altered grayscale range (0.754 vs. 0.762, respectively, P = 0.716). The diagnostic sensitivity of CBCT with and without change in the grayscale range was 51% and 52%, respectively, with a specificity of 100% for both. The diagnostic accuracy of CBCT with and without altered grayscale range had no significant difference for apical and middle third defects (P > 0.05) and was significantly higher than that for the cervical third defects (P < 0.05). Conclusion: Changing the grayscale range does not improve the diagnostic accuracy of CBCT for the detection of buccal cortical plate defects adjacent to dental implants. [ABSTRACT FROM AUTHOR]

PurposePneumonia is a common clinical diagnosis for which chest radiographs are often an important part of the diagnostic workup. Deep learning has the potential to expedite and improve the clinical interpretation of chest radiographs. While earlier approaches have emphasized the feasibility of "binary classification" to accomplish this task, alternative strategies may be possible. We explore the feasibility of a "semantic segmentation" deep learning approach to highlight the potential foci of pneumonia on frontal chest radiographs.Materials and methodsIn this retrospective study, we trained a U-net convolutional neural network (CNN) to predict pixel-wise probability maps for pneumonia using a public data set provided by the Radiological Society of North America (RSNA) comprised of 22,000 radiographs and radiologist-defined bounding boxes. We reserved 3684 radiographs as an independent validation data set and assessed overall performance for localization using Dice overlap and classification performance using the area under the receiver-operator characteristic curve.ResultsFor classification/detection of pneumonia, area under the receiver-operator characteristic curve on frontal radiographs was 0.854 with a sensitivity of 82.8% and specificity of 72.6%. Using this strategy of neural network training, probability maps localized pneumonia to lung parenchyma for essentially all validation cases. For segmentation of pneumonia for positive cases, predicted probability maps had a mean Dice score (±SD) of 0.603±0.204, and 60.0% of these had a Dice score >0.5.ConclusionsA "semantic segmentation" deep learning approach can provide a probabilistic map to assist in the diagnosis of pneumonia. In combination with the patient's history, clinical findings and other imaging, this strategy may help expedite and improve diagnosis.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P

BackgroundCardiac fat is emerging as an important parameter for cardiovascular risk stratification. Accurate and reproducible volumetric measurements can facilitate in the serial assessment of cardiac fat by computed tomography (CT). We assessed the intra- and inter-observer variability of cardiac fat volumetric measurements using a semi-automated CT software.MethodsWe used non-contrast coronary calcium CT scans to quantify epicardial and intra-thoracic fat volumes. Two expert readers analyzed baseline and follow up CT scans of 45 subjects by using a semi-automated CT software (QFAT 2.0, Cedars Sinai-Medical Center). Correlation and Bland-Altman analysis was performed for both intra- and inter-observer comparisons for each cardiac fat type.ResultsThe intra-observer correlation coefficients ranged between 0.86 to 0.99 and 0.87 to 0.99 for epicardial (median fat per reader (cm3) 20.9 to 25.7) and intra-thoracic (median fat per reader (cm3) 27.1 to 31.6) fat volumes respectively, with no significant differences between individual data points (all p > 0.38). The inter-observer correlation coefficient was 0.99 (p

Introduction: Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks. This literature search aims to present a comprehensive systematic review of the published literature with regard to CNNs and their utility to date in ENT radiology. Methods: Data were extracted from a variety of databases including PubMED, Proquest, MEDLINE Open Knowledge Maps, and Gale OneFile Computer Science. Medical subject headings (MeSH) terms and keywords were used to extract related literature from each databases inception to October 2020. Inclusion criteria were studies where CNNs were used as the main intervention and CNNs focusing on radiology relevant to ENT. Titles and abstracts were reviewed followed by the contents. Once the final list of articles was obtained, their reference lists were also searched to identify further articles. Results: Thirty articles were identified for inclusion in this study. Studies utilizing CNNs in most ENT subspecialties were identified. Studies utilized CNNs for a number of tasks including identification of structures, presence of pathology, and segmentation of tumors for radiotherapy planning. All studies reported a high degree of accuracy of CNNs in performing the chosen task. Conclusion: This study provides a better understanding of CNN methodology used in ENT radiology demonstrating a myriad of potential uses for this exciting technology including nodule and tumor identification, identification of anatomical variation, and segmentation of tumors. It is anticipated that this field will continue to evolve and these technologies and methodologies will become more entrenched in our everyday practice. [ABSTRACT FROM AUTHOR]

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT.

PURPOSE:To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. METHODS:This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II-III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. RESULTS:Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). CONCLUSIONS:Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

Small-bowel obstruction (SBO) is a common and important disease, for which machine learning tools have yet to be developed. Image annotation is a critical first step for development of such tools. This study assesses whether image annotation by eye tracking is sufficiently accurate and precise to serve as a first step in the development of machine learning tools for detection of SBO on CT. Seven subjects diagnosed with SBO by CT were included in the study. For each subject, an obstructed segment of bowel was chosen. Three observers annotated the centerline of the segment by manual fiducial placement and by visual fiducial placement using a Tobii 4c eye tracker. Each annotation was repeated three times. The distance between centerlines was calculated after alignment using dynamic time warping (DTW) and statistically compared to clinical thresholds for diagnosis of SBO. Intra-observer DTW distance between manual and visual centerlines was calculated as a measure of accuracy. These distances were 1.1 ± 0.2, 1.3 ± 0.4, and 1.8 ± 0.2 cm for the three observers and were less than 1.5 cm for two of three observers (P

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.

BACKGROUND: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. METHODS: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. RESULTS: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). CONCLUSIONS: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

AimsThis study explored the coronary plaque volume change (PVC) according to the change of percent body mass index (BMI) and categorical BMI group using serial coronary computed tomography angiography (CCTA).Methods and resultsA total of 1568 subjects who underwent serial CCTA with available BMI at baseline (CCTA1) and follow-up (CCTA2) were included. Median inter-scan period was 3.3 (interquartile range: 2.6-4.6) years. Quantitative assessment of coronary plaque was performed at both scans. All participants were categorized into three BMI (kg/m2) groups: normal:

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

PURPOSE:To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS:One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS:Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS:Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

ObjectivesThe aim of this study was to determine whether coronary artery calcium (CAC) progression was associated with coronary plaque progression on coronary computed tomographic angiography.BackgroundCAC progression and coronary plaque characteristics are associated with incident coronary heart disease. However, natural history of coronary atherosclerosis has not been well described to date, and the understanding of the association between CAC progression and coronary plaque subtypes such as noncalcified plaque progression remains unclear.MethodsConsecutive patients who were referred to our clinic for evaluation and had serial coronary computed tomography angiography scans performed were included in the study. Coronary artery plaque (total, fibrous, fibrous-fatty, low-attenuation, densely calcified) volumes were calculated using semiautomated plaque analysis software.ResultsA total of 211 patients (61.3 ± 12.7 years of age, 75.4% men) were included in the analysis. The mean interval between baseline and follow-up scans was 3.3 ± 1.7 years. CAC progression was associated with a significant linear increase in all types of coronary plaque and no plaque progression was observed in subjects without CAC progression. In multivariate analysis, annualized and normalized total plaque (β = 0.38; p < 0.001), noncalcified plaque (β = 0.35; p = 0.001), fibrous plaque (β = 0.56; p < 0.001), and calcified plaque (β = 0.63; p = 0.001) volume progression, but not fibrous-fatty (β = 0.03; p = 0.28) or low-attenuation plaque (β = 0.11; p = 0.1) progression, were independently associated with CAC progression. Plaque progression did not differ between the sexes. A significantly increased total and calcified plaque progression was observed in statin users.ConclusionsIn a clinical practice setting, progression of CAC was significantly associated with an increase in both calcified and noncalcified plaque volume, except fibrous-fatty and low-attenuation plaque. Serial CAC measurements may be helpful in determining the need for intensification of preventive treatment.

Objectives: Concept mapping is used to promote critical thinking and meaningful learning in health professions education. This study aimed to evaluate dental undergraduates' perceptions of concept mapping as a learning tool for oral radiographic interpretation competency. Methods: A total of 39 third‐year undergraduates at a dental college participated in the oral radiological interpretation session. The students received 1 hour of instruction before creating their concept map (CM). Participants were provided an assignment of four relevant clinical cases to draw an individualised CM. The students' draft and final CM were submitted. The instructor evaluated the CMs to identify whether students could analyse, connect and organise the information in a meaningful way. Among them, 37 completed the questionnaire about their experiences and responses to concept mapping. Results: Students perceived concept mapping positively. The instructor's feedback helped them recognise their misconceptions and fostered greater motivation to learn. Students attempted to integrate basic biomedical knowledge and clinical features through cross‐linking. However, they also expressed negative attitudes toward concept mapping regarding time consumption and heavy burden. Conclusions: Concept mapping motivated students' directed learning and gave them the opportunity to recognise their misconceptions. This study suggests the potential use of CMs as an adjunctive learning strategy in the oral radiographic interpretation class. [ABSTRACT FROM AUTHOR]

Copyright of Revue d'Orthopédie Dento-Faciale is the property of Parresia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BackgroundPneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition.Methods and findingsIn all, 13,292 frontal chest X-rays (3,107 with pneumothorax) were visually annotated by radiologists. This dataset was used to train and evaluate multiple network architectures. Images showing large- or moderate-sized pneumothorax were considered positive, and those with trace or no pneumothorax were considered negative. Images showing small pneumothorax were excluded from training. Using an internal validation set (n = 1,993), we selected the 2 top-performing models; these models were then evaluated on a held-out internal test set based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV). The final internal test was performed initially on a subset with small pneumothorax excluded (as in training; n = 1,701), then on the full test set (n = 1,990), with small pneumothorax included as positive. External evaluation was performed using the National Institutes of Health (NIH) ChestX-ray14 set, a public dataset labeled for chest pathology based on text reports. All images labeled with pneumothorax were considered positive, because the NIH set does not classify pneumothorax by size. In internal testing, our "high sensitivity model" produced a sensitivity of 0.84 (95% CI 0.78-0.90), specificity of 0.90 (95% CI 0.89-0.92), and AUC of 0.94 for the test subset with small pneumothorax excluded. Our "high specificity model" showed sensitivity of 0.80 (95% CI 0.72-0.86), specificity of 0.97 (95% CI 0.96-0.98), and AUC of 0.96 for this set. PPVs were 0.45 (95% CI 0.39-0.51) and 0.71 (95% CI 0.63-0.77), respectively. Internal testing on the full set showed expected decreased performance (sensitivity 0.55, specificity 0.90, and AUC 0.82 for high sensitivity model and sensitivity 0.45, specificity 0.97, and AUC 0.86 for high specificity model). External testing using the NIH dataset showed some further performance decline (sensitivity 0.28-0.49, specificity 0.85-0.97, and AUC 0.75 for both). Due to labeling differences between internal and external datasets, these findings represent a preliminary step towards external validation.ConclusionsWe trained automated classifiers to detect moderate and large pneumothorax in frontal chest X-rays at high levels of performance on held-out test data. These models may provide a high specificity screening solution to detect moderate or large pneumothorax on images collected when human review might be delayed, such as overnight. They are not intended for unsupervised diagnosis of all pneumothoraces, as many small pneumothoraces (and some larger ones) are not detected by the algorithm. Implementation studies are warranted to develop appropriate, effective clinician alerts for the potentially critical finding of pneumothorax, and to assess their impact on reducing time to treatment.

The DIALS diffraction-modeling software package has been applied to serial crystallography data. Diffraction modeling is an exercise in determining the experimental parameters, such as incident beam wavelength, crystal unit cell and orientation, and detector geometry, that are most consistent with the observed positions of Bragg spots. These parameters can be refined by nonlinear least-squares fitting. In previous work, it has been challenging to refine both the positions of the sensors (metrology) on multipanel imaging detectors such as the CSPAD and the orientations of all of the crystals studied. Since the optimal models for metrology and crystal orientation are interdependent, alternate cycles of panel refinement and crystal refinement have been required. To simplify the process, a sparse linear algebra technique for solving the normal equations was implemented, allowing the detector panels to be refined simultaneously against the diffraction from thousands of crystals with excellent computational performance. Separately, it is shown how to refine the metrology of a second CSPAD detector, positioned at a distance of 2.5 m from the crystal, used for recording low-angle reflections. With the ability to jointly refine the detector position against the ensemble of all crystals used for structure determination, it is shown that ensemble refinement greatly reduces the apparent nonisomorphism that is often observed in the unit-cell distributions from still-shot serial crystallography. In addition, it is shown that batching the images by timestamp and re-refining the detector position can realistically model small, time-dependent variations in detector position relative to the sample, and thereby improve the integrated structure-factor intensity signal and heavy-atom anomalous peak heights.

BackgroundAs combined morphological and physiological assessment of coronary artery disease (CAD) is necessary to reliably resolve CAD severity, the objective of this study was to validate an automated minimum-cost path assignment (MCP) technique which enables accurate, vessel-specific assignment of the left (LCA) and right (RCA) coronary perfusion territories using computed tomography (CT) angiography data for both left and right ventricles.MethodsSix swine were used to validate the MCP technique. In each swine, a dynamic acquisition comprised of twenty consecutive volume scans was acquired with a 320-slice CT scanner following peripheral injection of contrast material. From this acquisition the MCP technique was used to automatically assign LCA and RCA perfusion territories for the left and right ventricles, independently. Each animal underwent another dynamic CT acquisition following direct injection of contrast material into the LCA or RCA. Using this acquisition, reference standard LCA and RCA perfusion territories were isolated from the myocardial blush. The accuracy of the MCP technique was evaluated by quantitatively comparing the MCP-derived LCA and RCA perfusion territories to these reference standard territories.ResultsAll MCP perfusion territory masses (MassMCP) and all reference standard perfusion territory masses (MassRS) in the left ventricle were related by MassMCP = 0.99MassRS+0.35 g (r = 1.00). MassMCP and MassRS in the right ventricle were related by MassMCP = 0.94MassRS+0.39 g (r = 0.96).ConclusionThe MCP technique was validated in a swine animal model and has the potential to be used for accurate, vessel-specific assignment of LCA and RCA perfusion territories in both the left and right ventricular myocardium using CT angiography data.

BackgroundCT SQUEEZ is a new automated technique to evaluate regional endocardial strain by tracking features on the endocardium from 4D cine CT data. The objective of this study was to measure the range of endocardial regional strain (RSCT) values obtained with CT SQUEEZ in the normal human left ventricle (LV) from standard clinical 4D coronary CTA exams.MethodsRSCT was measured over the heart cycle in 25 humans with normal LV function using cine CT from three vendors. Mean and standard deviation of RSCT values were computed in 16 AHA LV segments to estimate the range of values expected in the normal LV.ResultsCurves describing RSCT vs. time were consistent between subjects. There was a slight gradient of decreasing minimum RSCT value (increased shortening) from the base to the apex of the heart. Mean RSCT values at end-systole were: base = -32% ± 1%, mid = -33% ± 1%, apex = -36% ± 1%. The standard deviation of the minimum systolic RSCT in each segment over all subjects was 5%. The average time to reach maximum shortening was 34% of the RR interval.ConclusionsRegional strain (RSCT) can be rapidly obtained from standard gated coronary CCTA protocols using 4DCT SQUEEZ processing. We estimate that 95% of normal LV end-systolic RSCT values will fall between -23% and -43%; therefore, we hypothesize that an RSCT value higher than -23% will indicate a hypokinetic segment in the human heart.

AimTo evaluate whether knee alignment explains the higher prevalence of lateral compartment tibiofemoral radiographic osteoarthritis (TFROA) among rural Chinese compared with that among Whites.MethodsThe Wuchuan OA Study is a population-based longitudinal study of risk factors for knee OA. At baseline 1030 participants had home interviews, clinical examinations and weight-bearing posteroanterior semi-flexed radiographs of the tibiofemoral joints. Anatomic knee alignment was measured using an e-film workstation and divided into three categories: normal (182°-184°), valgus (> 184°), and varus (< 182°) alignment. A knee was defined as having medial or lateral compartmental ROA if its Kellgren and Lawrence grade was ≥ 2 and joint space narrowing ≥ 1 in the medial or lateral compartment, respectively. We examined the association between knee alignment with prevalent medial or lateral knee ROA separately using multiple logistic regression.ResultsAmong 1030 participants, the proportions of knees with normal, valgus and varus alignment were 29.9%, 56.5% and 13.7%, respectively. The prevalence of medial and lateral ROA was 16.0% and 4.3%, respectively. Valgus alignment was associated with prevalence of lateral compartment ROA (odds ratio [OR] = 5.0, 95% CI: 2.4-10.5), while varus alignment was associated with medial compartment ROA (OR = 6.1, 95% CI: 4.4-8.6). The ratio of prevalence of lateral versus medial compartment TFROA was greater in Wuchuan than that in the Framingham OA Study and valgus malalignment was more common in Wuchuan than in the Rotterdam study.ConclusionsThe prevalence of compartment-specific TFROA differs between rural Chinese and Whites. This difference is likely due to relatively high prevalence of valgus malalignment in rural Chinese compared with that in Whites.

We sought to assess the feasibility of computed tomography coronary angiography (CTCA) on a 128-slice, dual source scanner in children with acute and convalescent phase Kawasaki disease (KD).A prospective study of 49 children with KD (12 at presentation and 37 in the convalescent phase) was conducted between November 2013 and April 2015. CTCA was performed with either prospective (n = 37) or retrospective (n = 12) electrocardiographic gating. A radiologist blinded to clinical profile and echocardiogram evaluated each scan.Median age (36 boys and 13 girls) was 7 years. Median dose-length product value and median effective CT radiation dose was 32mGycm (interquartile range [IQR]: 21-74) and 0.54 miliSieverts (mSv) (IQR: 0.77-3.2) for all scans, and 27mGycm (IQR: 18.5-33.75) and 0.48mSv (IQR: 0.18-1.17) for prospectively triggered scans (n = 37). Fourteen subjects (30 coronary segments) showed abnormalities by CTCA including aneurysms (n = 27) and stenoses (n = 3). In the acute phase (n = 12), aneurysms were detected in 5 children (18 segments).CTCA allows comprehensive evaluation of coronary arteries in children with KD.

BackgroundIn 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.ObjectiveTo determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.DesignCase-control.SettingSan Francisco Mammography Registry and Mayo Clinic.Participants1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.MeasurementsAutomated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.ResultsOf women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.LimitationNeither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.ConclusionAutomated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.Primary funding sourceNational Cancer Institute.

IntroductionMachine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores.MethodsFrom the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data).ResultsIn total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P

Finite element model can estimate bone strength better than BMD. This study used such a model to determine its association with hip fracture risk and found that the strength estimate provided limited improvement over the hip BMDs in predicting femoral neck (FN) fracture risk only.IntroductionBone fractures occur only when it is loaded beyond its ultimate strength. The goal of this study was to determine the association of femoral strength, as estimated by finite element (FE) analysis of DXA scans, with incident hip fracture as a single condition or with femoral neck (FN) and trochanter (TR) fractures separately in older men.MethodsThis prospective case-cohort study included 91 FN and 64 TR fracture cases and a random sample of 500 men (14 had a hip fracture) from the Osteoporotic Fractures in Men study during a mean ± SD follow-up of 7.7 ± 2.2 years. We analysed the baseline DXA scans of the hip using a validated plane-stress, linear-elastic FE model of the proximal femur and estimated the femoral strength during a sideways fall.ResultsThe estimated strength was significantly (P

The DIALS project is a collaboration between Diamond Light Source, Lawrence Berkeley National Laboratory and CCP4 to develop a new software suite for the analysis of crystallographic X-ray diffraction data, initially encompassing spot finding, indexing, refinement and integration. The design, core algorithms and structure of the software are introduced, alongside results from the analysis of data from biological and chemical crystallography experiments.

ObjectiveThe dual-energy CT (DECT) algorithm for urate detection is feasible only if hyperdense deposits are present. Based on our experience, around half of the performed DECT examinations show no such deposits and thus were useless for this indication. Our diagnostic accuracy study investigates whether conventional radiographs can serve as gatekeeper test prior to DECT for reliable exclusion of such radiopaque deposits.Materials and methodsIn this retrospective study, 77 clinically indicated DECT examinations of the hand (n = 29), foot (n = 36) and ankle (n = 12) of 55 patients (13 female, mean age 62±15 years) with suspected gouty arthritis were included. Two blinded readers independently evaluated DECT, gray-scale CT images (reference standard) and corresponding standardized radiographs for the presence/location of dense soft tissue deposits.ResultsInterreader agreement for detection of soft tissue deposits with DECT and radiographs was excellent (DECT: both readers, κ = 1; radiographs: both readers, κ = 0.94). DECT showed soft tissue deposits in 54/77 DECT (70%) scans. 30/54 scans (56%) showed deposits on the corresponding radiographs, while in 24 scans (44%) no deposits were seen on radiographs. Test performance of radiographs for soft tissue deposit detection: sensitivity 56%, specificity 100%, PPV 100%, NPV 48.9%, and accuracy 69%. Low density of the deposits was the main reasons for false-negative radiographs (19 cases, 79%), followed by superimposition of deposits by osseous structures (5 cases, 21%).ConclusionConventional radiographs of the hand, foot and ankle cannot serve as a gatekeeper test for reliable exclusion of radiopaque soft tissue deposits prior to DECT.

Purpose: The primary objective of this study was to assess the accuracy of periapical radiographs in determining the peri-implant marginal bone level. The accuracy of the linear measurements on radiographs was considered as the absolute difference between the true, intraoperative or surgical marginal bone level measurements (direct bone measurements during surgical procedures) and the radiographic measured distances. The secondary aims were to identify the variables influencing the radiographic evaluation (arch: mandible/maxilla; implant location: anterior/posterior; timing of implant placement: “early delayed” and “prolonged delayed”). The influence of vestibular and palatal/lingual crestal bone levels was also investigated. Materials and Methods: STROBE guidelines were followed. As soon as the implant was inserted, the marginal bone levels were recorded using a straight periodontal probe (intraoperative or surgical measurements). At the same time, periapical radiographs were taken. To standardize the radiographic images, periapical radiographs were acquired using the long-cone parallel technique and film holding system. All radiographs were analyzed by two examiners blinded to the surgical measurements. Intraclass correlation coefficient (ICC) was employed to assess the intraobserver and interobserver variability. The descriptive statistics, t test, and multivariate statistics were used; the threshold for statistical significance was P ≤ .05. Results: Two hundred sixty-eight implants were inserted in 142 patients. The interobserver agreement was 0.980; the intraobserver variability was 0.990 and 0.993. The mean difference between the radiographic and surgical measurements was 0.45 mm (range: 0 to 8 mm; SD: 1.76). Comparing the radiographic and surgical measurements, a statistically significant difference (P = .000) was detected. None of the variables considered (arch, implant location, and timing of implant placement) significantly influenced the accuracy. Neither the vestibular alveolar edge (P = .908) nor the lingual/palatal (P = .485) significantly influenced the accuracy. Conclusion: The periapical radiograph statistically significantly overestimates the level of periimplant marginal bone compared with surgical measurements. The arch, implant location, timing of implant placement, and level of vestibular or lingual/palatal alveolar edge do not influence deviation between the intraoperative peri-implant marginal bone level measurements and the radiographically determined marginal bone levels. [ABSTRACT FROM AUTHOR]

Background: This study tests the impact of the addition of autonomous computed tomography (CT) interpreting software to radiologist assessment of pulmonary nodules. Methods: Computed tomography scans for nodule assessment were identified retrospectively. Lung cancer risk factors, initial radiologist (RAD) report, Philips Lung Nodule software report (computer-aided nodule (CAD)) and radiologist report following the review of CT images and CAD (RAD + CAD) were collected. Follow-up recommendations based on current guidelines were derived from each report. Results: In all, 100 patients were studied. Median maximal diameter of the largest nodule reported by RAD and RAD + CAD were similar at 10.0 and 9.0 mm, respectively (p = 0.06) but were reported as larger by CAD at 11.8 mm (p < 0.001). Follow-up recommendations derived from RAD + CAD were less intensive in 23 (23%) and more intensive in 34 (34%) than that of RAD. Discussion: This study suggests that autonomous software use can alter radiologist assessment of pulmonary nodules such that suggested follow-up is altered. [ABSTRACT FROM AUTHOR]