Prediction of radiographic progression pattern in patients with ankylosing spondylitis using group-based trajectory modeling and decision trees.

Objective: This study aimed to identify trajectories of radiographic progression of the spine over time and use them, along with associated clinical factors, to develop a prediction model for patients with ankylosing spondylitis (AS). Methods: Data from the medical records of patients diagnosed with AS in a single center were extracted between 2001 and 2018. Modified Stoke Ankylosing Spondylitis Spinal Scores (mSASSS) were estimated from cervical and lumbar radiographs. Group-based trajectory modeling classified patients into trajectory subgroups using longitudinal mSASSS data. In multivariate analysis, significant clinical factors associated with trajectories were selected and used to develop a decision tree for prediction of radiographic progression. The most appropriate group for each patient was then predicted using decision tree analysis. Results: We identified three trajectory classes: class 1 had a uniformly increasing slope of mSASSS, class 2 showed sustained low mSASSS, and class 3 showed little change in the slope of mSASSS but highest mSASSS from time of diagnosis to after progression. In multivariate analysis for predictive factors, female sex, younger age at diagnosis, lack of eye involvement, presence of peripheral joint involvement, and low baseline erythrocyte sedimentation rate (log) were significantly associated with class 2. Class 3 was significantly associated with male sex, older age at diagnosis, presence of ocular involvement, and lack of peripheral joint involvement when compared with class 1. Six clinical factors from multivariate analysis were used for the decision tree for classifying patients into three trajectories of radiographic progression. Conclusion: We identified three patterns of radiographic progression over time and developed a decision tree based on clinical factors to classify patients according to their trajectories of radiographic progression. Clinically, this model holds promise for predicting prognosis in patients with AS.

A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis.

Background: Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS). Objective: This pilot study aimed to develop a deep learning model for grading the corners of the cervical and lumbar vertebral bodies for computer-aided detection of mSASSS in patients with ankylosing spondylitis (AS). Methods: Digital radiographic examination of the spine was performed using Discovery XR656 (GE Healthcare) and Digital Diagnost (Philips). The disk points were detected between the bodies using a key-point detection deep learning model from the image obtained in DICOM (digital imaging and communications in medicine) format from the cervical and lumbar spinal radiographs. After cropping the vertebral regions around the disk point, the lower and upper corners of the vertebral bodies were classified as grade 3 (total bony bridges) or grades 0, 1, or 2 (non-bridges). We trained a convolutional neural network model to predict the grades in the lower and upper corners of the vertebral bodies. The performance of the model was evaluated in a validation set, which was separate from the training set. Results: Among 1280 patients with AS for whom mSASSS data were available, 5,083 cervical and 5245 lumbar lateral radiographs were reviewed. The total number of corners where mSASSS was measured in the cervical and lumbar vertebrae, including the upper and lower corners, was 119,414. Among them, the number of corners in the training and validation sets was 110,088 and 9326, respectively. The mean accuracy, sensitivity, and specificity for mSASSS scoring in one corner of the vertebral body were 0.91604, 0.80288, and 0.94244, respectively. Conclusion: A high-performance deep learning model for grading the corners of the vertebral bodies was developed for the first time. This model must be improved and further validated to develop a computer-aided tool for assessing mSASSS in the future.

Coronary microcirculation assessment using functional angiography: Development of a wire-free method applicable to conventional coronary angiograms.

OBJECTIVES: We aimed to develop a novel wire- and adenosine-free microcirculatory resistive index from functional angiography (angio-IMR) to estimate coronary microcirculatory resistance, and to investigate how this method can improve clinical interpretation of physiological stenosis assessment with quantitative flow ratio (QFR). BACKGROUND: Hyperemic index of coronary microcirculatory resistance (IMR) is a widely used tool to assess microcirculatory dysfunction. However, the need of dedicated intracoronary wire and hyperemia limits its adoption in clinical practice. METHODS: We performed our study in two separate stages: (1) development of a formula (angio-IMR) to estimate IMR from resting angiograms and aortic pressure (Pa), and (2) validation of the method in a clinical population using invasively measured IMR as reference. Additionally, QFR diagnostic performance was assessed considering angio-IMR values. RESULTS: We developed the formula: angio-IMR = (Pa-[0.1*Pa])*QFR*e-Tmn (where e-Tmn is an estimation of hyperaemic mean transit time) and validated it in 115 vessels (104 patients). Angio-IMR correlated well with IMR (Spearman's rho = 0.70, p < 0.001). Sensitivity, specificity, positive and negative predictive value, accuracy and area under the curve of angio-IMR to predict IMR were 87.5% (73.2-95.8), 85.3% (75.3-92.4), 76.1% (64.5-84.8), 92.8% (84.9-96.7), 85% and 0.90 (0.83-0.95), respectively. False positive QFR measurements decreased from 19.5% to 8.5% when angio-IMR was incorporated into the QFR interpretation workflow. CONCLUSIONS: Estimation of IMR without physiology wire and adenosine is feasible. Coronary microcirculatory dysfunction causing high IMR can be ruled-out with high confidence in vessels with low angio-IMR. Awareness of angio-IMR contributes to a better clinical interpretation of functional stenosis assessment with QFR.

Molecular Detection and Assessment of Risk Factors for Tick-Borne Diseases in Sheep and Goats from Turkey.

BACKGROUND: Tick-borne diseases mainly, theileriosis, babesiosis and anaplasmosis cause significant economic losses in livestock globally, including Turkey. The tick-borne pathogens of small ruminants in Turkey have been studied widely but information on molecular characterization and disease occurrence is still limited. METHODS: In this study, both microscopy and molecular detection and characterization for Theileria spp. Babesia ovis, Anaplasma ovis and Anaplasma phagocytophilum was conducted. A total of 133 blood samples of tick-infested small ruminants (105 sheep and 28 goats) were collected from Turkey: half of the animals had clinical signs of tick-borne disease infections. RESULTS: Using PCR assays and microscopy, 90.2% and 45.1% of the samples were positive for at least one pathogen, respectively. Overall, the infection rates of A. phagocytophilum, B. ovis, A. ovis, Theileria spp. were 66.7%, 62.4%, 46.6% and 7.0%, respectively. Fifty-nine of the 133 (44.4%) samples were co-infected with two or more pathogens. Sex, season and B. ovis positivity were significant risk factors for occurrence of clinical disease. Sequence and phylogenetic analysis based on B. ovis 18S small subunit rRNA, A. ovis major surface protein 4, Theileria spp. 18S rRNA and A. phagocytophilum 16S rRNA genes showed that the isolates in this study clustered together in well-supported clades with those previously collected from Turkey and other countries. CONCLUSIONS: The study shows B. ovis as the most significant pathogen associated with clinical and fatal cases in small ruminants from Turkey. Female sex and summer season are associated with increased risk of the disease. This study shows high infection rates with the pathogens among small ruminants including A. phagocytophilum which has veterinary and public health importance.

Predictors of severe radiographic progression in patients with early rheumatoid arthritis: A Prospective observational cohort study.

AIM: To identify predictors of severe radiographic progression in patients with early rheumatoid arthritis (ERA). METHODS: A total of 374 patients with ERA were selected from a Korean prospective cohort. Based on their annual Sharp/Van der Heijde modified score changes (ΔSHS/year), patients were classified into severe and no progression groups. Predictors of severe progression were evaluated using a multivariable logistic regression. RESULTS: After a mean follow-up duration of 4.2 years, the median (interquartile range) ΔSHS/year were 6.3 (4.4-10.2) and 0 (0-0) in the severe and no progression groups, respectively. Multivariable regression model revealed that Health Assessment Questionnaire (HAQ) score (odds ratio [OR] = 2.17), anticyclic citrullinated peptide antibody (OR = 3.44), body mass index (BMI; OR = 0.88), 6-month cumulative erythrocyte sedimentation rate (OR = 1.01) and baseline SHS (OR = 1.07) were independent predictors of severe progression. A model incorporating all five predictors satisfactorily predicted severe progression, with an area under the curve of 0.80. Baseline SHS was the predictor with the highest contribution to the predictive power of the final model (38%). CONCLUSIONS: Our predictive model composed of five clinical predictors showed high discriminative ability between severe and no radiographic progression in patients with ERA. Among them, baseline SHS was the strongest predictor. Also, low BMI in Korean patients with ERA have a high risk of severe radiographic progression, as has previously been found for Caucasians.

Assessment of osteoporosis using pelvic diagnostic computed tomography.

The purpose of the present study is to determine if a correlation exists between bone mineral density (BMD) obtained from dual energy X-ray absorptiometry (DXA) and Hounsfield unit (HU) from pelvic diagnostic computed tomography (dCT), and to evaluate whether HU could be used to identify osteoporosis. Seventy-nine patients were included in this study. HU values were measured in three different sections: the head-neck junction of the femur, the middle portion of the femoral neck, and the intertrochanter of the femur (IT). In each sectional image, HU values were measured at two regions of interest: cortical and cancellous bone (HU_t) and cancellous bone. The correlation between BMD and HU_t of IT was significant (r = 0.839, p < 0.01). In IT, the area under the curve value of HU_t was 0.875 (0.796-0.955). We found that a HU_t of IT <170 can be regarded as indicating osteoporosis: its positive predictive value is 96.9 %. A HU_t of IT >210 can be regarded as indicating an absence of osteoporosis: its negative predictive value is 84.6 %. In conclusion, we found that a significant correlation between HU of pelvic dCT and BMD of DXA, and HU potentially provided an alternative method for determining regional BMD. Therefore, pelvic dCT could possibly be a supplementary method for initial diagnosis of osteoporosis and for initiation of treatment.

Assessment of treatment success and short-term effectiveness using C-arm CT immediately after hepatic chemoembolization of HCC.

BACKGROUND/AIMS: To investigate semiquantitative analyses based on amount and morphology of iodized oil uptake about non-enhanced C-arm cone-beam CT(C-arm CT) immediately following hepatic chemoembolization of HCC. METHODOLOGY: We retrospectively reviewed 40 C-arm CT images taken immediately following chemoembolization in 29 consecutive patients who underwent hepatic chemoembolization for HCC lesions(n=97). Two radiologists scored iodized oil uptake in the index tumours from Grade 0(complete) to 4(invisible). To describe the morphologic characteristics of uptake, the lesions scored as Grade 1 to 3 were subclassified as 'defective' and 'not-defective' with respect to uptake. To evaluate the performance of this modality, we performed a receiver operating characteristic curve analysis. RESULTS: All treated lesions were classified into 'complete treatment'(n=50) and 'viable HCC'(n=47). Thirty-one lesions were evaluated as Grade 0, two as Grade 4, and the rest (n=64) as between Grades 1 and 3, and the lesions of Grade 3 or 4 were all viable. Of the defective uptake lesions(n=35), 28 lesions were also viable. The areas under the curves of the lesions were 0.827 to 0.860. CONCLUSIONS: The analysis of C-arm CT based on the amount and morphologic characteristics of iodized oil uptake may be useful to predict the effectiveness of the hepatic chemoembolization of HCC.

Validity and role of vertebral fracture assessment in detecting prevalent vertebral fracture in patients with rheumatoid arthritis.

OBJECTIVES: We aimed to identify the validity and the role of vertebral fracture assessment (VFA) for the diagnosis of prevalent vertebral fracture (VF) in rheumatoid arthritis (RA) patients. METHODS: Total of 100 women with RA who were 50 years or older were enrolled. All participants underwent lateral imaging of the thoraco-lumbar spine by radiography and VFA. All radiographs were analyzed by two radiologists. Discrepancies between radiologists for spine radiography were resolved by consensus and these results were defined as the reference standard. VFA interpretation was done independently by two nuclear medicine physicians. Fracture defined by VFA measure was done only when two physicians both independently reported fracture. The inter-rater agreement for the detection of VF on VFA was evaluated by kappa statistics. We calculated percent values for the diagnostic validity of VFA in detecting VF in the 100 women as a whole and according to the presence of previous fracture or back pain. RESULTS: The prevalence of VF identified by spine radiography was 47%. Inter-rater agreement of VFA per vertebra by two VFA readers showed moderate agreement (kappa=0.60). The sensitivity, PPV, specificity and NPV of VFA compared to spine radiography were 57.3%, 30.9%, 89.1% and 96.1% for total vertebrae. All patients with history of previous VF (n=13) were visualized with VFA with 100% sensitivity but it has 64.7% sensitivity and 79.3% specificity in patients without previous VF (n=87). CONCLUSION: VFA is most useful to identify patients without VF because of its high specificity and NPV in all spine level.