Study of the K_{L}→π^{0}νν[over ¯] Decay at the J-PARC KOTO Experiment

The rare decay K_{L}→π^{0}νν[over ¯] was studied with the dataset taken at the J-PARC KOTO experiment in 2016, 2017, and 2018. With a single event sensitivity of (7.20±0.05_{stat}±0.66_{syst})×10^{-10}, three candidate events were observed in the signal region. After unveiling them, contaminations from K^{±} and scattered K_{L} decays were studied, and the total number of background events was estimated to be 1.22±0.26. We conclude that the number of observed events is statistically consistent with the background expectation. For this dataset, we set an upper limit of 4.9×10^{-9} on the branching fraction of K_{L}→π^{0}νν[over ¯] at the 90% confidence level.

Blockade of BK channels attenuates chronic visceral hypersensitivity in an IBS-like rat model

BACKGROUND

Visceral hypersensitivity in irritable bowel syndrome (IBS) is still poorly understood, despite that chronic abdominal pain is the most common symptoms in IBS patients. To study effects of BK channels on visceral hypersensitivity in IBS rats and the underlying mechanisms, IBS rats were established by colorectal distention (CRD) in postnatal rats. The expression of large-conductance calcium and voltage-dependent potassium ion channels (BK channels) of the thoracolumbar spinal cord was examined in IBS and control rats. The effects of BK channel blockade on visceral hypersensitivity were evaluated. The interaction of BK channels and N-methyl-D-aspartate acid (NMDA) receptors was explored, and synaptic transmission at superficial dorsal horn (SDH) neurons of the thoracolumbar spinal cord was recorded by whole-cell patch clamp in IBS rats.

RESULTS

The expression of the BK channels of the thoracolumbar spinal cord in IBS rats was significantly reduced. The blockade of BK channels could reduce the visceral hypersensitivity in IBS rats. There was an interaction between BK channels and NMDA receptors in the spinal cord. The frequency of spontaneous inhibitory postsynaptic currents (sIPSCs) in SDH neurons is significantly reduced in IBS rats. The blockade of BK channels depolarizes the inhibitory interneuron membrane and increases their excitability in IBS rats.

CONCLUSIONS

BK channels could interact with NMDA receptors in the thoracolumbar spinal cord of rats and regulate visceral hypersensitivity in IBS rats.

Reducing scan time of paediatric 99mTc-DMSA SPECT via deep learning

AIM

To investigate the feasibility of reducing the scan time of paediatric technetium 99m (^99mTc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method.

MATERIAL AND METHODS

A total of 112 paediatric ^99mTc-DMSA renal SPECT scans were analysed retrospectively. Of the 112 examinations, 88 (84 for training and four for validation) were used to train a DL-based model that could generate full-acquisition-time reconstructed SPECT images from half-time acquisition. The remaining 24 examinations were used to evaluate the performance of the trained model.

RESULTS

DL-based SPECT images obtained from half-time acquisition have image quality similar to the standard clinical SPECT images obtained from full-acquisition-time acquisition. Moreover, the accuracy, sensitivity and specificity of the DL-based SPECT images for detection of affected kidneys were 91.7%, 83.3%, and 100%, respectively.

CONCLUSION

These preliminary results suggest that DL has the potential to reduce the scan time of paediatric ^99mTc-DMSA SPECT imaging while maintaining diagnostic accuracy.

Toward High Precision XCO2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO2 processing.

Phase states, microstructure and dielectric characteristics of solid solutions (1 - x)NaNbO3 - xCa2Nb2O7 and (1 - x)NaNbO3 - xSr2Nb2O7

Ceramics of binary systems solid solutions (1 – x)NaNbO₃ – xCa₂Nb₂O₇ and (1 – x)NaNbO₃ – xSr₂Nb₂O₇ with non-isostructural extreme components were prepared by the solid-phase reactions technique with the following sintering using conventional ceramic technology. It was found that ceramics with x ≤ 0.2 have a perovskite structure. Layered type of structure predominates in the concentration range 0.2 < x ≤ 1. Phase diagrams of both systems at room temperature have been determined in the perovskite area. It was shown that this area contains two concentration regions with the different crystal structures and the morphotropic phase boundary between them. Microstructure and dielectric characteristics of selected solid solutions were investigated. The influence of technological regulations, such as mechanical activation and variation of sintering temperatures, on the formation of the microstructure and dielectric characteristics was studied for the individually selected concentrations (x = 0.1 and x = 0.25). Dielectric characteristics of ceramics revealed the presence of the Maxwell-Wagner polarization and its corresponding relaxation in the solid solutions (1 – x)NaNbO₃ – xCa₂Nb₂O₇ at x > 0.20.

International normalised ratio as an independent predictor of mortality in limb necrotising fasciitis with sepsis

INTRODUCTION

Necrotising fasciitis with sepsis is a life threatening disease. The aim of this study was to analyse the association between international normalised ratio (INR) and mortality in sepsis patients with necrotising fasciitis.

METHODS

A retrospective review was undertaken of 106 patients suffering from necrotising fasciitis with sepsis between November 2007 and December 2016. Data on comorbidities, clinical manifestations, laboratory findings, causative microbiological organisms, APACHE II (Acute Physiology and Chronic Health Evaluation II) score and outcomes were extracted. Logistic regression was carried out to examine the factors affecting mortality.

RESULTS

Forty patients (37.7%) died. There was no significant difference in the white blood count (WBC) for the survivor and non-survivor groups. Non-survivors had a lower mean oxygenation index (OI) (288.7mmHg vs 329.4mmHg, p=0.032) and platelet count (PC) (139.5 vs 214.8 x 10⁹/l, p=0.028), and a higher mean INR (1.9 vs 1.3, p=0.000), activated partial thromboplastin time (APTT) (54.6 vs 44.2 seconds, p=0.005) and serum creatinine (2.3mg/dl vs 1.4mg/dl, p=0.007). Mortality in patients with INR >1.5 was significantly higher than in those with INR <1.5 when all risk factors (WBC, PC, OI, INR, APTT, creatinine) were considered (odds ratio: 4.414, 95% confidence interval: 1.263-15.428, p=0.020). Even after adjusting for age, sex, bacteraemia, diabetes and hepatic disorders, the data still exhibited elevated mortality for patients with INR >1.5 (odds ratio: 5.600, 95% confidence interval: 1.415-22.166, p=0.014).

CONCLUSIONS

INR is a significant independent predictor of mortality in sepsis patients diagnosed with necrotising fasciitis.

The invasion status of lymphovascular space and lymph nodes in cervical cancer assessed by mono-exponential and bi-exponential DWI-related parameters

AIM

To investigate whether mono-exponential and bi-exponential diffusion-weighted imaging (DWI)-related parameters of the primary tumour can evaluate the status of lymphovascular space invasion (LVSI) and lymph node metastasis (LNM) in patients with cervical carcinoma preoperatively.

MATERIALS AND METHODS

Eighty patients with cervical carcinoma were enrolled, who underwent preoperative multi b-value DWI and radical hysterectomy. They were classified into LVSI(+) versus LVSI(-) and LNM(+) versus LNM(-) according to postoperative pathology. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D∗), and perfusion fraction (f) were calculated from the whole tumour (__whole) and tumour margin (__margin). All parameters were compared between LVSI(+) and LVSI(-) and between LNM(+) and LNM(-). Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic performance of these parameters.

RESULTS

f__margin and D∗__whole showed significant differences in differentiating LVSI(+) from LVSI(-) tumours (p=0.002, 0.008, respectively), while LNM(+) tumours presented with significantly higher ADC__margin than that of LNM(-) tumours (p=0.009). The other parameters were not independent related factors with the status of LVSI or LNM according to logistic regression analysis (p>0.05). The area under the ROC curve of f__margin combined with D∗__whole in discriminating LVSI(+) from LVSI(-) was 0.826 (95% confidence interval [CI]: 0.691-0.961), while ADC__margin in differentiating LNM(+) from LNM(-) was 0.788 (95% CI: 0.648-0.928).

CONCLUSIONS

The parameters generated from mono-exponential and bi-exponential DWI of the primary cervical carcinoma could help discriminate its status regarding LVSI (f__margin and D∗__whole) and LNM (ADC__margin).

SAT0564 BONE TEXTURE ANALYSIS WITH DEEP LEARNING IN HAND RADIOGRAPHS FOR ASSESSING THE RISK OF RHEUMATOID ARTHRITIS

Background:

Conventional x-rays are essential to identify radiographic changes of rheumatoid arthritis (RA) in structure and bone texture. Limited evidence suggests that the bone texture analysis may quantify the radiographic changes in RA;1however, current techniques such as the fractal dimension characterize fixed texture features. Deep learning offers novel methods to ‘learn’ radiographic texture features relevant to RA.

Objectives:

To develop a deep learning model to assess the radiographic bone texture in the distal metacarpal bone relevant to RA.

Methods:

We collected 3,738 conventional hand radiographs from 2,128 individuals (RA, n = 908; non-RA, n = 1220). The second, third, and fourth metacarpal bone images were segmented using a curve Graph Convolutional Network (GCN), and the distal third was used as the input to train a texture model to classify RA. The texture model was based on the Deep Texture Encoding Network (Deep-TEN) architecture (figure 1),2which put an encoding layer on top of a pre-trained 18-layered residual network (ResNet18). The vectors produced by the model represent the orderless texture features that were used to generate a texture score for RA. Five texture models are trained using 5-fold cross-validation and are ensembled during inference by averaging the model outputs to produce the final score. We then validate the model using hand radiographs of 166 RA patients and 166 non-RA patients. Overall model performance was measured by area under the curve of the receiver operator curve (AUROC). Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) of RA.

Figure 1.

Schematic representation of deep learning models to extract and encode texture features for RA classification.

Results:

We included 140 women and 26 men with RA (mean age, 55.9±1.8 years) and 166 non-RA individuals (F: M, 140:26; mean age, 55.5 ± 1.8 years). The mean texture score was 0.49 (95% CI, 0.48–0.50) in RA patients, which is significantly higher than non-RA patients (0.42, 95% CI, 0.40–0.43; p<0.01). The AUROC of the model was 0.68. In the multivariate logistic regression model, a high texture score (>0.43) is associated with an OR (95% CI) of 3.42 (2.48–4.72) for RA, adjusted by age and sex.

Conclusion:

This study indicates that the texture model can delineate radiographic changes in texture relevant to RA and, coupled with automatic joint detection and segmentation, it has the potential to aid early RA diagnosis and monitor radiographic progression.

References:

[1]Zandieh S, Haller J, Bernt R, et al. Fractal analysis of subchondral bone changes of the hand in rheumatoid arthritis. Medicine (Baltimore) 2017;96(11):e6344.

[2]Zhang H, Xue J, Dana K. Deep TEN: Texture Encoding Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:708-17.

Disclosure of Interests:

None declared

OP0301 PREDICTION OF LOW BONE MINERAL DENSITY AND FRAX SCORE BY ASSESSING HIP BONE TEXTURE WITH DEEP LEARNING

Background:

Osteoporosis is a widespread health concern associated with an increased risk of fractures in individuals with low bone mineral density (BMD). Dual-energy x-ray absorptiometry (DXA) is the gold standard to measure BMD, but methods based on the assessment of plain films, such as the digital radiogrammetry,1are also available. We describe a novel approach based on the assessment of hip texture with deep learning to estimate BMD.

Objectives:

To compare the BMD estimated by assessing hip texture using a deep learning model and that measured by DXA.

Methods:

In this study, we identified 1,203 patients who underwent DXA of left hip and hip plain film within six months. The dataset was split into a training set with 1,024 patients and a testing set with 179 patients. Hip images were obtained and regions of interest (ROI) around left hips were segmented using a tool based on the curve Graph Convolutional Network. The ROIs are processed using a Deep Texture Encoding Network (Deep-TEN) model,2which comprises the first 3 blocks of Residual Network with 18 layers (ResNet-18) model followed by a dictionary encoding operator (Figure 1). The encoded features are processed using a fully connected layer to estimate BMD. Five-fold cross-validation was conducted. Pearson’s correlation coefficient was used to assess the correlation between predicted and reference BMD. We also test the performance of the model to identify osteoporosis (T-score ≤ -2.5)

Figure 1.

Schematic representation of deep learning models to extract and encode texture features for estimation of hip bone density.

Results:

We included 151 women and 18 men in the testing dataset (mean age, 66.1 ± 1.7 years). The mean predicted BMD was 0.724 g/cm2compared with the mean BMD measured by DXA of 0.725 g/cm2(p = 0.51). Pearson’s correlation coefficient between predicted and true BMD was 0.88. The performance of the model to detect osteoporosis/osteopenia was shown in Table 1. The positive predictive value was 87.46% for a T-score ≤ -1 and 83.3% for a T-score ≤ -2.5. Furthermore, the mean FRAX® 10-year major fracture risk did not differ significantly between scores based on predicted (6.86%) and measured BMD (7.67%, p=0.52). The 10-year probability of hip fracture was lower in the predicted score (1.79%) than the measured score (2.43%, p = 0.01).

Table 1.

Performance matrices of the deep texture model to detect osteoporosis/osteopenia

T-score ≤ -1T-score ≤ -2.5Sensitivity91.11%(95% CI, 83.23% to 96.08%)33.33%(95% CI, 17.29% to 52.81%)Specificity86.08%(95% CI, 76.45% to 92.84%)98.56%(95% CI, 94.90% to 99.83%)Positive predictive value88.17%(95% CI, 81.10% to 92.83%)83.33%(95% CI, 53.58% to 95.59%)Negative predictive value89.47%(95% CI, 81.35% to 94.31%)87.26%(95% CI, 84.16% to 89.83%)

Conclusion:

This study demonstrates the potential of the bone texture model to detect osteoporosis and to predict the FRAX score using plain hip radiographs.

References:

[1]Zandieh S, Haller J, Bernt R, et al. Fractal analysis of subchondral bone changes of the hand in rheumatoid arthritis. Medicine (Baltimore) 2017;96(11):e6344.

[2]Zhang H, Xue J, Dana K. Deep TEN: Texture Encoding Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:708-17.

Disclosure of Interests:

None declared

OP0062 PREDICTIVE VALUE OF BONE TEXTURE FEATURES EXTRACTED BY DEEP LEARNING MODELS FOR THE DETECTION OF OSTEOARTHRITIS: DATA FROM THE OSTEOARTHRITIS INITIATIVE

Background:

Osteoarthritis is a degenerative disorder characterized by radiographic features of asymmetric loss of joint space, subchondral sclerosis, and osteophyte formation. Conventional plain films are essential to detect structural changes in osteoarthritis. Recent evidence suggests that fractal- and entropy-based bone texture parameters may improve the prediction of radiographic osteoarthritis.1In contrast to the fixed texture features, deep learning models allow the comprehensive texture feature extraction and recognition relevant to osteoarthritis.

Objectives:

To assess the predictive value of deep learning-extracted bone texture features in the detection of radiographic osteoarthritis.

Methods:

We used data from the Osteoarthritis Initiative, which is a longitudinal study with 4,796 patients followed up and assessed for osteoarthritis. We used a training set of 25,978 images from 3,086 patients to develop the textual model. We use the BoneFinder software2to do the segmentation of distal femur and proximal tibia. We used the Deep Texture Encoding Network (Deep-TEN)3to encode the bone texture features into a vector, which is fed to a 5-way linear classifier for Kellgren and Lawrence grading for osteoarthritis classification. We also developed a Residual Network with 18 layers (ResNet18) for comparison since it deals with contours as well. Spearman’s correlation coefficient was used to assess the correlation between predicted and reference KL grades. We also test the performance of the model to identify osteoarthritis (KL grade≥2).

Results:

We obtained 6,490 knee radiographs from 446 female and 326 male patients who were not in the training sets to validate the performance of the models. The distribution of the KL grades in the training and testing sets were shown in Table 1. The Spearman’s correlation coefficient was 0.60 for the Deep-TEN and 0.67 for the ResNet18 model. Table 2 shows the performance of the models to detect osteoarthritis. The positive predictive value for Deep-TEN and ResNet18 model classification for OA was 81.37% and 87.46%, respectively.

Table 1

Distribution of KL grades in the training and testing sets.

KL grades01234TotalTraining set1089341.9%458218.7%611423.5%332012.8%7993.1%25,978Testing set247238.1%135320.8%169626.1%77511.9%1943.0%6,490Table 2

Performance matrices of the Deep-Ten and ResNet18 models to detect osteoarthritis

Deep-TENResNet18Sensitivity62.29%(95% CI, 60.42%–64.13%)59.14%(95% CI, 57.24%–61.01%)Specificity90.07%(95% CI, 89.07%–91.00%)94.09%(95% CI, 93.30%–94.82%)Positive predictive value81.37%(95% CI, 79.81%–82.84%)87.46%(95% CI, 85.96%–88.82%)Negative predictive value77.42%(95% CI, 77.64%–79.65%)76.77%(95% CI, 75.93%–77.59%)

Conclusion:

This study demonstrates that the bone texture model performs reasonably well to detect radiographic osteoarthritis with a similar performance to the bone contour model.

References:

[1]Bertalan Z, Ljuhar R, Norman B, et al. Combining fractal- and entropy-based bone texture analysis for the prediction of osteoarthritis: data from the multicenter osteoarthritis study (MOST). Osteoarthritis Cartilage 2018;26:S49.

[2]Lindner C, Wang CW, Huang CT, et al. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Sci Rep 2016;6:33581.

[3]Zhang H, Xue J, Dana K. Deep TEN: Texture Encoding Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:708-17.

Disclosure of Interests:

None declared