A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning.

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.

A Closer Look at the Joint Training of Object Detection and Re-identification in Multi-Object Tracking.

Unifying object detection and re-identification (ReID) into a single network enables faster multi-object tracking (MOT), while this multi-task setting poses challenges for training. In this work, we dissect the joint training of detection and ReID from two dimensions: label assignment and loss function. We find previous works generally overlook them and directly borrow the practices from object detection, inevitably causing inferior performance. Specifically, we identify a qualified label assignment for MOT should: 1) have the assignment cost aware of ReID cost, not just detection cost; 2) provide sufficient positive samples for robust feature learning while avoiding ambiguous positives (i.e., the positives shared by different ground-truth objects). To achieve the above goals, we first propose Identity-aware Label Assignment, which jointly considers the assignment cost of detection and ReID to select positive samples for each instance without ambiguities. Moreover, we advance a novel Discriminative Focal Loss that integrates ReID predictions with Focal Loss to focus the training on the discriminative samples. Finally, we upgrade the strong baseline FairMOT with our techniques and achieve up to 7.0 MOTA / 54.1% IDs improvements on MOT16/17/20 benchmarks under favorable inference speed, which verifies our tailored label assignment and loss function for MOT are superior to those inherited from object detection.

The diagnostic value of dynamic volume computed tomography angiography in children with anomalous origin of the left coronary artery from the pulmonary artery.

There have been almost no reports on the technique of dynamic volume computed tomography angiography (DVCTA) in children with anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA). Twelve children with ALCAPA, aged 5 months to 15 years, were enrolled in this retrospective study to explore the clinical value of DVCTA in the diagnosis of ALCAPA in children. All patients underwent low-dose prospective ECG-gated 320-slice DVCTA and transthoracic echocardiography. Two radiologists evaluated the image quality of the DVCTA and recorded the radiation dose at the same time. The accuracy of DVCTA in the diagnosis of ALCAPA was 100%, with the left coronary artery (LCA) opening in the left wall of the pulmonary artery in 4 cases (33.3%), the right wall in 2 cases (16.7%), and the posterior wall in 6 cases (50.0%). All children completed 320-slice DVCTA at a single timepoint; all of the images were diagnosable, and the subjective score was 3.3±0.6, with good consistency between the evaluations performed by the two radiologists (k=0.79). From the echocardiographs of these cases, 4 cases (33.3%) of ALCAPA were diagnosed correctly, 4 cases (33.3%) were misdiagnosed as LCA-pulmonary artery fistula, and 4 cases (33.3%) were missed, including a small LCA that was not displayed in 2 cases. The average CT radiation dose was 0.83±0.57 mSv. Low-dose DVCTA clearly showed the origin, course, and collateral vessels of ALCAPA and could be used reliably for noninvasive diagnosis of ALCAPA in children.

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.

Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for Real-Time Siamese Tracking.

Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework. Although they have drawn continuous interest from the visual tracking community due to the proper trade-off between accuracy and speed, both stages are easily sensitive to the distracters in search branch, thereby inducing unreliable response positions. To fill this gap, we advance Siamese trackers with two novel non-local blocks named Nocal-Siam, which leverages the long-range dependency property of the non-local attention in a supervised fashion from two aspects. First, a target-aware non-local block (T-Nocal) is proposed for learning the target-guided feature weights, which serve to refine visual features of both target and search branches, and thus effectively suppress noisy distracters. This block reinforces the interplay between both target and search branches in the first stage. Second, we further develop a location-aware non-local block (L-Nocal) to associate multiple response maps, which prevents them inducing diverse candidate target positions in the future coming frame. Experiments on five popular benchmarks show that Nocal-Siam performs favorably against well-behaved counterparts both in quantity and quality.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

Hypertension Control in Adults With CKD in China: Baseline Results From the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE).

BACKGROUND: Hypertension contributes to increased morbidity and mortality in the chronic kidney disease (CKD) population. Studies on blood pressure control in CKD patients in China are limited. In this study, we aimed to describe the status of blood pressure control in Chinese CKD patients based on the first national prospective CKD cohort data. METHODS: A subgroup of Chinese Cohort Study of Chronic Kidney Disease participants with hypertension at baseline was included in the present study. Uncontrolled blood pressure was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg. Defined daily doses (DDDs) are used as a standard measurement of drug utilization in this population. Factors associated with uncontrolled blood pressure were analyzed using multivariable logistic regression. RESULTS: There were 2,251 hypertensive CKD subjects among 2,873 predialysis CKD participants. The awareness, treatment, and control rates of hypertension were 80.7%, 95.6%, and 57.1%, respectively. Factors independently associated with uncontrolled blood pressure were overweight, obesity, albuminuria, decreased estimated glomerular filtration rate (eGFR), and diabetes. Over 50% of study subjects were prescribed 2 or more antihypertensive medications and only 7% were prescribed diuretics. Uncontrolled hypertensive patients were prescribed less antihypertensive medication than controlled hypertensives (DDD 1.3 [1.0-2.3] vs. 2.0 [1.0-3.1], P < 0.001). CONCLUSIONS: Hypertension control was suboptimal among hypertensive CKD patients in China, especially among those overweight or with obesity, albuminuria, lower eGFR, and diabetes. Patients with uncontrolled hypertension should undergo treatment regimen evaluation to select the appropriate dosage and type of antihypertensive medications.