scholarly journals Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 471
Author(s):  
You-Zhen Feng ◽  
Sidong Liu ◽  
Zhong-Yuan Cheng ◽  
Juan C. Quiroz ◽  
Dana Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Progression Prediction ◽  
The Relationship ◽  
Potential Use

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

scholarly journals Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT

2020 ◽  
Author(s):  
Youzhen Feng ◽  
Sidong Liu ◽  
Zhongyuan Cheng ◽  
Juan Quiroz ◽  
Data Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Progression Prediction ◽  
The Relationship ◽  
Potential Use

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. [Manuscript last updated on 20 May, 2020.]

Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT (Preprint)

2021 ◽  
Author(s):  
You-Zhen Feng ◽  
Sidong Liu ◽  
Zhong-Yuan Cheng ◽  
Juan C. Quiroz ◽  
Dana Rezazadegan ◽  
...  
Keyword(s):  
Disease Progression ◽  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Severity Assessment ◽  
Prognostic Information ◽  
Feature Vectors ◽  
Institutional Review ◽  
Retrospective Nature ◽  
Progression Prediction

BACKGROUND Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. OBJECTIVE This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. METHODS A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. RESULTS Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. CONCLUSIONS The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. CLINICALTRIAL we performed a retrospective in China. This multicentre study was approved by the institutional review board of the principal investigator’s hospital. Informed consent from patients was exempted due to the retrospective nature of this study.

scholarly journals DABC-Net for robust pneumonia segmentation and prediction of COVID-19 progression on chest CT scans

2020 ◽  
Author(s):  
Xiao-Yong Zhang ◽  
Ziqi Yu ◽  
Xiaoyang Han ◽  
Botao Zhao ◽  
Yaoyao Zhuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
State Of The Art ◽  
Chest Ct ◽  
Ct Scans ◽  
Patient Specific ◽  
Specific Prediction ◽  
Lstm Network ◽  
Progression Prediction ◽  
Ct Slices

Abstract Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic.

scholarly journals One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

2021 ◽  
Author(s):  
Aram Ter-Sarkisov
Keyword(s):  
Source Code ◽  
Chest Ct ◽  
Training Data ◽  
Ct Scans ◽  
Affinity Matrix ◽  
True Negative ◽  
Data Manipulation ◽  
True Negative Rate ◽  
Affinity Model ◽  
The Relationship

AbstractWe introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation of an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the affinity between the lesion masks’ features extracted from the image. First, a batch of vectorized lesion masks is constructed. Then, the model learns the parameters of the affinity matrix that captures the relationship between features in each vector. Finally, the affinity is expressed as a single vector of pre-defined length. Without any complicated data manipulation, class balancing tricks, and using only a fraction of the training data, we achieve a 91.74% COVID-19 sensitivity, 85.35% common pneumonia sensitivity, 97.26% true negative rate and 91.94% F1-score. Ablation studies show that the method can quickly generalize to new datasets. All source code, models and results are publicly available on https://github.com/AlexTS1980/COVID-Affinity-Model.

The ACPA isotype profile reflects long-term radiographic progression in rheumatoid arthritis

2010 ◽  
Vol 69 (6) ◽  
pp. 1110-1116 ◽  
Author(s):  
Diane van der Woude ◽  
Silje W Syversen ◽  
Ellen I H van der Voort ◽  
Kirsten N Verpoort ◽  
Guro L Goll ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Progression ◽  
Enzyme Linked Immunosorbent Assay ◽  
Prognostic Information ◽  
Radiographic Damage ◽  
The Relationship ◽  
Acpa Status ◽  
Citrullinated Protein

BackgroundThe presence of anti-citrullinated protein antibodies (ACPA) is a powerful predictive factor for the development and progression of rheumatoid arthritis (RA). The ACPA response has been shown to consist of various isotypes, but the consequences of differences in isotype distribution have not been extensively investigated.ObjectiveTo investigate the relationship between ACPA isotypes, disease progression and radiological outcome.MethodsACPA isotypes were determined in sera of anti-cyclic citrullinated peptide 2-positive patients by enzyme-linked immunosorbent assay (ELISA). To investigate whether the ACPA response continues to evolve during disease development, the ACPA isotype profile during progression of undifferentiated arthritis (UA) to RA was studied. The association of disease progression with ACPA isotype use was assessed using long-term radiographic follow-up data from patients with RA in two independent cohorts.ResultsThe ACPA isotype distribution did not expand during disease progression from UA to RA, but was relatively stable over time. In both RA cohorts, the baseline ACPA isotype profile was a significant predictor of disease severity, with more isotypes indicating a higher risk of radiographic damage (odds ratio for every additional isotype: 1.4 (95% CI 1.1 to 1.9) p<0.001). ACPA isotypes supplied additional prognostic information to ACPA status alone, even after correction for other predictive factors.ConclusionsThe magnitude of the ACPA isotype profile at baseline reflects the risk of future radiographic damage. These results indicate that the presence and the constitution of the ACPA response are relevant to the disease course of RA.

scholarly journals The Pulmonary Sequalae in Discharged Patients With COVID-19：A Short-term Observational Study

2020 ◽  
Author(s):  
Dehan Liu ◽  
Wanshu Zhang ◽  
Feng Pan ◽  
Lin Li ◽  
Lian Yang ◽  
...  
Keyword(s):  
Lung Lesion ◽  
Chest Ct ◽  
Ct Scans ◽  
Optimal Time ◽  
Imaging Features ◽  
Affecting Factors ◽  
Short Term ◽  
Cumulative Percentage ◽  
Time Point

Abstract Background: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of the complete radiological resolution at each time point, to explore the relevant affecting factors, and describe the chest CT findings through different timepoints after hospital discharge.Methods: Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from hospital between 5 February 2020 to 10 March 2020 and underwent serial chest CT scans on schedule were enrolled. Radiological demonstrations of all patients were collected and analyzed. The total CT score was the sum of non-GGO invovlement determined at discharge. Afterwards, all patients underwent chest CT scans at 1st, 2nd, and 3rd week after discharge. Imaging features and distribution were analyzed across different time points.Results: 149 patients who completed all CT scans were evaluated, 67 (45.0%) men and 82 (55.0%) women with median age of 43 years old (IQR 36-56). The cumulative percentage of the complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), 53% (79) at discharge and the 1st, 2nd, and 3rd week after discharge, respectively. Patients ≤44 years old showed a significantly higher CP than patients >44 years old after 3-week follow-up. The predominant pattern of abnormality observed at discharge were ground-glass opacification(GGO) (65 [43.6%]), fibrous stripe (45 [30.2%]), and thickening of the adjacent pleura (16 [10.7%]). Lung lesion showed obvious resolution from 2 to 3 weeks after discharge, especially in GGO and fibrous stripe. “Tinted” sign and branchovascular bundle distortion as two special features were discovered in the evolution.Conclusion: Lung lesion of COVID-19 pneumonia patient can be absorbed completely in short-term follow-up with no sequelae. 3 weeks after discharge might be the optimal time point for early radiological estimation.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

scholarly journals Human TERT promoter mutations as a prognostic biomarker in glioma

2021 ◽  
Vol 147 (4) ◽  
pp. 1007-1017
Author(s):  
Branka Powter ◽  
Sarah A. Jeffreys ◽  
Heena Sareen ◽  
Adam Cooper ◽  
Daniel Brungs ◽  
...  
Keyword(s):  
Clinical Utility ◽  
Nucleotide Polymorphism ◽  
Liquid Biopsies ◽  
Single Nucleotide ◽  
Tert Promoter ◽  
Tert Promoter Mutations ◽  
Potential Clinical Utility ◽  
Promoter Mutations ◽  
The Relationship ◽  
Potential Use

AbstractThe TERT promoter (pTERT) mutations, C228T and C250T, play a significant role in malignant transformation by telomerase activation, oncogenesis and immortalisation of cells. C228T and C250T are emerging as important biomarkers in many cancers including glioblastoma multiforme (GBM), where the prevalence of these mutations is as high as 80%. Additionally, the rs2853669 single nucleotide polymorphism (SNP) may cooperate with these pTERT mutations in modulating progression and overall survival in GBM. Using liquid biopsies, pTERT mutations, C228T and C250T, and other clinically relevant biomarkers can be easily detected with high precision and sensitivity, facilitating longitudinal analysis throughout therapy and aid in cancer patient management.In this review, we explore the potential for pTERT mutation analysis, via liquid biopsy, for its potential use in personalised cancer therapy. We evaluate the relationship between pTERT mutations and other biomarkers as well as their potential clinical utility in early detection, prognostication, monitoring of cancer progress, with the main focus being on brain cancer.

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