scholarly journals Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Edward H. Lee ◽  
Jimmy Zheng ◽  
Errol Colak ◽  
Maryam Mohammadzadeh ◽  
Golnaz Houshmand ◽  
...  
Keyword(s):  
International Institutions ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Chest Ct ◽  
Disease Course ◽  
Chest Computed Tomography ◽  
Open Questions ◽  
Normal Controls ◽  
Ct Volume

AbstractThe Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

scholarly journals Analyses of Changing Disease Patterns and Factors Related to Disease Outcomes in Patients with Crohn’s Disease after 7 Years of Follow-up: A Single-center Retrospective Study

2021 ◽  
Author(s):  
Dong Yoon Han ◽  
So Hyun Park ◽  
Mirinae Seo ◽  
Seong Jin Park ◽  
Zi-Xin Liu ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Area Under The Curve ◽  
Disease Course ◽  
Disease Patterns ◽  
Disease Characteristics ◽  
Disease Behavior ◽  
Significant Factors

Abstract Background: The clinical spectrum and disease course of Crohn’s disease (CD) are heterogeneous and difficult to predict based on initial presentation. Aim: To analyze the long-term disease course and factors leading to poor prognosis of the disease.Methods: In total, 112 patients with CD who were initially diagnosed or treated at our institution were included. We analyzed their clinical data, disease characteristics according to Montreal classification, endoscopic and computed tomography (CT) examinations at initial visit, and 2-year, 5-year, and last follow-ups. We categorized the long-term disease course into four categories: remission, stable, chronic refractory, and chronic relapsing. Significant factors associated with a poorer prognosis were analyzed.Results: The median follow-up period was 107 (range, 61-139) months. Complicated disease behavior increased slightly (20.5% to 26.2%). Chronic refractory (19.6%) and relapsing (16.1%) courses were defined as unfavorable disease course. Two-year disease characteristics were significant factors for unfavorable disease course, and the combination of 2-year perianal disease and 2-year moderate-to-severe CT activity could predict unfavorable disease course with the highest accuracy (0.722, area under the curve 0.768, p<.0001). Conclusions: One-third of our CD patients showed an unfavorable disease course (35.7%) and 2-year disease characteristics were significant factors for an unfavorable disease course.

scholarly journals Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Musa Sani Musa ◽  
Mubarak Taiwo Mustapha ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Chest Radiography ◽  
Area Under The Curve ◽  
Chest Ct ◽  
Chest Computed Tomography ◽  
Classification Tasks ◽  
Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

scholarly journals A multiple sclerosis disease progression measure based on cumulative disability

2021 ◽  
pp. 135245852098863
Author(s):  
Ali Manouchehrinia ◽  
Elaine Kingwell ◽  
Feng Zhu ◽  
Helen Tremlett ◽  
Jan Hillert ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Disease Course ◽  
Cross Sectional ◽  
Age Related ◽  
Similar Disease ◽  
Longitudinal Comparison ◽  
Using Data

Background: Existing severity measurements in multiple sclerosis (MS) are often cross-sectional, making longitudinal comparisons of disease course between individuals difficult. Objective: The objective of this study is to create a severity metric that can reliably summarize a patient’s disease course. Methods: We developed the nARMSS – normalized ARMSS (age-related MS severity score) over follow-up, using the deviation of individual ARMSS scores from the expected value and integrated over the corresponding time period. The nARMSS scales from −5 to +5; a positive value indicates a more severe disease course for a patient when compared to other patients with similar disease timings. Results: Using Swedish MS registry data, the nARMSS was tested using data at 2 and 4 years of follow-up to predict the most severe quartile during the subsequent period up to 10 years total follow-up. The metric used was area under the curve of the receiver operating characteristic (AUC-ROC). This resulted in measurements of 0.929 and 0.941. In an external Canadian validation cohort, the equivalent AUC-ROCs were 0.901 and 0.908. Conclusion: The nARMSS provides a reliable, generalizable and easily measurable metric which makes longitudinal comparison of disease course between individuals feasible.

scholarly journals More than six weeks to resolve from CT manifestations in COVID-19 patient: A case report

2020 ◽  
Author(s):  
Wenxiong Xu ◽  
Ziying Lei ◽  
Dabiao Chen ◽  
Xuejun Li ◽  
Zhanlian Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Health Care Providers ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
Care Providers ◽  
Case Presentation ◽  
Chest Computed Tomography ◽  
Guangzhou City

Abstract Background: Coronavirus Disease 2019 (COVID-19) outbroke in Wuhan and spread to the world recently. But there were little studies on how long it took to recover from treatment beginning and resolve from chest computed tomography (CT) imaging so far.Case presentation: A patient diagnosed with severe type of COVID-19 was reported in this study. He was the first patient recovered and discharged from our hospital located in Guangzhou city. Initial chest computed tomography (CT) images of him showed bilateral multiple lobular peripheral ground-glass opacities without consolidation. Features and changes of his chest CT images from admission to discharge and follow-up were demonstrated. It took more than six weeks for lesion resolution in CT manifestations although the symptoms improved for a period of time after proper treatment. Conclusions: Repeated chest CT imaging for a period of more than six weeks in patients of COVID-19 is necessary to ascertain the lesion resolution and completely recovery. The result could be supplementary data to COVID-19 and help health care providers manage the COVID-19 patients.

Does Hospitalization Predict the Disease Course in Ulcerative Colitis? Prevalence and Predictors of Hospitalization and Re- Hospitalization in Ulcerative Colitis in a Population-based Inception Cohort (2000-2012)

2015 ◽  
Vol 24 (3) ◽  
pp. 287-292 ◽  
Author(s):  
Petra A. Golovics ◽  
Laszlo Lakatos ◽  
Michael D. Mandel ◽  
Barbara D. Lovasz ◽  
Zsuzsanna Vegh ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Cox Regression ◽  
Diagnostic Procedures ◽  
Population Based ◽  
Disease Course ◽  
Inception Cohort ◽  
Disease Extent ◽  
Cox Regression Analysis ◽  
Hospitalization Rates

Background & Aims: Limited data are available on the hospitalization rates in population-based studies. Since this is a very important outcome measure, the aim of this study was to analyze prospectively if early hospitalization is associated with the later disease course as well as to determine the prevalence and predictors of hospitalization and re-hospitalization in the population-based ulcerative colitis (UC) inception cohort in the Veszprem province database between 2000 and 2012. Methods: Data of 347 incident UC patients diagnosed between January 1, 2000 and December 31, 2010 were analyzed (M/F: 200/147, median age at diagnosis: 36, IQR: 26-50 years, follow-up duration: 7, IQR 4-10 years). Both in- and outpatient records were collected and comprehensively reviewed. Results: Probabilities of first UC-related hospitalization were 28.6%, 53.7% and 66.2% and of first re-hospitalization were 23.7%, 55.8% and 74.6% after 1-, 5- and 10- years of follow-up, respectively. Main UC-related causes for first hospitalization were diagnostic procedures (26.7%), disease activity (22.4%) or UC-related surgery (4.8%), but a significant percentage was unrelated to IBD (44.8%). In Kaplan-Meier and Cox-regression analysis disease extent at diagnosis (HR extensive: 1.79, p=0.02) or at last follow-up (HR: 1.56, p=0.001), need for steroids (HR: 1.98, p<0.001), azathioprine (HR: 1.55, p=0.038) and anti-TNF (HR: 2.28, p<0.001) were associated with the risk of UC-related hospitalization. Early hospitalization was not associated with a specific disease phenotype or outcome; however, 46.2% of all colectomies were performed in the year of diagnosis. Conclusion: Hospitalization and re-hospitalization rates were relatively high in this population-based UC cohort. Early hospitalization was not predictive for the later disease course.

scholarly journals Using advanced analysis of multifocal visual-evoked potentials to evaluate the risk of clinical progression in patients with radiologically isolated syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. M. Miguel ◽  
M. Roldán ◽  
C. Pérez-Rico ◽  
M. Ortiz ◽  
L. Boquete ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Area Under The Curve ◽  
Multifocal Visual Evoked Potentials ◽  
Radiologically Isolated Syndrome ◽  
Advanced Analysis ◽  
The Time Domain ◽  
Visual Evoked

AbstractThis study aimed to assess the role of multifocal visual-evoked potentials (mfVEPs) as a guiding factor for clinical conversion of radiologically isolated syndrome (RIS). We longitudinally followed a cohort of 15 patients diagnosed with RIS. All subjects underwent thorough ophthalmological, neurological and imaging examinations. The mfVEP signals were analysed to obtain features in the time domain (SNRmin: amplitude, Latmax: monocular latency) and in the continuous wavelet transform (CWT) domain (bmax: instant in which the CWT function maximum appears, Nmax: number of CWT function maximums). The best features were used as inputs to a RUSBoost boosting-based sampling algorithm to improve the mfVEP diagnostic performance. Five of the 15 patients developed an objective clinical symptom consistent with an inflammatory demyelinating central nervous system syndrome during follow-up (mean time: 13.40 months). The (SNRmin) variable decreased significantly in the group that converted (2.74 ± 0.92 vs. 4.07 ± 0.95, p = 0.01). Similarly, the (bmax) feature increased significantly in RIS patients who converted (169.44 ± 24.81 vs. 139.03 ± 11.95 (ms), p = 0.02). The area under the curve analysis produced SNRmin and bmax values of 0.92 and 0.88, respectively. These results provide a set of new mfVEP features that can be potentially useful for predicting prognosis in RIS patients.

scholarly journals Saliva RNA biomarkers predict concussion duration and detect symptom recovery: a comparison with balance and cognitive testing

2021 ◽  
Author(s):  
Gregory Fedorchak ◽  
Aakanksha Rangnekar ◽  
Cayce Onks ◽  
Andrea C. Loeffert ◽  
Jayson Loeffert ◽  
...  
Keyword(s):  
Test Performance ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Cognitive Testing ◽  
Cognitive Test ◽  
Clinical Tool ◽  
Post Injury ◽  
Symptom Recovery ◽  
Cognitive Test Performance

Abstract Objective The goals of this study were to assess the ability of salivary non-coding RNA (ncRNA) levels to predict post-concussion symptoms lasting ≥ 21 days, and to examine the ability of ncRNAs to identify recovery compared to cognition and balance. Methods RNA sequencing was performed on 505 saliva samples obtained longitudinally from 112 individuals (8–24-years-old) with mild traumatic brain injury (mTBI). Initial samples were obtained ≤ 14 days post-injury, and follow-up samples were obtained ≥ 21 days post-injury. Computerized balance and cognitive test performance were assessed at initial and follow-up time-points. Machine learning was used to define: (1) a model employing initial ncRNA levels to predict persistent post-concussion symptoms (PPCS) ≥ 21 days post-injury; and (2) a model employing follow-up ncRNA levels to identify symptom recovery. Performance of the models was compared against a validated clinical prediction rule, and balance/cognitive test performance, respectively. Results An algorithm using age and 16 ncRNAs predicted PPCS with greater accuracy than the validated clinical tool and demonstrated additive combined utility (area under the curve (AUC) 0.86; 95% CI 0.84–0.88). Initial balance and cognitive test performance did not differ between PPCS and non-PPCS groups (p > 0.05). Follow-up balance and cognitive test performance identified symptom recovery with similar accuracy to a model using 11 ncRNAs and age. A combined model (ncRNAs, balance, cognition) most accurately identified recovery (AUC 0.86; 95% CI 0.83–0.89). Conclusions ncRNA biomarkers show promise for tracking recovery from mTBI, and for predicting who will have prolonged symptoms. They could provide accurate expectations for recovery, stratify need for intervention, and guide safe return-to-activities.

scholarly journals Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

2021 ◽  
Author(s):  
Martina Pecoraro ◽  
Stefano Cipollari ◽  
Livia Marchitelli ◽  
Emanuele Messina ◽  
Maurizio Del Monte ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Imaging Biomarker ◽  
Cross Sectional ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Sectional Analysis ◽  
Ct And Mri ◽  
Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

scholarly journals Correlation between age, sex, and severity of Coronavirus disease-19 based on chest computed tomography severity scoring system

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Shimaa Farghaly ◽  
Marwa Makboul
Keyword(s):  
Computed Tomography ◽  
Scoring System ◽  
Fatal Outcome ◽  
Age Groups ◽  
Significant Risk ◽  
Chest Ct ◽  
Age Group ◽  
Severity Scoring ◽  
Chest Computed Tomography ◽  
Severity Of The Disease

Abstract Background Coronavirus disease 2019 (COVID-19) is the most recent global health emergency; early diagnosis of COVID-19 is very important for rapid clinical interventions and patient isolation; chest computed tomography (CT) plays an important role in screening, diagnosis, and evaluating the progress of the disease. According to the results of different studies, due to high severity of the disease, clinicians should be aware of the different potential risk factors associated with the fatal outcome, so chest CT severity scoring system was designed for semi-quantitative assessment of the severity of lung disease in COVID-19 patients, ranking the pulmonary involvement on 25 points severity scale according to extent of lung abnormalities; this study aims to evaluate retrospectively the relationship between age and severity of COVID-19 in both sexes based on chest CT severity scoring system. Results Age group C (40–49 year) was the commonest age group that was affected by COVID-19 by 21.3%, while the least affected group was group F (≥ 70 years) by only 6.4%. As regards COVID-RADS classification, COVID-RADS-3 was the most commonly presented at both sexes in all different age groups. Total CT severity lung score had a positive strong significant correlation with the age of the patient (r = 0.64, P < 0.001). Also, a positive strong significant correlation was observed between CT severity lung score and age in both males and females (r = 0.59, P < 0.001) and (r = 0.69, P < 0.001) respectively. Conclusion We concluded that age can be considered as a significant risk factor for the severity of COVID-19 in both sexes. Also, CT can be used as a significant diagnostic tool for the diagnosis of COVID-19 and evaluation of the progression and severity of the disease.

scholarly journals Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1963
Author(s):  
Daimantas Milonas ◽  
Tomas Ruzgas ◽  
Zilvinas Venclovas ◽  
Mindaugas Jievaltas ◽  
Steven Joniau
Keyword(s):  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Competing Risk ◽  
Grade Group ◽  
Tertiary Center ◽  
Stable Performance ◽  
Cancer Specific Mortality ◽  
Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

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