A stacked generalization method for disease progression prediction

2017 ◽  
Author(s):  
Gamal Elkomy ◽  
ElSayed Sallam ◽  
Sherin Elgokhy
Keyword(s):  
Disease Progression ◽  
Stacked Generalization ◽  
Progression Prediction

scholarly journals A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

2021 ◽  
Vol 12 (10) ◽  
pp. 3675-3680
Author(s):  
K. Karthikayani, Et. al.
Keyword(s):  
Lung Cancer ◽  
Disease Progression ◽  
Lung Cancer Mortality ◽  
Learning System ◽  
Chain Code ◽  
Ct Screening ◽  
Lung Cancer Diagnosis ◽  
System A ◽  
Segmentation Approach ◽  
Progression Prediction

CT screening has been commonly used to identify and diagnose lung cancer in its early stages. CT has been shown in clinical studies to reduce lung cancer mortality by 20% as compared to plain chest radiography; however, existing CT screening services face obstacles such as high over diagnosis rates, high costs, and elevated radiation exposure.The study develops computer and deep learning models for predictive lung cancer diagnosis and disease progression prediction in an effort to solve these difficulties. Using a symmetric chain code method and a machine learning system, a novel lung segmentation approach was first developed. The lung nodules connected to the lung wall are included in this process, which minimises over-segmentation error. Finally, to predict the inter disease progression of lung cancer, a Bayesian method was coupled with a prolonged Markov model.The resultant model calculates specific lung cancer state transition data, which can be used to make customised screening recommendations. Extensive trials and results have shown the efficacy of these approaches, paving the way for current CT screening systems to be optimised and improved.

Reply to: Triple-phase CT scan for disease progression prediction in cirrhosis: A valid repurpose?

2021 ◽  
pp. 110076
Author(s):  
Elton Dajti ◽  
Antonio Colecchia ◽  
Nicolò Brandi ◽  
Rita Golfieri ◽  
Matteo Renzulli
Keyword(s):  
Ct Scan ◽  
Disease Progression ◽  
Progression Prediction

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.]

Abstract 546: Arterial Stiffness And Pulse Pressure Predict Kidney Disease Progression In Chronic Kidney Disease

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Raymond Townsend ◽  
Nisha Bansal ◽  
Julio Chirinos ◽  
Magdalena Cuevas ◽  
Virginia Ford ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Arterial Stiffness ◽  
Disease Progression ◽  
Pulse Pressure ◽  
Cox Regression ◽  
Ckd Progression ◽  
Multivariable Regression ◽  
Kidney Disease Progression ◽  
Progression Prediction

Brachial pulse pressure (bPP) predicts kidney function in CKD. Pulse wave velocity (PWV) may provide additional prediction of CKD progression, and was evaluated longitudinally in CKD patients enrolled in the CRIC cohort. bPP was used because it shows superior CKD progression prediction in humans. There were 2821 participants in CRIC who underwent an assessment of PWV most of whom were studied at their second year follow-up visit. Of these 43 % were women, 48% diabetic, mean age 60 years and mean estimated GFR of 44.6 mL/min/1.73m2. Blacks were 40%, whites 44%, and Hispanics 12%. Mean blood pressure was 127/70 mmHg; mean bPP was 54 mmHg. The interaction of PWV with bPP on the occurrence of ESRD (dialysis or a transplant) [left panel of figure] and a combined endpoint of halving of the eGFR or ESRD [right panel], was analyzed used Cox regression. Events by tertiles of PWV (<7.9 msec; 7.9-10.3 m/sec; >10.3m/sec) and bPP (<46 mmHg, 46-62 mmHg, >62 mmHg) are shown in the figure. There were significant interactions between PWV and bPP for both outcomes. For ESRD the hazard ratio of the highest tertiles of PWV/bPP compared with the lowest was 3.18 (p<0.001). For the combined ESRD or halving of eGFR the HR was 2.65 (p<0.001). In multivariable regression important predictors of CKD progression in this model were age, male gender, non-white race/ethnicity, initial eGFR, and proteinuria.PWV showed a strong and graded interaction with bPP in the prediction of ESRD and the combined outcome of halving of GFR or ESRD. A greater appreciation of the role of arterial stiffness in CKD is an important step towards pursuing the mechanisms underlying how this measure may influence the course of kidney disease progression.

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.

Sign in / Sign up

Export Citation Format

Share Document